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# Keep structured point-cloud assets byte-identical across platforms.
*.pcd -text
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!05_software/real/sim2real/vendored/odin1_imu/build/libodin1_imu_bridge.so !05_software/real/sim2real/vendored/odin1_imu/build/libodin1_imu_bridge.so
!05_software/real/sim2real/vendored/odin1_imu/lib/*.a !05_software/real/sim2real/vendored/odin1_imu/lib/*.a
!05_software/real/sim2real_v2/vendored/odin1_imu/lib/*.a !05_software/real/sim2real_v2/vendored/odin1_imu/lib/*.a
!05_software/real/sim2real_ros2_v2/src/odin_ros_driver/lib/*.a
!05_software/real/sim2real_ros2_v3/src/odin_ros_driver/lib/*.a
# Training outputs # Training outputs
logs/ logs/
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本目录用于保存 16DOF 轮足项目自身的技术文档和使用说明。 本目录用于保存 16DOF 轮足项目自身的技术文档和使用说明。
后续建议按主题组织 当前文档结构
```text ```text
01_doc/ 01_doc/
├─ architecture/ # 系统架构和数据流 ├─ architecture/
├─ control/ # 控制与强化学习原理 │ └─ early_software_stack.md # 第一代训练—仿真—真机闭环
├─ deployment/ # Sim2Sim 和 Sim2Real 部署 ├─ training_evolution.md # v0.4v0.6 训练架构演进
hardware/ # 接线、标定和硬件兼容性 version_history.md # 全项目 Tag 与里程碑
└─ user_guide/ # 安装、运行和调试说明
``` ```
具体运行说明放在对应工程目录内,避免在顶层重复并逐渐失真:训练见 `05_software/train/rc_mjlab/`,真机部署见 `05_software/real/`
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比赛得分:1050 比赛得分:1050
``` ```
`model_6800.onnx` 是比赛最终部署工件,不用模型编号替代训练代码版本号。它将在最终比赛部署版本中与运行配置一起归档 `model_6800.onnx` 是比赛最终部署工件,不用模型编号替代训练代码版本号。它已随比赛部署归档;当前规范目录为 `05_software/real/sim2real_ros2_v3/policies/`
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# 版本演进 # 版本演进
本项目使用同一条 `16dof` 主线和里程碑 Tag 保存线性演进,不在源码目录中复制历史版本 本项目使用里程碑 Tag 保存时间演进;同一架构的小步迭代不复制目录。ROS 2 的无后缀、`_v2``_v3` 目录分别代表三个架构大版本,并在 `v1.1.0` 中同时保留
| Tag | 阶段 | 核心内容 | | Tag | 阶段 | 核心内容 |
| --- | --- | --- | | --- | --- | --- |
@@ -16,8 +16,15 @@
| `v0.8.1` | 导航打点工具 | 地图/航点编辑、路线迭代和抽样 PCD 补充包 | | `v0.8.1` | 导航打点工具 | 地图/航点编辑、路线迭代和抽样 PCD 补充包 |
| `v0.9.0` | Python Sim2Real v2 | 反馈新鲜度、Odin odom 诊断、Web 调试和安全监控增强 | | `v0.9.0` | Python Sim2Real v2 | 反馈新鲜度、Odin odom 诊断、Web 调试和安全监控增强 |
| `v0.10.0` | ROS 2/C++ 初版 | 50 Hz C++ 推理、200 Hz CAN 热路径和 ROS 2 系统集成 | | `v0.10.0` | ROS 2/C++ 初版 | 50 Hz C++ 推理、200 Hz CAN 热路径和 ROS 2 系统集成 |
| `v0.11.0` | ROS 2 导航原型 | 简单导航、PCD 交互定位、任务点和 Web 导航调试 |
| `v0.11.1` | Odin 与站姿调参 | 完整 Odin 驱动、TensorRT、多策略切换和调参站姿 |
| `v0.12.0` | 里程计导航联调 | 纯里程计 fallback、A_min 路线、TF 冲突保护和 model_9600 |
| `v1.0.0` | 比赛最终部署初次归档 | last_not_slalom_1050、model_6800/model_84、最终路线和触控屏;当时暂存于无后缀目录 |
| `v1.0.1` | 比赛成果媒体补充 | 最终机器人图片与 1050 分比赛视频 |
| `v1.0.2` | 文档一致性修正 | 统一历史 Tag、当前快照和成果媒体的描述 |
| `v1.1.0` | 三代目录规范化 | 恢复无后缀初版、保留 v2 里程计版、明确最终比赛 v3,并校准训练 README |
> 原先临时归档为 `v0.9.0` 的最终 ROS 2/C++ 比赛部署已保存在 `backup/final-ros2-v0.9.0` 分支和 `backup-v0.9.0-ros2-final` 标签中,重排完成后将正式归入 `v1.0.0`。 > 原先临时归档为 `v0.9.0` 的最终 ROS 2/C++ 比赛部署已保存在 `backup/final-ros2-v0.9.0` 分支和 `backup-v0.9.0-ros2-final` 标签中,重排后已正式归入 `v1.0.0`。
## `v0.9.0` 的 Python Sim2Real v2 ## `v0.9.0` 的 Python Sim2Real v2
@@ -34,6 +41,57 @@
- 原始快照中的 `src/odin_ros_driver` 为空目录,因此本版本仍需外部 Odin 驱动,不能宣称传感器依赖已自包含。 - 原始快照中的 `src/odin_ros_driver` 为空目录,因此本版本仍需外部 Odin 驱动,不能宣称传感器依赖已自包含。
- 保留原始候选 ONNX 文件以记录初版部署试验;排除计划、任务和 walkthrough 草稿。 - 保留原始候选 ONNX 文件以记录初版部署试验;排除计划、任务和 walkthrough 草稿。
## `v0.11.0` 的 ROS 2 Sim2Real v2
- 归档 `real/sim2real_ros2_v2`,保持 `v0.10.0` 的 ROS 2/C++ 控制契约。
- 增加 `simple_nav_node.py`、PCD 点击工具、任务点/任务序列配置和 Web 导航控制入口。
- 默认命令源从遥控切换为 `NAV`,加入简单导航状态、PCD 位姿和地图显示链路。
- 原始 `map1.pcd``map6.pcd` 分别约 49.05 MiB、44.24 MiB,归档时确定性抽样到 10 MB 以下并记录哈希。
- 原始快照中的 Odin 驱动仍为空目录;排除设备运行日志和开发草稿。
## `v0.11.1` 的 Odin、TensorRT 与站姿调参
- 归档 `real/sim2real_ros2_v2(z=0.380 hip=0.670 knee=-1.390)`,在同一 `sim2real_ros2_v2` 目录中记录真实差异。
- 首次随部署工程保留完整 Odin ROS 驱动、Apache-2.0 许可证、设备标定参数和预编译 SDK 静态库。
- 策略运行时增加 TensorRT、ONNX 回退、Rough/Crawl 模式切换、事件日志和更完整的电机失效诊断。
- 默认 Rough 策略为 `NEWmodel_1900`,默认站姿为髋俯仰 `0.670`、膝关节 `-1.390`Crawl 配置使用 IK 后端。
- `map_b.pcd` 从 1,080,047 点确定性抽样为 270,012 点,并保留原始和抽样哈希。
- 排除嵌套 Git、Odin 运行日志、缓存、开发草稿和未被配置引用的候选策略。
## `v0.12.0` 的里程计导航联调
- 归档 `real/sim2real_ros2_v2(odom)`,继续沿用 `sim2real_ros2_v2` 目录的线性演进。
- 将 Rough 策略切换为 `model_9600`,默认站姿恢复为髋俯仰 `0.550`、膝关节 `-1.125`
- Odin `custom_map_mode` 固定为纯里程计,加入 odom 新鲜度、外部 map/odom TF 冲突和任务结束交接保护。
- 增加 A_min 路线、PCD 地图编辑工具和三份抽样点云;原始大 PCD 不直接进入 Git。
- 保留完整 Odin 驱动、标定参数和 SDK 静态库;未找到的 `map_a.bin` 仍不伪造,重定位闭环不在本 Tag 声称已复现。
## `v1.0.0` 的比赛最终部署
- 首次归档原始 `sim2real_ros2_v2(last_not_slalom_1050)` 最终 ROS 2/C++ 真机工程;该 Tag 中暂存于无后缀 `real/sim2real_ros2`,目录命名在 `v1.1.0` 才修正为 `real/sim2real_ros2_v3``1050` 是比赛成绩,不是模型编号。
- Rough 使用 `model_6800`Wall 使用 `model_84`,Crawl 按比赛配置使用 IK 后端。
- 保留最终五份路线、1 号场地抽样 PCD、Odin 驱动、CAN 硬件桥、命令仲裁、导航和 Orin 触控屏 UI。
- 最终配置默认命令源为 `NAV`、定位模式为 `relocal`,但真实 Odin `1hao.bin` 不在备份中,重定位闭环需要从比赛设备补回。
- 排除嵌套 Git、日志、备份、候选策略、构建产物和开发草稿;TensorRT engine 仅代表比赛机环境。
## `v1.0.1` 的比赛成果媒体补充
- 保持 `v1.0.0` 的比赛最终代码和部署内容不变。
- 补充最终机器人图片和比赛视频,成绩为 1050 分、第七名(前 5%)。
- 代码复现可查看 `v1.0.0`,包含成果媒体的对应快照可查看 `v1.0.1`
## `v1.0.2` 的文档一致性修正
- 统一 ROS 2 历史 Tag、当前工作树和媒体补丁的说明。
- 该版本仅修正文档,没有改变训练或真机运行代码。
## `v1.1.0` 的目录与说明规范化
-`v0.10.0` 恢复无后缀 `sim2real_ros2` 初版快照。
- `sim2real_ros2_v2` 保持 `v0.12.0` 里程计联调快照。
-`last_not_slalom_1050` 最终比赛部署正式命名为 `sim2real_ros2_v3`
- 依据当前源码重新校准 `rc_mjlab` README 中的物理步长、控制频率、环境数、执行器、地形、奖励和随机化说明。
## `v0.4.0` 的模型变化 ## `v0.4.0` 的模型变化
- 机械 CAD 不变。 - 机械 CAD 不变。
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本目录用于保存 16DOF 轮足机器人的自研电路、接线图、BOM、传感器与计算平台说明。 本目录用于保存 16DOF 轮足机器人的自研电路、接线图、BOM、传感器与计算平台说明。
第三方硬件资料不作为自研成果提交。 第三方硬件资料不作为自研成果提交。
当前目录仅有范围说明,尚未归档 16DOF 平台的自研原理图、PCB、BOM 或正式接线图,不应将本目录视为已完成的硬件开源包。
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本目录用于保存运行在 MCU 或其他嵌入式控制器上的固件源码和构建工程。 本目录用于保存运行在 MCU 或其他嵌入式控制器上的固件源码和构建工程。
编译生成的 `.hex``.bin``.elf``.axf` 等文件不进入源码目录,可在需要时作为 Release 附件发布。 编译生成的 `.hex``.bin``.elf``.axf` 等文件不进入源码目录,可在需要时作为 Release 附件发布。
当前 16DOF 主线尚未在本目录归档 MCU/Keil 工程;该目录是预留入口。8DOF 大疆 A 板 Keil 工程由 `8dof` 分支和 `v0.1.0` 保存。
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├─ ik_real/ # IK 轨迹与早期真机控制 ├─ ik_real/ # IK 轨迹与早期真机控制
├─ sim2real/ # 第一代 Python 策略真机部署 ├─ sim2real/ # 第一代 Python 策略真机部署
├─ sim2real_v2/ # Python Sim2Real v2 ├─ sim2real_v2/ # Python Sim2Real v2
─ sim2real_ros2/ # ROS 2/C++ Sim2Real 初版 ─ sim2real_ros2/ # ROS 2/C++ Sim2Real 初版
├─ sim2real_ros2_v2/ # ROS 2 导航原型及里程计演进
└─ sim2real_ros2_v3/ # 最终比赛 ROS 2/C++ 部署
``` ```
当前工作树按架构大版本同时保留三个 ROS 2 目录:无后缀目录是初版,`_v2` 是第二版演进的最终里程计快照,`_v3``last_not_slalom_1050` 最终比赛部署。各目录内部的小阶段仍可通过对应 Tag 恢复。
## 数据流 ## 数据流
```text ```text
@@ -32,7 +36,7 @@ MJCF + mjlab task
IK real --------------------------------> 电机 IK real --------------------------------> 电机
``` ```
`rc_mjlab` 是自包含工程。训练、MJCF、MuJoCo、Sim2Sim、导航工具和策略权重通过相对路径绑定,因此保留其内部布局,没有为了目录外观拆散。第一代完整闭环见 `v0.3.0`,第一份新版 MJCF 与训练框架见 `v0.4.0`,随机化增强版见 `v0.5.0`,比赛最终训练架构见 `v0.6.0`,后期 MuJoCo 工具集见 `v0.7.0`,后期 Sim2Sim 与比赛 Rough 策略见 `v0.8.0`,完整导航打点工具见 `v0.8.1`Python Sim2Real v2 对应 `v0.9.0`ROS 2/C++ Sim2Real 初版对应 `v0.10.0` `rc_mjlab` 是自包含工程。训练、MJCF、MuJoCo、Sim2Sim、导航工具和策略权重通过相对路径绑定,因此保留其内部布局,没有为了目录外观拆散。第一代完整闭环见 `v0.3.0`,第一份新版 MJCF 与训练框架见 `v0.4.0`,随机化增强版见 `v0.5.0`,比赛最终训练架构见 `v0.6.0`,后期 MuJoCo 工具集见 `v0.7.0`,后期 Sim2Sim 与比赛 Rough 策略见 `v0.8.0`,完整导航打点工具见 `v0.8.1`Python Sim2Real v2 对应 `v0.9.0`ROS 2/C++ 初版对应 `v0.10.0`,简单导航原型对应 `v0.11.0`,完整 Odin/TensorRT 与站姿调参对应 `v0.11.1`,纯里程计导航联调对应 `v0.12.0`1050 分比赛最终部署对应 `v1.0.0`
详细说明见: 详细说明见:
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## `sim2real_v2` ## `sim2real_v2`
Python Sim2Real v2,保留 `53D -> 16D` 策略接口,并增加电机反馈新鲜度、Odin odom 诊断、命令平滑、Web 运行时诊断和安全监控工具。该版本对应重排主线的 `v0.9.0` Python Sim2Real v2,保留 `53D -> 16D` 策略接口,并增加电机反馈新鲜度、Odin odom 诊断、命令平滑、Web 运行时诊断和安全监控工具,对应 `v0.9.0`
部署说明见 [`sim2real_v2/README.md`](sim2real_v2/README.md) 与 [`sim2real_v2/DEPLOYMENT.md`](sim2real_v2/DEPLOYMENT.md)。 部署说明见 [`sim2real_v2/README.md`](sim2real_v2/README.md) 与 [`sim2real_v2/DEPLOYMENT.md`](sim2real_v2/DEPLOYMENT.md)。
## `sim2real_ros2` ## ROS 2/C++ 版本线
ROS 2/C++ Sim2Real 初版,将策略热路径迁移为 50 Hz C++ 推理和 200 Hz CAN 电机循环,并加入 ROS 2 消息、命令仲裁、Nav2 与统一启动结构。该版本对应重排主线的 `v0.10.0` ### `sim2real_ros2`(初版,`v0.10.0`
原始快照没有随工程保存 Odin ROS 2 驱动源码,依赖边界见 [`sim2real_ros2/README.md`](sim2real_ros2/README.md)。 无后缀目录固定表示 ROS 2/C++ Sim2Real 初版:将策略热路径迁移为 50 Hz C++ 推理和 200 Hz CAN 电机循环,并加入 ROS 2 消息、命令仲裁、Nav2 与统一启动结构。原始快照随工程保存 Odin ROS 2 驱动源码,依赖边界见 [`sim2real_ros2/README.md`](sim2real_ros2/README.md)。
### `sim2real_ros2_v2``v0.11.0``v0.12.0`
`v0.11.0` 中,该目录是 ROS 2 Sim2Real v2 导航原型,增加简单导航节点、PCD 交互定位、任务点/任务序列和 Web 导航调试。
`v0.11.1` 在同一路径继续演进,首次归档完整 Odin 驱动、TensorRT、多策略切换和硬件诊断,并使用 `hip=0.670``knee=-1.390` 的调参站姿。
`v0.12.0` 仍在同一路径上形成里程计导航联调快照:固定纯里程计模式,加入 odom fallback 的 TF 冲突保护、A_min 路线和多地图工具;默认 Rough 策略为 `model_9600`,默认站姿回到比赛站姿。当前该目录保持 `v0.12.0` 快照,阶段说明见 [`sim2real_ros2_v2/README.md`](sim2real_ros2_v2/README.md)。
### `sim2real_ros2_v3`(最终比赛版;代码快照 `v1.0.0`,规范目录 `v1.1.0`
第三版来自原始目录 `sim2real_ros2_v2(last_not_slalom_1050)`,整理时正式命名为 `sim2real_ros2_v3`。它是 1050 分比赛最终部署,包含 `model_6800` Rough、`model_84` Wall、最终路线、完整 Odin 驱动、CAN 和触控屏。部署说明见 [`sim2real_ros2_v3/README.md`](sim2real_ros2_v3/README.md)。
## 实机记录 ## 实机记录
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# `sim2real` 部署说明 # `sim2real` 部署说明
> 版本范围:第一代 Python Sim2Real`v0.3.0`)。本文“当前”均指该快照。
## 模型 ## 模型
当前只使用: 当前只使用:
- `sim2real/policies/model_rough.pt` - `policies/model_rough.pt`
## 模型契约 ## 模型契约
@@ -38,7 +40,7 @@
## 纯 Python 命令 ## 纯 Python 命令
默认前提:当前目录`sim2real/` 默认前提:当前目录是 `05_software/real/sim2real/`
```bash ```bash
python -m pip install -r requirements-orin.txt python -m pip install -r requirements-orin.txt
@@ -1,9 +1,11 @@
# `FACTS_AND_ASSUMPTIONS` # `FACTS_AND_ASSUMPTIONS`
> 版本范围:第一代 Python Sim2Real`v0.3.0`)。事实项只适用于该快照。
## 已确认 ## 已确认
- 当前部署模型:`sim2real/policies/model_rough.pt` - 当前部署模型:`policies/model_rough.pt`
- 源模型:`model_2000.pt` - 原始说明记录的源模型`model_2000.pt`;同名文件未随本目录归档
- actor 输入:`53D` - actor 输入:`53D`
- actor 输出:`16D` - actor 输出:`16D`
- 当前 actor 不吃 `base_lin_vel` - 当前 actor 不吃 `base_lin_vel`
@@ -1,5 +1,7 @@
# `Orin Nano` 部署说明 # `Orin Nano` 部署说明
> 版本范围:第一代 Python Sim2Real`v0.3.0`)。本文不是最终 ROS 2 v3 部署指南。
## 是否必须转 ONNX ## 是否必须转 ONNX
不必须。 不必须。
@@ -26,7 +28,7 @@
## 纯 Python 部署命令 ## 纯 Python 部署命令
默认前提:当前目录`sim2real/` 默认前提:当前目录是 `05_software/real/sim2real/`
```bash ```bash
python3 -m pip install -r requirements-orin.txt python3 -m pip install -r requirements-orin.txt
+12 -11
View File
@@ -1,11 +1,13 @@
# `sim2real` # `sim2real`
当前版本只部署现在这套 `53D -> 16D` 模型,不再兼容旧版 `crawl`、多策略和历史观测 本目录是第一代 Python Sim2Real 快照,对应 `v0.3.0`。下文“当前”均指该历史快照,不指仓库 `main` 的最终 ROS 2 v3
该快照只部署当时的 `53D -> 16D` Rough 模型,不兼容更早的 Crawl、多策略和其他历史观测契约。
## 当前部署模型 ## 当前部署模型
- 使用文件:`sim2real/policies/model_rough.pt` - 使用文件:`policies/model_rough.pt`
- 来源文件`model_2000.pt` - 原始说明记录的来源名`model_2000.pt`;该同名源文件未随本目录归档,仓库只保留重命名后的 `policies/model_rough.pt`
## 当前 actor 输入 ## 当前 actor 输入
@@ -52,7 +54,7 @@
## 启动命令 ## 启动命令
默认前提:当前目录`sim2real/` 默认前提:当前目录是 `05_software/real/sim2real/`
`python` `python`
@@ -65,13 +67,12 @@ python main.py
python web/server.py --host 0.0.0.0 --port 8080 python web/server.py --host 0.0.0.0 --port 8080
``` ```
Windows 本机: Windows 本机可使用目标虚拟环境中的 Python;不要依赖个人机器的绝对安装路径
```bash ```bash
D:\Minicoda3\envs\py10\python.exe -m pip install -r requirements-orin.txt python -m pip install -r requirements-orin.txt
D:\Minicoda3\envs\py10\python.exe tools\alignment_check.py --policy policies\model_rough.pt --manifest deployment_manifest.yaml python tools\alignment_check.py --policy policies\model_rough.pt --manifest deployment_manifest.yaml
D:\Minicoda3\envs\py10\python.exe tools\standalone_check.py python tools\standalone_check.py
D:\Minicoda3\envs\py10\python.exe main.py python main.py
D:\Minicoda3\envs\py10\python.exe web\server.py --host 0.0.0.0 --port 8080 python web\server.py --host 0.0.0.0 --port 8080
``` ```
#sim2real/policies/model_rough.pt
@@ -1,6 +1,6 @@
# ROS2 C++ Sim2Real 运动控制栈 - 部署指南 # ROS 2/C++ Sim2Real 初版部署指南
本工作区提供了一个自包含、独立的 C++ ROS2 Humble 实现,用于在 Jetson Orin 目标机上部署轮腿四足机器人控制策略 本工作区保存 `v0.10.0` ROS 2 Humble/C++ 初版。控制栈主体源码已归档,但 Odin 驱动源码在该快照中缺失,因此不是自包含部署包;完成真实传感器闭环前必须补充兼容驱动
--- ---
+7 -7
View File
@@ -1,6 +1,6 @@
# ROS 2/C++ Sim2Real 初版 # ROS 2/C++ Sim2Real 初版
本目录归档 `real/sim2real_ros2`,对应重排主线的 `v0.10.0`。这是轮腿机器人 Sim2Real 部署栈从 Python 运行时迁移到 ROS 2 + C++ 的第一版系统工程。 本目录归档 `real/sim2real_ros2`,对应 `v0.10.0`。这是轮腿机器人 Sim2Real 部署栈从 Python 运行时迁移到 ROS 2 + C++ 的第一版系统工程。
本工程保留当前 `sim2real` 已验证的部署契约,同时将运行时热路径迁移到 C++: 本工程保留当前 `sim2real` 已验证的部署契约,同时将运行时热路径迁移到 C++:
@@ -26,7 +26,7 @@
- `src/sim2real_bringup` - `src/sim2real_bringup`
统一启动文件与运行时参数配置。 统一启动文件与运行时参数配置。
- `src/odin_ros_driver` - `src/odin_ros_driver`
Odin 传感器 ROS 2 驱动(含 IMU、点云里程计发布 仅保留依赖边界说明;原始 `v0.10.0` 快照没有归档 Odin 驱动源码,不能独立提供 IMU、点云里程计发布。
- `docs` - `docs`
架构说明与迁移计划。 架构说明与迁移计划。
@@ -58,11 +58,11 @@ Nav2 / cmd_vel ------------------------------> sim2real_runtime
迁移过程中以下文件被视为真值源: 迁移过程中以下文件被视为真值源:
- `sim2real/deployment_manifest.yaml` - `../sim2real/deployment_manifest.yaml`
- `sim2real/interface/motor_mapping.py` - `../sim2real/interface/motor_mapping.py`
- `sim2real/interface/real_io.py` - `../sim2real/interface/real_io.py`
- `sim2real/policy/policy_runner.py` - `../sim2real/policy/policy_runner.py`
- `sim2real/web/session.py` - `../sim2real/web/session.py`
## 注意事项 ## 注意事项
@@ -1,5 +1,7 @@
# 迁移计划 # 迁移计划
> 版本范围:ROS 2/C++ 初版(`v0.10.0`)。这是历史迁移记录,不是最终比赛版验收报告;本快照未归档 Odin 驱动源码。
## Phase 1: 硬件核心迁移 ✅ 已完成 ## Phase 1: 硬件核心迁移 ✅ 已完成
将当前高频热路径从 Python 迁出。 将当前高频热路径从 Python 迁出。
@@ -39,11 +41,7 @@
## Phase 3: ROS 2 系统集成 ✅ 已完成 ## Phase 3: ROS 2 系统集成 ✅ 已完成
参考的源项目: 参考过的外部项目包括 Odin ROS 驱动、EDULITE A3 ROS 工程和 RL-SAR;这些外部参考目录不属于本仓库公开内容。
- `00_ reference/odin_ros_driver`
- `00_ reference/EDULITE_A3/el_a3_ros`
- `00_ reference/rl_sar`
交付物: 交付物:
@@ -76,5 +74,5 @@
| 策略运行时 | `sim2real_runtime_node` | 50Hz ONNX 推理 + 53D 观测 + raw_action clip | | 策略运行时 | `sim2real_runtime_node` | 50Hz ONNX 推理 + 53D 观测 + raw_action clip |
| 里程计中继 | `odom_relay_node` | /odin1/odometry → /odom + odom→base_link TF | | 里程计中继 | `odom_relay_node` | /odin1/odometry → /odom + odom→base_link TF |
| 导航栈 | Nav2 全套节点 | AMCL + costmap + DWB + Navfn + BT + lifecycle | | 导航栈 | Nav2 全套节点 | AMCL + costmap + DWB + Navfn + BT + lifecycle |
| 传感器驱动 | `odin_ros_driver` | IMU + 点云 + 里程计原始发布 | | 传感器驱动 | 外部 Odin 驱动 | 本快照未归档源码,不能视为自包含组件 |
| 点云转换 | `pointcloud_to_laserscan` | /odin1/cloud_slam → /scan (供 AMCL 使用) | | 点云转换 | `pointcloud_to_laserscan` | 外部 ROS 包;/odin1/cloud_slam → /scan供 AMCL 使用 |
@@ -0,0 +1,6 @@
build/
install/
log/
.colcon/
.vscode/
compile_commands.json
@@ -0,0 +1,178 @@
# ROS 2/C++ Sim2Real v2 部署指南
本工作区保存 `v0.12.0` 里程计联调快照,包含 ROS 2 Humble/C++ 主体和 Odin 驱动。系统依赖、设备标定和 TensorRT engine 仍与目标机环境绑定,不能仅凭源码声明在新设备上已复现。
---
## 1. 前提条件与环境
### 硬件
* **目标计算机**:运行 Ubuntu 22.04 LTS 的 Jetson Orin Nano / Orin NX / AGX Orin。
* **IMU 传感器**Odin 集成 IMU,发布至 `/odin1/imu`
* **CAN 总线适配器**Peak CAN、USB-to-CAN 或板载 SocketCAN 接口,使用 CAN0 和 CAN1。
### 主机依赖
* **操作系统**Ubuntu 22.04 LTS (Jammy Jellyfish)。
* **ROS 2 发行版**ROS 2 HumbleDesktop-Base 或 ROS-Base)。
* **C++ 编译器**:支持 C++17 的 GCC/G++ 9.0+。
* **库与 ROS2 包**
* `libyaml-cpp-dev`
* `libeigen3-dev`
* `libusb-1.0-0-dev`Odin USB 传感器通信)
* `libpcl-dev``libopencv-dev`3D 点云与相机处理)
* `ros-humble-navigation2``ros-humble-nav2-bringup`Nav2 规划器/控制器服务器)
* `ros-humble-pointcloud-to-laserscan`(点云转激光扫描,供 AMCL 使用)
* `ros-humble-cv-bridge``ros-humble-pcl-conversions`(Odin 传感器驱动图像与点云处理)
* `can-utils`SocketCAN 验证工具)
---
## 2. 本地编译与部署
按以下步骤在主机系统上编译运行整个栈:
### 步骤 1:安装系统依赖
```bash
sudo apt-get update
sudo apt-get install -y build-essential cmake can-utils libyaml-cpp-dev libeigen3-dev \
libusb-1.0-0-dev libpcl-dev libopencv-dev ros-humble-navigation2 \
ros-humble-nav2-bringup ros-humble-pointcloud-to-laserscan \
ros-humble-cv-bridge ros-humble-pcl-conversions
```
### 步骤 2:下载 ONNXRuntime C++ SDK
策略需要 ONNXRuntime 库来运行推理。必须下载并解压到已知目录:
```bash
# 创建目录
sudo mkdir -p /opt/onnxruntime
cd /opt
# 针对 Jetson Orin (ARM64 / aarch64)
sudo wget https://github.com/microsoft/onnxruntime/releases/download/v1.16.3/onnxruntime-linux-aarch64-1.16.3.tgz
sudo tar -zxvf onnxruntime-linux-aarch64-1.16.3.tgz --strip-components=1 -C /opt/onnxruntime
# 或标准桌面仿真 (x86_64 / amd64)
# sudo wget https://github.com/microsoft/onnxruntime/releases/download/v1.16.3/onnxruntime-linux-x64-1.16.3.tgz
# sudo tar -zxvf onnxruntime-linux-x64-1.16.3.tgz --strip-components=1 -C /opt/onnxruntime
```
导出 CMake 辅助变量:
```bash
export ONNXRUNTIME_DIR=/opt/onnxruntime
```
### 步骤 3:构建工作区
进入包含 `src/` 的本包根目录,运行 `colcon`
```bash
colcon build --merge-install --cmake-args -DCMAKE_BUILD_TYPE=Release
```
### 步骤 4:配置 SocketCAN 接口
启动前,以 1 Mbps 波特率激活 CAN 接口:
```bash
sudo ip link set can0 up type can bitrate 1000000
sudo ip link set can1 up type can bitrate 1000000
```
使用 `ifconfig``ip link` 验证接口已启动。
### 步骤 5:启动节点
使启动脚本可执行并运行:
```bash
chmod +x start_sim2real.sh
./start_sim2real.sh
```
---
## 3. Docker 部署(推荐)
强烈推荐使用 Docker 隔离依赖,避免 Jetson Orin 上的库版本冲突。
### 步骤 1:构建镜像
确保在 `sim2real_ros2_v2` 目录中(包含 `Dockerfile`):
```bash
# 使用标准 docker build
docker build -t sim2real_ros2:latest .
# 或使用 Docker Compose
docker compose build
```
### 步骤 2:运行容器
对于真实硬件部署,容器**必须**共享主机网络栈(用于 ROS2 DDS 和 SocketCAN)并具备线程优先级能力以实现实时调度:
```bash
# 选项 A:手动运行
docker run -it \
--network host \
--privileged \
--cap-add=sys_nice \
--volume=/dev:/dev \
--shm-size=2g \
--name sim2real_ros2_run \
sim2real_ros2:latest
# 选项 B:通过 Docker Compose 运行(最简单)
docker compose up -d
```
---
## 4. 系统拓扑与话题
控制节点通过标准 ROS 2 DDS 消息与传感器驱动和导航栈交互:
* **IMU 输入**:订阅 `/odin1/imu``sensor_msgs/msg/Imu`)。硬件节点自动执行逆轴旋转(`x_raw = -y_ros``y_raw = x_ros`)以重建 RL 策略期望的原始坐标系。
* **控制命令**:订阅 `/cmd_vel``/cmd_vel_stamped``geometry_msgs/msg/Twist` / `TwistStamped`),由导航栈或手动键盘节点发布。
* **里程计输入**:订阅 `/odom``nav_msgs/msg/Odometry`),由 `odom_relay_node``/odin1/odometry` 中继并重映射帧名后提供。
* **急停**:订阅 `/safety/estop``std_msgs/msg/Bool`)。发布 `true` 触发软件急停,机器人进入低刚度阻尼刹车。
* **状态遥测**:发布 `runtime/state``sim2real_interfaces/msg/RuntimeState`),包含当前关节速度、温度、IMU 输出和诊断信息。
* **策略目标**:发布 `runtime/target``sim2real_interfaces/msg/RuntimeTarget`),包含策略推理输出的目标关节位置。
### TF 树
```
odom ──→ base_link (由 odom_relay_node 广播)
map ──→ odom (由 AMCL / Odin SLAM 发布,取决于运行模式)
```
---
## 5. 集成 ROS 2 导航与传感器驱动
### USB 设备权限(Odin 传感器)
要运行物理 Odin 传感器驱动(`odin_ros_driver`),目标计算机必须具有传感器 USB 接口的读写权限。在主机系统上添加以下 udev 规则:
```bash
# 1. 添加 udev 规则
echo 'SUBSYSTEM=="usb", ATTR{idVendor}=="2207", ATTR{idProduct}=="0019", MODE="0666", GROUP="plugdev"' | sudo tee /etc/udev/rules.d/99-odin-usb.rules
# 2. 重新加载 udev 规则并重新插拔传感器
sudo udevadm control --reload
sudo udevadm trigger
```
### 集成启动参数
统一启动文件 `sim2real_system.launch.py` 支持模块化激活传感器驱动和 Nav2 导航栈:
* `launch_driver`(默认:`true`):启动 `odin_ros_driver` 节点以获取 IMU 和点云遥测。
* `launch_nav2`(默认:`true`):启动 ROS2 Navigation2 规划器、控制器、costmap、AMCL 和 pointcloud_to_laserscan。
#### 1. 完整真实硬件闭环(默认)
启动运动控制运行时、物理 CAN 桥接、Odin 传感器驱动和 Nav2 导航:
```bash
ros2 launch sim2real_bringup sim2real_system.launch.py dry_run:=false launch_driver:=true launch_nav2:=true
```
#### 2. Dry-Run / 仿真航点测试
在 dry-run 模式下运行策略运行时和 Nav2 导航(不访问 CAN 总线或物理 USB 传感器,适合测试导航话题路由):
```bash
ros2 launch sim2real_bringup sim2real_system.launch.py dry_run:=true launch_driver:=false launch_nav2:=true
```
#### 3. 仅运动控制(无导航)
禁用传感器驱动和 Nav2,让运动策略等待 `/cmd_vel` 上的手动速度输入(如键盘遥操作):
```bash
ros2 launch sim2real_bringup sim2real_system.launch.py launch_driver:=false launch_nav2:=false
```
@@ -0,0 +1,80 @@
# 使用 ROS2 官方 Humble 基础镜像
FROM ros:humble-ros-base-jammy
ENV DEBIAN_FRONTEND=noninteractive
# 安装 C++ 编译依赖、SocketCAN 调试工具及 Eigen 等核心库
RUN apt-get update && apt-get install -y --no-install-recommends \
build-essential \
cmake \
git \
can-utils \
libyaml-cpp-dev \
libeigen3-dev \
libusb-1.0-0-dev \
libpcl-dev \
libopencv-dev \
ros-humble-navigation2 \
ros-humble-nav2-bringup \
ros-humble-pointcloud-to-laserscan \
ros-humble-cv-bridge \
ros-humble-pcl-conversions \
wget \
tar \
python3-pip \
&& rm -rf /var/lib/apt/lists/*
# ============================================================================
# ONNX Runtime — 架构自适应,aarch64 启用 CUDA GPU 加速
# ============================================================================
# - Orin Nano (aarch64): pip 安装 onnxruntime-gpu(含 CUDA EP
# - x86_64 开发机: 下载 CPU-only 预编译包(GPU 不可用)
WORKDIR /opt
RUN ARCH=$(uname -m) && \
if [ "$ARCH" = "aarch64" ]; then \
echo "[ONNX] Installing CUDA-enabled ONNX Runtime for Jetson Orin..." && \
pip3 install --no-cache-dir onnxruntime-gpu && \
SITE_PKGS=$(python3 -c "import site; print(site.getsitepackages()[0])") && \
mkdir -p onnxruntime/include onnxruntime/lib && \
cp -r "$SITE_PKGS/onnxruntime/include/"* onnxruntime/include/ && \
cp "$SITE_PKGS/onnxruntime/capi/libonnxruntime.so"* onnxruntime/lib/ && \
echo "[ONNX] CUDA ONNX Runtime installed."; \
else \
echo "[ONNX] Installing CPU-only ONNX Runtime for x86_64 dev..." && \
wget -q https://github.com/microsoft/onnxruntime/releases/download/v1.16.3/onnxruntime-linux-x64-1.16.3.tgz && \
tar -zxf onnxruntime-linux-x64-1.16.3.tgz && \
mv onnxruntime-linux-x64-1.16.3 onnxruntime && \
rm onnxruntime-linux-x64-1.16.3.tgz; \
fi
ENV ONNXRUNTIME_DIR=/opt/onnxruntime
# 创建工作空间,将所有 C++ 源码包拷入
WORKDIR /sim2real_ws/src
COPY src/sim2real_bringup sim2real_bringup
COPY src/sim2real_common sim2real_common
COPY src/sim2real_hw sim2real_hw
COPY src/sim2real_interfaces sim2real_interfaces
COPY src/sim2real_runtime sim2real_runtime
COPY src/odin_ros_driver odin_ros_driver
COPY src/sim2real_nav2 sim2real_nav2
# 拷贝策略文件与运行脚本
WORKDIR /sim2real_ws
COPY policies policies
COPY map map
COPY tools tools
COPY start_sim2real.sh start_sim2real.sh
RUN chmod +x start_sim2real.sh
# 编译 ROS2 工作空间
SHELL ["/bin/bash", "-c"]
RUN source /opt/ros/humble/setup.bash && \
colcon build --merge-install --cmake-args -DCMAKE_BUILD_TYPE=Release
# 拷贝 Docker 入口脚本并设置
COPY docker_entrypoint.sh /docker_entrypoint.sh
RUN chmod +x /docker_entrypoint.sh
ENTRYPOINT ["/docker_entrypoint.sh"]
CMD ["./start_sim2real.sh"]
@@ -0,0 +1,76 @@
# ROS 2 Sim2Real v2:里程计导航联调
本目录归档原始 `real/sim2real_ros2_v2(odom)`,对应 `v0.12.0`。该阶段在 `v0.11.1` 的 Odin/TensorRT 与站姿调参基础上,固定纯里程计模式,增加 odom fallback 的 TF 冲突保护、A_min 路线和地图工具。
本工程保留当前 `sim2real` 已验证的部署契约,同时将运行时热路径迁移到 C++:
- `53D` 策略观测契约不变
- `16D` 动作契约不变
- `50Hz` 策略循环与训练对齐
- `200Hz` 电机循环为专用 C++ 热路径
- ROS 2 作为导航、TF、诊断和启动管理的系统集成层
## 工作区布局
- `src/sim2real_interfaces`
硬件桥接与策略运行时共享的 ROS 2 消息定义。
- `src/sim2real_common`
共享常量、部署契约辅助函数、Mahony 姿态滤波器、站立平衡控制器、安全监控。
- `src/sim2real_hw`
面向硬件的桥接节点:RobStride CAN 收发、IMU/Odin 数据采集、看门狗、状态发布。
- `src/sim2real_runtime`
策略运行时节点:`53D→16D` ONNX 推理、命令滤波/仲裁、目标发布。
同时包含 `odom_relay_node`(里程计中继与 TF 广播)。
- `src/sim2real_nav2`
ROS 2 Navigation2 (Nav2) 配置包:参数、启动文件、AMCL、costmap、planner/controller。
- `src/sim2real_bringup`
统一启动文件与运行时参数配置。
- `src/odin_ros_driver`
Odin 传感器 ROS 2 驱动(含 IMU、点云、里程计发布)。
- `docs`
架构说明与迁移计划。
## 目标架构
```text
Odin / IMU / Odom ---> sim2real_hw ---> sim2real_runtime ---> sim2real_hw
| | |
v v v
RuntimeState RuntimeTarget 电机 CAN 指令
| |
+-------> 诊断 / 遥测
Nav2 / cmd_vel ------------------------------> sim2real_runtime
(经 odom_relay_node 提供 odom→base_link TF)
```
## 当前状态
已完成 Phase 0-5 的全部迁移:
1. ✅ 冻结部署契约(deployment_contract.hpp
2. ✅ ROS 2 包结构搭建
3. ✅ 硬件热路径迁移至 C++SocketCAN 驱动、200Hz 电机循环)
4. ✅ ONNX 策略运行时迁移至 C++(50Hz 推理循环)
5. ✅ 导航与诊断通过 ROS 2 接入(Nav2 + odom_relay + TF
## 契约来源
迁移过程中以下文件被视为真值源:
- `sim2real/deployment_manifest.yaml`
- `sim2real/interface/motor_mapping.py`
- `sim2real/interface/real_io.py`
- `sim2real/policy/policy_runner.py`
- `sim2real/web/session.py`
## 注意事项
- 开发目标为 Linux + ROS 2 Humble,运行于 Jetson Orin / x86_64。
- Windows 仅作为编辑环境使用。
- 观测顺序、动作缩放、默认站姿、电机映射不得独立修改,
除非训练与部署同步更新。
- 默认 Rough 策略为 `model_9600`,默认站姿回到比赛站姿髋俯仰 `0.550`、膝关节 `-1.125`
- Odin `custom_map_mode` 固定为 `0`(纯里程计),避免没有 `map_a.bin` 时产生 map/odom TF 冲突。
- `A_min.pcd``C.pcd``map_b.pcd` 均为确定性抽样预览,点数和哈希见 [`map/README.md`](map/README.md)。
- TensorRT engine 与 JetPack、TensorRT 版本及 GPU 架构有关,其他机器应从 ONNX 重新生成。
@@ -0,0 +1,22 @@
version: '3.8'
services:
sim2real_ros2:
build:
context: .
dockerfile: Dockerfile
container_name: sim2real_ros2_node
runtime: nvidia
network_mode: host
privileged: true
stdin_open: true
tty: true
environment:
- NVIDIA_VISIBLE_DEVICES=all
- NVIDIA_DRIVER_CAPABILITIES=compute,utility
cap_add:
- SYS_NICE
shm_size: '2gb'
volumes:
- /dev:/dev
restart: unless-stopped
@@ -0,0 +1,12 @@
#!/bin/bash
set -e
# Source ROS2 Humble environment
source /opt/ros/humble/setup.bash
# Source workspace install setup if compiled
if [ -f "/sim2real_ws/install/setup.bash" ]; then
source /sim2real_ws/install/setup.bash
fi
exec "$@"
@@ -0,0 +1,102 @@
# sim2real_ros2_v2 架构说明
## 设计目标
- 保留已验证的 RL 部署契约不变
- 将低延迟循环从 Python 迁移至 C++
- 暴露标准 ROS 2 接口用于导航和系统集成
- 保持安全边界独立于策略正确性
## 各包职责
### `sim2real_interfaces`(接口消息)
定义最小化的运行时消息:
- `RuntimeState`
硬件桥接发布的归一化运行时状态快照
- `RuntimeTarget`
策略运行时发送至硬件桥接的最新策略目标
### `sim2real_common`(共享常量)
存储编译期常量和部署契约辅助:
- 观测维度和字段布局
- 动作维度和轮子索引
- 关节顺序和默认站姿
- 动作缩放因子和默认循环频率
- Mahony 姿态滤波器
- 站立平衡控制器
- 安全监控器(SafetyMonitor / RuntimeGuard
### `sim2real_hw`(硬件桥接)
拥有硬件侧执行循环和安全边界:
- RobStride CAN 收发
- IMU 与 Odin 状态采集
- 电机丢帧检测与保活逻辑(holdover)
- 看门狗与阻尼刹车
- 发布 `RuntimeState`
- 订阅 `RuntimeTarget`
- 订阅 `/odom` 里程计数据
目标热路径:
-`200Hz` 频率读取状态
- 应用最新安全目标
- 超时或安全违规时立即停机
### `sim2real_runtime`(策略运行时)
拥有策略侧执行:
- 订阅 `RuntimeState`
- 按当前部署契约精确构建 `53D` 观测
-`50Hz` 运行 ONNXRuntime 推理
- 对 raw_action 做 `[-10, 10]` 安全裁剪
- 发布 `RuntimeTarget`
- 仲裁命令来源:estop > safety_hold > startup > navigation > web
同时包含:
- `odom_relay_node`:将 `/odin1/odometry` 中继为 `/odom`,帧名 `odin1_base_link``base_link`,并广播 TF
### `sim2real_nav2`(导航配置)
拥有:
- Nav2 参数文件(planner、controller、costmap、AMCL、behavior
- Nav2 启动文件(含 AMCL、costmap 生命周期节点、pointcloud_to_laserscan
### `sim2real_bringup`(启动管理)
拥有:
- 参数文件
- 启动组合
- 运行时模式选择
- 集成 odin_ros_driver、sim2real_nav2 的条件启动
## 迁移规则
1. 优化之前先冻结当前契约
2. 先迁移传输和循环结构,再调整控制算法
3. C++ 运行时未达到影子模式一致性前,保留 Python 运行时可用
4. 按段测量延迟:
- 观测延迟
- 策略推理延迟
- 目标传输延迟
- 执行器响应延迟
## 首个里程碑
首个里程碑不是"机器人在 ROS 2 下行走",而是:
1. `sim2real_hw` 发布稳定的 `RuntimeState`
2. `sim2real_runtime` 从该状态构建正确的 `53D` 观测
3. `sim2real_runtime``50Hz` 发布 `RuntimeTarget`
4. `sim2real_hw` 消费最新目标并执行超时刹车
5. `cmd_vel` 可通过 ROS 2 注入而不改变策略契约
> ✅ 以上里程碑已全部完成。
@@ -0,0 +1,78 @@
# 迁移计划
> 版本范围:ROS 2 v2 里程计联调快照(`v0.12.0`)。完成标记表示该快照中的代码迁移状态,不等于已在任意新硬件环境复现。
## Phase 1: 硬件核心迁移 ✅ 已完成
将当前高频热路径从 Python 迁出。
吸收的源文件:
- `sim2real/interface/motor_driver.py`
- `sim2real/interface/motor_mapping.py`
- `sim2real/interface/imu_client.py`
- `sim2real/safety/runtime_guard.py`
- `sim2real/web/session.py`
交付物:
- C++ SocketCAN 电机总线封装
- C++ 状态缓存
- target 超时保活(timeout hold
- 阻尼刹车 / 急停通路
- 发布 `RuntimeState`
## Phase 2: 策略运行时迁移 ✅ 已完成
吸收的源文件:
- `sim2real/policy/policy_runner.py`
- `sim2real/interface/real_io.py`
- `sim2real/web/session.py`
交付物:
- 精确的 `53D` 观测构造器
- ONNXRuntime C++ 推理封装
- `50Hz` 策略定时器
- 命令平滑与来源仲裁
- raw_action `[-10, 10]` 安全裁剪
- 发布 `RuntimeTarget`
## Phase 3: ROS 2 系统集成 ✅ 已完成
参考过的外部项目包括 Odin ROS 驱动、EDULITE A3 ROS 工程和 RL-SAR;外部参考目录不属于本仓库公开内容。
交付物:
- `cmd_vel` / `cmd_vel_stamped` 输入(支持 Twist 和 TwistStamped
- `odom_relay_node`:里程计中继 + TF 广播(odom → base_link
- 诊断话题
- rosbag/foxglove 可观测性
## Phase 4: 导航集成 ✅ 已完成
目标:
- 导航通过 ROS 2 发送身体速度指令
- RL 运行时保持为 locomotion 控制器
- 看门狗和安全边界始终在导航之下
规则:
- 导航绝不直接写电机指令
- 策略契约在重新训练前保持不变
- 任何新增历史项或里程计项必须版本化
## 当前状态
所有 4 个 Phase 已全部完成。以下为已实现的关键组件:
| 组件 | 节点 | 说明 |
|------|------|------|
| 硬件桥接 | `sim2real_hw_node` | 200Hz CAN 收发 + IMU + Mahony + 安全 |
| 策略运行时 | `sim2real_runtime_node` | 50Hz ONNX 推理 + 53D 观测 + raw_action clip |
| 里程计中继 | `odom_relay_node` | /odin1/odometry → /odom + odom→base_link TF |
| 导航栈 | Nav2 全套节点 | AMCL + costmap + DWB + Navfn + BT + lifecycle |
| 传感器驱动 | `odin_ros_driver` | IMU + 点云 + 里程计原始发布 |
| 点云转换 | `pointcloud_to_laserscan` | 外部 ROS 包;/odin1/cloud_slam → /scan(供 AMCL 使用) |
@@ -0,0 +1,495 @@
# sim2real_ros2_v2 遥控器调用说明
本文档说明如何在 `sim2real_ros2_v2` 中调用已接入的 SBUS UART 遥控器节点,以及执行后系统会产生什么效果。
## 1. 当前接入关系
遥控器节点位于:
```text
src/sim2real_runtime/src/remote_uart_node.py
```
该节点读取 SBUS 串口数据,并发布标准 ROS 2 控制话题:
| 输入 | 输出 | 作用 |
|---|---|---|
| SBUS UART 遥控器 | `/cmd_vel` | 给策略运行时发送速度命令 |
| SBUS CH7 高位 | `/safety/estop` | 触发软件急停 |
策略节点 `sim2real_runtime_node` 已经订阅 `/cmd_vel``/safety/estop`,所以遥控器不直接控制电机,而是通过 ROS 2 标准速度接口进入策略控制链路。
## 2. 通道映射
通道映射与前一阶段 Python Sim2Real 中的遥控器实现保持一致。
| 遥控器通道 | ROS 2 输出 | 含义 | 默认最大值 |
|---|---|---|---:|
| `CH2` | `cmd_vel.linear.x` | 前后速度 `vx` | `0.8 m/s` |
| `CH4` | `cmd_vel.linear.y` | 左右速度 `vy` | `0.3 m/s` |
| `CH1` | `cmd_vel.angular.z` | 转向角速度 `yaw` | `0.5 rad/s` |
| `CH7 HIGH` | `/safety/estop = true` | 软件急停 | - |
默认方向反转配置:
| 参数 | 默认值 | 含义 |
|---|---:|---|
| `remote_invert_vx` | `true` | 反转前后方向 |
| `remote_invert_vy` | `false` | 不反转横移方向 |
| `remote_invert_yaw` | `true` | 反转转向方向 |
## 3. 参数位置
遥控器参数在:
```text
src/sim2real_bringup/config/runtime.yaml
```
当前默认参数:
```yaml
remote_enabled: true
remote_port: "/dev/ttyACM0"
remote_baudrate: 100000
remote_timeout: 0.02
remote_axis_deadzone: 50
remote_active_threshold: 50
remote_axis_full_scale: 660.0
remote_max_vx: 0.8
remote_max_vy: 0.3
remote_max_yaw_rate: 0.5
remote_invert_vx: true
remote_invert_vy: false
remote_invert_yaw: true
remote_publish_inactive_zero: true
remote_estop_latch: true
remote_poll_hz: 50.0
```
如果遥控器串口不是 `/dev/ttyACM0`,需要修改:
```yaml
remote_port: "/dev/ttyUSB0"
```
或改成实际设备路径。
## 4. 启动前检查
### 4.1 确认串口存在
```bash
ls /dev/ttyACM* /dev/ttyUSB*
```
如果使用默认配置,应能看到:
```bash
/dev/ttyACM0
```
### 4.2 确认串口权限
如果节点提示串口权限不足,可以临时执行:
```bash
sudo chmod 666 /dev/ttyACM0
```
更推荐的长期方式是把当前用户加入 `dialout` 组:
```bash
sudo usermod -aG dialout $USER
```
然后重新登录。
### 4.3 确认 Python serial 依赖
节点依赖 `pyserial`。如果系统没有安装:
```bash
sudo apt update
sudo apt install -y python3-serial
```
## 5. 构建
如果刚修改过代码或参数,建议重新构建相关包:
```bash
cd /path/to/sim2real_ros2_v2
source /opt/ros/humble/setup.bash
colcon build --packages-select sim2real_runtime sim2real_bringup --symlink-install --merge-install
```
构建完成后 source 环境:
```bash
source install/setup.bash
```
确认可执行节点存在:
```bash
ros2 pkg executables sim2real_runtime
```
应包含:
```text
sim2real_runtime remote_uart_node.py
```
## 6. 推荐启动方式
### 6.1 启动完整系统,不启动 Nav2
这是你当前常用方式:
```bash
cd /path/to/sim2real_ros2_v2
source /opt/ros/humble/setup.bash
source install/setup.bash
ros2 launch sim2real_bringup sim2real_system.launch.py launch_nav2:=false
```
默认情况下,`launch_remote:=true`,所以上面命令会同时启动遥控器节点。
等价完整写法:
```bash
ros2 launch sim2real_bringup sim2real_system.launch.py launch_nav2:=false launch_remote:=true
```
### 6.2 不启动遥控器
如果只想用手动 `ros2 topic pub` 或其他上位机发 `/cmd_vel`,可以关闭遥控器节点:
```bash
ros2 launch sim2real_bringup sim2real_system.launch.py launch_nav2:=false launch_remote:=false
```
## 7. 单独启动遥控器节点
如果系统已经在运行,只想单独测试遥控器节点:
```bash
cd /path/to/sim2real_ros2_v2
source /opt/ros/humble/setup.bash
source install/setup.bash
ros2 run sim2real_runtime remote_uart_node.py --ros-args --params-file src/sim2real_bringup/config/runtime.yaml
```
如果要临时指定串口:
```bash
ros2 run sim2real_runtime remote_uart_node.py --ros-args \
--params-file src/sim2real_bringup/config/runtime.yaml \
-p remote_port:=/dev/ttyUSB0
```
## 8. 执行后会产生什么效果
启动以下命令后:
```bash
ros2 launch sim2real_bringup sim2real_system.launch.py launch_nav2:=false
```
系统会产生以下效果。
### 8.1 启动硬件桥接节点
节点:
```text
/sim2real_hw_node
```
效果:
1. 打开 `can0``can1`
2. 如果 `dry_run: false` 且 CAN 初始化成功,会使能 16 个 RobStride 电机。
3. 设置电机 MIT 模式。
4. 设置电机速度限制和力矩限制。
5.`200Hz` 运行硬件读写循环。
6. 发布 `/runtime/state`
7. 订阅 `/runtime/target` 执行策略目标。
### 8.2 启动策略运行节点
节点:
```text
/sim2real_runtime_node
```
效果:
1. 加载 ONNX 策略模型。
2. 订阅 `/runtime/state`
3. 订阅 `/cmd_vel`
4. 订阅 `/safety/estop`
5. 执行启动站立流程:
- `boot_hold`
- `startup_soft_hold`
- `startup_hold`
- `runtime_zero_hold`
- `runtime_policy`
6.`50Hz` 发布 `/runtime/target`
### 8.3 启动遥控器节点
节点:
```text
/sim2real_remote_uart_node
```
效果:
1. 打开默认串口 `/dev/ttyACM0`
2.`50Hz` 轮询 SBUS 数据。
3. 遥控器摇杆居中时持续发布零速度:
```text
/cmd_vel:
linear.x = 0.0
linear.y = 0.0
angular.z = 0.0
```
4. 推动遥控器时发布非零速度,例如:
```text
/cmd_vel:
linear.x = vx
linear.y = vy
angular.z = yaw
```
5. 当 CH7 打到高位时发布:
```text
/safety/estop: true
```
由于当前 `remote_estop_latch: true`,急停是锁存式行为:一旦 CH7 高位触发,节点会发布急停,并保持内部急停已触发状态。恢复运行通常需要重启系统或手动发布复位信号,并确认机器人安全。
### 8.4 机器人行为效果
正常启动后,机器人不会立即按策略行走,而是按阶段执行:
1. 电机使能。
2. 读取当前关节位置。
3. 软保持当前姿态。
4. 平滑过渡到默认站立姿态。
5. 稳定后进入 runtime。
6. 遥控器无输入时保持站立平衡,即 `runtime_zero_hold`
7. 遥控器有输入时进入策略控制,即 `runtime_policy`
也就是说:
| 遥控器状态 | 机器人效果 |
|---|---|
| 摇杆居中 | 站立保持,不主动行走 |
| CH2 前后推动 | 前进/后退 |
| CH4 左右推动 | 横向移动 |
| CH1 左右推动 | 原地转向 |
| CH7 高位 | 软件急停,进入安全刹车 |
## 9. 如何确认遥控器已经生效
### 9.1 查看节点是否存在
```bash
ros2 node list
```
应看到:
```text
/sim2real_remote_uart_node
/sim2real_runtime_node
/sim2real_hw_node
```
### 9.2 查看 `/cmd_vel`
```bash
ros2 topic echo /cmd_vel
```
摇动遥控器时应看到 `linear.x``linear.y``angular.z` 变化。
### 9.3 查看 `/safety/estop`
```bash
ros2 topic echo /safety/estop
```
CH7 高位时应看到:
```yaml
data: true
```
### 9.4 查看策略目标阶段
```bash
ros2 topic echo /runtime/target --field target_source
```
常见输出含义:
| `target_source` | 含义 |
|---|---|
| `boot_hold` | 刚启动,保持初始姿态 |
| `startup_soft_hold` | 启动软保持 |
| `startup_hold` | 正在站立或站立后保持 |
| `runtime_zero_hold` | 已进入 runtime,遥控器无有效输入 |
| `runtime_policy` | 遥控器有输入,策略已经介入 |
| `safety_brake` | 安全刹车 |
| `timeout_hold` | 目标超时,硬件保持默认姿态 |
### 9.5 查看完整目标状态
```bash
ros2 topic echo --once /runtime/target
```
重点关注字段:
```yaml
target_source:
zero_command:
runtime_released:
release_alpha:
command:
raw_command:
```
如果遥控器摇杆有输入,通常会看到:
```yaml
target_source: runtime_policy
zero_command: false
runtime_released: true
release_alpha: 1.0
```
## 10. 常见问题
### 10.1 启动后提示无法打开串口
可能原因:
1. 串口路径不对。
2. 权限不足。
3. 设备没有插好。
4. 设备被其他程序占用。
检查:
```bash
ls /dev/ttyACM* /dev/ttyUSB*
```
修改 `runtime.yaml`
```yaml
remote_port: "/dev/ttyUSB0"
```
### 10.2 `/cmd_vel` 没有变化
检查:
```bash
ros2 node list
ros2 topic echo /cmd_vel
```
如果节点存在但无变化,可能是:
1. 遥控器没有输出 SBUS。
2. 串口波特率不对。
3. SBUS 接线错误。
4. 遥控器通道未校准。
5. 死区 `remote_axis_deadzone``remote_active_threshold` 太大。
### 10.3 摇杆方向反了
修改:
```yaml
remote_invert_vx: true
remote_invert_vy: false
remote_invert_yaw: true
```
例如前后方向反了,就切换:
```yaml
remote_invert_vx: false
```
### 10.4 急停后不恢复
当前配置:
```yaml
remote_estop_latch: true
```
这表示急停锁存。触发后建议:
1. 先确认机器人物理安全。
2. 停止 launch。
3. 将 CH7 打回安全位置。
4. 重新启动系统。
如果需要非锁存模式,可以改为:
```yaml
remote_estop_latch: false
```
但实机调试时更建议使用锁存模式。
## 11. 快速验证命令清单
```bash
cd /path/to/sim2real_ros2_v2
source /opt/ros/humble/setup.bash
source install/setup.bash
ros2 launch sim2real_bringup sim2real_system.launch.py launch_nav2:=false
```
另开终端:
```bash
cd /path/to/sim2real_ros2_v2
source /opt/ros/humble/setup.bash
source install/setup.bash
ros2 node list
ros2 topic echo /cmd_vel
ros2 topic echo /runtime/target --field target_source
```
如果只测遥控器,不启动电机系统:
```bash
ros2 run sim2real_runtime remote_uart_node.py --ros-args --params-file src/sim2real_bringup/config/runtime.yaml
```
另开终端:
```bash
ros2 topic echo /cmd_vel
ros2 topic echo /safety/estop
```
@@ -0,0 +1,246 @@
# sim2real_ros2_v2 Web UDP 调试说明
本文档说明本次新增的最小 Web 调试链路。
## 1. 架构
```text
Windows 本地浏览器/HTTP 服务
|
| UDP JSON
v
Nano: sim2real_web_udp_bridge_node.py
|
| ROS 2 topics
v
sim2real_cmd_mux_node.py -> /cmd_vel -> sim2real_runtime_node
```
Web 页面在 Windows 本地渲染,Nano 只运行轻量 UDP bridge 和 ROS2 节点。
## 2. 新增 ROS2 节点
### `remote_uart_node.py`
遥控器节点现在发布:
```text
/cmd_vel_remote
```
不再直接发布 `/cmd_vel`
通道触发阈值改为:
```yaml
remote_axis_deadzone: 40
remote_active_threshold: 40
```
只有通道归一化值绝对值大于 `40` 才认为是有效输入。
### `cmd_mux_node.py`
输入:
```text
/cmd_vel_remote
/cmd_vel_web
/cmd_vel_nav
/control/mode
/remote/enabled
/web/enabled
/nav/enabled
/safety/estop
```
输出:
```text
/cmd_vel
/control/mode_state
/control/mux_status
```
控制模式:
```text
DISABLED
REMOTE
WEB
NAV
```
急停 `/safety/estop=true` 会强制进入 `DISABLED`,并输出零速度。
### `web_udp_bridge_node.py`
Nano 端 UDP 监听:
```text
0.0.0.0:15000
```
发布:
```text
/cmd_vel_web
/safety/estop
/control/mode
/web/enabled
/remote/enabled
/nav/enabled
```
订阅并回传状态:
```text
/runtime/state
/runtime/target
/cmd_vel
/safety/estop
/control/mode_state
/control/mux_status
```
## 3. Nano 启动
```bash
cd /path/to/sim2real_ros2_v2
source /opt/ros/humble/setup.bash
source install/setup.bash
ros2 launch sim2real_bringup sim2real_system.launch.py launch_nav2:=false
```
默认会启动:
```text
sim2real_remote_uart_node
sim2real_cmd_mux_node
sim2real_web_udp_bridge_node
```
如果不想启动 Web UDP bridge
```bash
ros2 launch sim2real_bringup sim2real_system.launch.py launch_nav2:=false launch_web_bridge:=false
```
## 4. Windows 本地 Web 启动
把目录复制到 Windows 或通过共享目录访问:
```text
tools/win_web_debug
```
在 Windows 上安装 Python 3 后运行:
```bash
python server.py --nano-host <Nano_IP> --http-port 8088 --udp-port 15001
```
浏览器打开:
```text
http://127.0.0.1:8088
```
## 5. UDP 命令格式
### 切换模式
```json
{"type":"mode","mode":"REMOTE"}
```
```json
{"type":"mode","mode":"WEB"}
```
```json
{"type":"mode","mode":"DISABLED"}
```
### Web 速度控制
```json
{
"type": "cmd_vel",
"linear": {"x": 0.2, "y": 0.0, "z": 0.0},
"angular": {"x": 0.0, "y": 0.0, "z": 0.1}
}
```
Nano 端会再次限幅:
```text
vx <= ±0.8 m/s
vy <= ±0.3 m/s
yaw <= ±0.5 rad/s
```
### 零速度
```json
{"type":"zero"}
```
### 软急停
```json
{"type":"estop","data":true}
```
## 6. 安全保护
当前最小版本已经包含:
1. 遥控器误触发阈值:`40`
2. 遥控器/Web/Nav 互斥控制模式。
3. `cmd_mux` 二次限幅。
4. `cmd_mux` 加速度限制。
5. Web UDP 超时自动发布零速度。
6. 急停优先级最高。
7. Web 页面切换到 `WEB` 模式需要确认。
8. Web 松开虚拟摇杆会自动发送零速度。
建议实机调试流程:
1. 先点击 `DISABLED`
2. 确认 `/cmd_vel` 为零。
3. 如果使用遥控器,点击 `REMOTE`
4. 如果使用 Web,点击 `WEB` 并确认周围安全。
5. 一旦异常,立即点击 `软急停`
## 7. 验证命令
查看最终输出速度:
```bash
ros2 topic echo /cmd_vel
```
查看遥控器输入:
```bash
ros2 topic echo /cmd_vel_remote
```
查看 Web 输入:
```bash
ros2 topic echo /cmd_vel_web
```
查看当前仲裁模式:
```bash
ros2 topic echo /control/mode_state
```
查看策略状态:
```bash
ros2 topic echo /runtime/target --field target_source
```
@@ -0,0 +1,20 @@
Tcl_0: [-0.009160, -0.999960, 0.000320, 0.032150,
0.002390, -0.000340, -1.000000, -0.011850,
0.999960, -0.009160, 0.002390, 0.005360,
0.000000, 0.000000, 0.000000, 1.000000]
cam_0:
image_width: 1600
image_height: 1296
k2: 0.000656
k3: -0.028961
k4: 0.045390
k5: -0.064513
k6: 0.038735
k7: -0.009903
p1: 0.000000
p2: 0.000000
A11: 736.894262
A12: -0.161150
A22: 736.611354
u0: 806.125535
v0: 639.650710
File diff suppressed because it is too large Load Diff
File diff suppressed because it is too large Load Diff
@@ -0,0 +1,11 @@
# `v0.12.0` 里程计导航点云
本阶段使用三份 ASCII PCD 进行路线和里程计导航联调。原始文件均使用 `tools/nav_tools/downsample_ascii_pcd.py` 确定性等步长抽样,抽样文件仅用于可视化、路线编辑和软件回归,不等价于原始高密度地图。
| 文件 | 原始点数 | 步长 | 抽样点数 | 抽样大小 | 原始 SHA-256 | 抽样 SHA-256 |
| --- | ---: | ---: | ---: | ---: | --- | --- |
| `map_b.pcd` | 1,080,047 | 4 | 270,012 | 8,680,283 B | `F17FC7E188C772D48E8D3FE20E3A934AFCDF59357C27BCCDCAC3C7E9AD1D1CB7` | `F3052431F217DE8FFFE87726F8E547A59C65048B0285F02AF4803C5F4E4C1B42` |
| `A_min.pcd` | 683,908 | 3 | 227,969 | 7,300,173 B | `77C884B17D78C4D157F84093BFEA0729FA25382C70E706C4BA12913A3C90ED20` | `CC4556D472BE6E5B232B9BFEE43F5F337D710F88E88D9B90994368C580327DC1` |
| `C.pcd` | 3,186,653 | 11 | 289,696 | 9,040,786 B | `C5C7E60B06A0FFEA15E55C14B8CFAFBC8525371C8C3AC9C72821AB736AED8A6C` | `1FC4124642D934460811DBF62EBE9C5C9CF30668BC42E4782B18F3C002ED1A25` |
大小和 SHA-256 均按 Git 中保存的字节计算;根目录 `.gitattributes` 禁止对 `*.pcd` 做跨平台换行转换。
File diff suppressed because it is too large Load Diff
@@ -0,0 +1,276 @@
{
"name": "A_min_route",
"map": "A_min",
"frame_id": "map",
"createdAt": "2026-07-04T00:40:56",
"segments": [
{
"name": "segment_1",
"obstacle": "slalom",
"waypoints": [
{
"id": 1,
"x": 9.3549,
"y": 0.9079,
"yawDeg": 0.0,
"speed": 0.7,
"policy": "rough",
"tolerance": 0.15
},
{
"id": 2,
"x": 11.1136,
"y": 0.8621,
"yawDeg": 0.0,
"speed": 0.7,
"policy": "rough",
"tolerance": 0.15
},
{
"id": 3,
"x": 13.0,
"y": 0.75,
"yawDeg": 88.0,
"speed": 0.7,
"policy": "rough",
"tolerance": 0.15
},
{
"id": 4,
"x": 12.9102,
"y": 2.611,
"yawDeg": 90.0,
"speed": 0.7,
"policy": "rough",
"tolerance": 0.15
},
{
"id": 5,
"x": 12.9102,
"y": 4.88,
"yawDeg": 190.0,
"speed": 0.7,
"policy": "rough",
"tolerance": 0.15
},
{
"id": 6,
"x": 11.94,
"y": 4.6203,
"yawDeg": 178.0,
"speed": 0.7,
"policy": "rough",
"tolerance": 0.15
},
{
"id": 7,
"x": 10.65,
"y": 4.65,
"yawDeg": 178.0,
"speed": 0.7,
"policy": "rough",
"tolerance": 0.15
},
{
"id": 8,
"x": 9.3997,
"y": 4.649,
"yawDeg": -95.0,
"speed": 0.7,
"policy": "rough",
"tolerance": 0.15
},
{
"id": 9,
"x": 9.3092,
"y": 3.8864,
"yawDeg": 180.0,
"speed": 0.7,
"policy": "rough",
"tolerance": 0.15
},
{
"id": 10,
"x": 7.9263,
"y": 4.0028,
"yawDeg": 150.0,
"speed": 0.7,
"policy": "rough",
"tolerance": 0.15
},
{
"id": 11,
"x": 6.8148,
"y": 4.6619,
"yawDeg": 170.0,
"speed": 0.7,
"policy": "rough",
"tolerance": 0.15
},
{
"id": 12,
"x": 5.0054,
"y": 5.0,
"yawDeg": 180.0,
"speed": 0.7,
"policy": "rough",
"tolerance": 0.15
},
{
"id": 13,
"x": 3.2994,
"y": 5.04,
"yawDeg": 185.0,
"speed": 0.7,
"policy": "crawl",
"tolerance": 0.15
},
{
"id": 14,
"x": 1.8838,
"y": 4.8455,
"yawDeg": 190.0,
"speed": 0.7,
"policy": "rough",
"tolerance": 0.15
},
{
"id": 15,
"x": 1.06,
"y": 4.45,
"yawDeg": 270.0,
"speed": 0.35,
"policy": "rough",
"tolerance": 0.15
},
{
"id": 16,
"x": 1.0561,
"y": 3.9,
"yawDeg": -90.0,
"speed": 0.7,
"policy": "rough",
"tolerance": 0.15
},
{
"id": 17,
"x": 1.04,
"y": 2.79,
"yawDeg": 0.0,
"speed": 0.7,
"policy": "rough",
"tolerance": 0.15
},
{
"id": 18,
"x": 1.552,
"y": 2.77,
"yawDeg": -90.0,
"speed": 0.7,
"policy": "rough",
"tolerance": 0.15
},
{
"id": 19,
"x": 1.519,
"y": 1.6677,
"yawDeg": 180.0,
"speed": 0.7,
"policy": "rough",
"tolerance": 0.15
},
{
"id": 20,
"x": 0.4708,
"y": 1.6677,
"yawDeg": -90.0,
"speed": 0.7,
"policy": "rough",
"tolerance": 0.15
},
{
"id": 21,
"x": 0.4552,
"y": 0.5,
"yawDeg": 0.0,
"speed": 0.7,
"policy": "rough",
"tolerance": 0.15
},
{
"id": 22,
"x": 1.2783,
"y": 0.5,
"yawDeg": 90.0,
"speed": 0.7,
"policy": "rough",
"tolerance": 0.15
},
{
"id": 23,
"x": 1.2628,
"y": 1.6211,
"yawDeg": 0.0,
"speed": 0.7,
"policy": "rough",
"tolerance": 0.15
},
{
"id": 24,
"x": 2.3576,
"y": 1.5978,
"yawDeg": -90.0,
"speed": 0.7,
"policy": "rough",
"tolerance": 0.15
},
{
"id": 25,
"x": 2.3265,
"y": 0.5,
"yawDeg": 0.0,
"speed": 0.7,
"policy": "rough",
"tolerance": 0.15
},
{
"id": 26,
"x": 3.12,
"y": 0.5,
"yawDeg": 45.0,
"speed": 0.7,
"policy": "rough",
"tolerance": 0.15
},
{
"id": 27,
"x": 3.8639,
"y": 0.9844,
"yawDeg": 0.0,
"speed": 0.7,
"policy": "rough",
"tolerance": 0.15
},
{
"id": 28,
"x": 6.8222,
"y": 0.9145,
"yawDeg": 0.0,
"speed": 0.7,
"policy": "rough",
"tolerance": 0.15
},
{
"id": 29,
"x": 9.5321,
"y": 0.9223,
"yawDeg": 0.0,
"speed": 0.7,
"policy": "rough",
"tolerance": 0.15
}
]
}
],
"yawToleranceDegDefault": 30.0
}
@@ -0,0 +1,12 @@
# `v0.12.0` 策略文件
本阶段运行配置使用 `model_9600` 作为 Rough 策略,并保留 Crawl ONNX 和部署契约参考模型。
| 文件 | 用途 |
| --- | --- |
| `model_9600.onnx` | 里程计版本 Rough ONNX |
| `model_9600_fp16.engine` | 里程计版本 Rough TensorRT |
| `model_crawl.onnx` | Crawl 候选;当前配置使用 IK 后端 |
| `model_rough.onnx` | 原部署契约参考基线 |
其余 `model_6800``model_8400``model_10200` 等候选策略属于相邻实验或后续比赛版本,不在本 Tag 重复归档。
@@ -0,0 +1,5 @@
recorddata/
/config/calib.yaml
!/config/calib.yaml
/log
/map
@@ -0,0 +1,8 @@
v0.10.5 2026_0525
1. 修改 recorddata 数据格式,新增 device_id、algorithm_version 字段
2. 修复 SLAM 模式下下载地图失败的问题(USB2.0)
3. 修复重定位模式下上传地图失败的问题(USB2.0)
v0.10.4 2026_0522
1. 修复 USB2.0 心跳超时导致软断开的问题
2. control_command.yaml 新增 custom_init_pose_search_radius 和 custom_init_pose_max_rot_deg 参数
@@ -0,0 +1,447 @@
cmake_minimum_required(VERSION 3.5)
project(odin_ros_driver)
if(DEFINED BUILD_SYSTEM)
set(ROS_VERSION ${BUILD_SYSTEM})
message(STATUS "ROS_VERSION: ${ROS_VERSION}")
elseif(DEFINED ENV{ROS_DISTRO})
if("$ENV{ROS_DISTRO}" MATCHES "foxy|galactic|humble|iron|rolling")
set(ROS_VERSION "ROS2")
else()
set(ROS_VERSION "ROS1")
endif()
elseif(DEFINED ENV{ROS_VERSION})
if("$ENV{ROS_VERSION}" EQUAL "2")
set(ROS_VERSION "ROS2")
else()
set(ROS_VERSION "ROS1")
endif()
else()
# Attempt automatic detection
if(COMMAND catkin_package)
set(ROS_VERSION "ROS1")
elseif(COMMAND ament_package)
set(ROS_VERSION "ROS2")
else()
# Default to ROS2
set(ROS_VERSION "ROS2")
message(WARNING "Unable to determine ROS version, defaulting to ROS2")
endif()
endif()
# Add compile definitions after detecting ROS version
if(ROS_VERSION STREQUAL "ROS2")
add_definitions(-DROS2)
message(STATUS "Defining ROS2")
else()
add_definitions(-DROS1)
message(STATUS "Defining ROS1")
endif()
message(STATUS "Build system: ${ROS_VERSION}")
# Platform detection
execute_process(
COMMAND uname -m
OUTPUT_VARIABLE ARCH
OUTPUT_STRIP_TRAILING_WHITESPACE
)
if(ARCH STREQUAL "x86_64")
set(TARGET_PLATFORM "x86")
message(STATUS "Detected x86_64 architecture")
elseif(ARCH MATCHES "arm|aarch64")
set(TARGET_PLATFORM "arm")
message(STATUS "Detected ARM architecture: ${ARCH}")
else()
message(WARNING "Unsupported architecture: ${ARCH}. Using default settings")
set(TARGET_PLATFORM "unknown")
endif()
# Set library path
set(LIB_DIR "${CMAKE_CURRENT_SOURCE_DIR}/lib")
message(STATUS "Library directory: ${LIB_DIR}")
# Set library name based on platform
if(TARGET_PLATFORM STREQUAL "arm")
set(LYD_HOST_API_LIB_NAME "lydHostApi_arm")
else()
set(LYD_HOST_API_LIB_NAME "lydHostApi_amd")
endif()
# Find precompiled lydHostApi library
find_library(LYD_HOST_API_LIB
NAMES
${LYD_HOST_API_LIB_NAME}
lib${LYD_HOST_API_LIB_NAME}.a
lib${LYD_HOST_API_LIB_NAME}.so
PATHS ${LIB_DIR}
NO_DEFAULT_PATH
)
if(LYD_HOST_API_LIB)
message(STATUS "Found lydHostApi library: ${LYD_HOST_API_LIB}")
else()
file(GLOB LIB_FILES "${LIB_DIR}/lib${LYD_HOST_API_LIB_NAME}.*")
if(LIB_FILES)
message(STATUS "Found library files: ${LIB_FILES}")
set(LYD_HOST_API_LIB ${LIB_FILES})
else()
message(FATAL_ERROR "Could not find precompiled lydHostApi library in ${LIB_DIR}")
endif()
endif()
# Set common compile options
set(CMAKE_CXX_STANDARD 17)
set(CMAKE_CXX_STANDARD_REQUIRED ON)
# Set optimization flags
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -O2")
# Find common dependencies
find_package(PkgConfig REQUIRED)
find_package(OpenCV REQUIRED)
find_package(Eigen3 REQUIRED)
find_package(PCL REQUIRED)
find_package(yaml-cpp REQUIRED)
find_package(OpenSSL REQUIRED)
pkg_check_modules(LIBUSB REQUIRED libusb-1.0)
# Shared include directories
include_directories(
include
${EIGEN3_INCLUDE_DIRS}
${OpenCV_INCLUDE_DIRS}
${PCL_INCLUDE_DIRS}
${yaml-cpp_INCLUDE_DIR}
${LIBUSB_INCLUDE_DIRS}
${CMAKE_CURRENT_SOURCE_DIR}/include
)
# Shared library list
set(COMMON_LIBS
${OpenCV_LIBS}
${PCL_LIBRARIES}
${yaml-cpp_LIBRARIES}
${OPENSSL_LIBRARIES}
${LIBUSB_LIBRARIES}
pthread
rt
${CMAKE_DL_LIBS}
${LYD_HOST_API_LIB}
)
# ===== ROS1 Configuration =====
if(ROS_VERSION STREQUAL "ROS1")
message(STATUS "Configuring for ROS1 build")
find_package(catkin REQUIRED COMPONENTS
roscpp
std_msgs
sensor_msgs
nav_msgs
cv_bridge
tf
image_transport
)
include_directories(${catkin_INCLUDE_DIRS})
catkin_package(
CATKIN_DEPENDS roscpp std_msgs sensor_msgs nav_msgs cv_bridge image_transport
INCLUDE_DIRS include
)
# Set output directories
set(CMAKE_RUNTIME_OUTPUT_DIRECTORY ${CATKIN_DEVEL_PREFIX}/lib/${PROJECT_NAME})
set(CMAKE_LIBRARY_OUTPUT_DIRECTORY ${CATKIN_DEVEL_PREFIX}/lib)
set(CMAKE_ARCHIVE_OUTPUT_DIRECTORY ${CATKIN_DEVEL_PREFIX}/lib)
add_executable(host_sdk_sample
src/host_sdk_sample.cpp
src/yaml_parser.cpp
src/rawCloudRender.cpp
src/camera_pose_visualization.cpp
)
target_link_libraries(host_sdk_sample
${catkin_LIBRARIES}
${COMMON_LIBS}
${LYD_HOST_API_LIB}
${LIBUSB_LIBRARIES}
yaml-cpp
${OpenCV_LIBS}
pthread
usb-1.0
)
add_library(pointcloud_depth_converter src/pointcloud_depth_converter.cpp)
target_link_libraries(pointcloud_depth_converter
${OpenCV_LIBS}
${PCL_LIBRARIES}
)
add_library(depth_image_ros_node src/depth_image_ros_node.cpp)
target_link_libraries(depth_image_ros_node
pointcloud_depth_converter
${catkin_LIBRARIES}
${OpenCV_LIBS}
${PCL_LIBRARIES}
)
add_executable(pcd2depth_node src/pcd2depth_ros.cpp)
target_link_libraries(pcd2depth_node
depth_image_ros_node
${catkin_LIBRARIES}
${OpenCV_LIBS}
${PCL_LIBRARIES}
)
add_library(cloud_reprojector src/cloud_reprojector.cpp)
target_link_libraries(cloud_reprojector
${OpenCV_LIBS}
${PCL_LIBRARIES}
)
add_executable(cloud_reprojection_node src/cloud_reprojection_ros.cpp)
target_link_libraries(cloud_reprojection_node
cloud_reprojector
${catkin_LIBRARIES}
${OpenCV_LIBS}
${PCL_LIBRARIES}
)
add_executable(image_overlay_node src/image_overlay_node.cpp)
target_link_libraries(image_overlay_node
${catkin_LIBRARIES}
${OpenCV_LIBS}
)
# Installation rules
install(TARGETS host_sdk_sample
RUNTIME DESTINATION ${CATKIN_PACKAGE_BIN_DESTINATION}
)
install(DIRECTORY include/
DESTINATION ${CATKIN_PACKAGE_INCLUDE_DESTINATION}
FILES_MATCHING PATTERN "*.h"
)
if(EXISTS "${CMAKE_CURRENT_SOURCE_DIR}/config")
install(DIRECTORY config/
DESTINATION ${CATKIN_PACKAGE_SHARE_DESTINATION}/config
)
endif()
# ===== ROS2 Configuration =====
elseif(ROS_VERSION STREQUAL "ROS2")
message(STATUS "Configuring for ROS2 build")
# Find all necessary ROS2 packages
find_package(ament_cmake REQUIRED)
find_package(rclcpp REQUIRED)
find_package(std_msgs REQUIRED)
find_package(sensor_msgs REQUIRED)
find_package(nav_msgs REQUIRED)
find_package(visualization_msgs REQUIRED)
find_package(cv_bridge REQUIRED)
find_package(image_transport REQUIRED)
find_package(pcl_conversions REQUIRED)
find_package(message_filters REQUIRED)
find_package(tf2 REQUIRED)
find_package(tf2_ros REQUIRED)
find_package(geometry_msgs REQUIRED)
find_package(tf2_geometry_msgs REQUIRED)
# Create executable
add_executable(host_sdk_sample
src/host_sdk_sample.cpp
src/yaml_parser.cpp
src/rawCloudRender.cpp
src/camera_pose_visualization.cpp
)
# Link libraries
target_link_libraries(host_sdk_sample
${COMMON_LIBS}
yaml-cpp
usb-1.0
)
# Add ROS2 dependencies
ament_target_dependencies(host_sdk_sample
rclcpp
std_msgs
sensor_msgs
nav_msgs
visualization_msgs
cv_bridge
image_transport
tf2
tf2_ros
geometry_msgs
tf2_geometry_msgs
)
add_library(pointcloud_depth_converter_ros2 src/pointcloud_depth_converter.cpp)
target_link_libraries(pointcloud_depth_converter_ros2
${OpenCV_LIBS}
${PCL_LIBRARIES}
)
add_library(depth_image_ros2_node_lib src/depth_image_ros2_node.cpp)
target_link_libraries(depth_image_ros2_node_lib
pointcloud_depth_converter_ros2
${OpenCV_LIBS}
${PCL_LIBRARIES}
)
ament_target_dependencies(depth_image_ros2_node_lib
rclcpp
sensor_msgs
std_msgs
cv_bridge
image_transport
pcl_conversions
message_filters
)
add_executable(pcd2depth_ros2_node src/pcd2depth_ros2.cpp)
target_link_libraries(pcd2depth_ros2_node
depth_image_ros2_node_lib
${OpenCV_LIBS}
${PCL_LIBRARIES}
yaml-cpp
)
ament_target_dependencies(pcd2depth_ros2_node
rclcpp
sensor_msgs
std_msgs
cv_bridge
image_transport
pcl_conversions
message_filters
)
add_library(cloud_reprojector_ros2 src/cloud_reprojector.cpp)
target_compile_definitions(cloud_reprojector_ros2 PRIVATE ROS2)
target_link_libraries(cloud_reprojector_ros2
${OpenCV_LIBS}
${PCL_LIBRARIES}
)
add_executable(cloud_reprojection_ros2_node src/cloud_reprojection_ros.cpp)
target_compile_definitions(cloud_reprojection_ros2_node PRIVATE ROS2)
target_link_libraries(cloud_reprojection_ros2_node
cloud_reprojector_ros2
${OpenCV_LIBS}
${PCL_LIBRARIES}
yaml-cpp
)
ament_target_dependencies(cloud_reprojection_ros2_node
rclcpp
sensor_msgs
nav_msgs
cv_bridge
image_transport
pcl_conversions
message_filters
)
add_executable(image_overlay_node src/image_overlay_node.cpp)
target_compile_definitions(image_overlay_node PRIVATE ROS2)
target_link_libraries(image_overlay_node
${OpenCV_LIBS}
)
ament_target_dependencies(image_overlay_node
rclcpp
sensor_msgs
cv_bridge
image_transport
message_filters
)
# Installation rules - ensure all install targets are defined before ament_package()
# Install executable
install(TARGETS
host_sdk_sample
pcd2depth_ros2_node
cloud_reprojection_ros2_node
image_overlay_node
EXPORT export_${PROJECT_NAME}
ARCHIVE DESTINATION lib
LIBRARY DESTINATION lib
RUNTIME DESTINATION lib/${PROJECT_NAME}
)
# Install package.xml
install(FILES package.xml
DESTINATION share/${PROJECT_NAME}
)
# Install headers
install(DIRECTORY include/
DESTINATION include
)
# Install launch_ROS2 directory
if(EXISTS "${CMAKE_CURRENT_SOURCE_DIR}/launch_ROS2")
install(DIRECTORY launch_ROS2/
DESTINATION share/${PROJECT_NAME}/launch
)
message(STATUS "Installing launch_ROS2 directory to share/${PROJECT_NAME}/launch")
else()
message(WARNING "launch_ROS2 directory not found")
endif()
# Install config files
if(EXISTS "${CMAKE_CURRENT_SOURCE_DIR}/config")
install(DIRECTORY config/
DESTINATION share/${PROJECT_NAME}/config
)
endif()
# Install launch files
if(EXISTS "${CMAKE_CURRENT_SOURCE_DIR}/launch")
install(DIRECTORY launch/
DESTINATION share/${PROJECT_NAME}/launch
)
endif()
ament_export_targets(export_${PROJECT_NAME})
# Declare dependencies
ament_export_dependencies(
rclcpp
std_msgs
sensor_msgs
nav_msgs
visualization_msgs
cv_bridge
image_transport
pcl_conversions
message_filters
tf2
tf2_ros
geometry_msgs
tf2_geometry_msgs
)
ament_package()
message(STATUS "Install targets added")
else()
message(FATAL_ERROR "Invalid ROS_VERSION: ${ROS_VERSION}")
endif()
# ARM platform specific link options
if(TARGET_PLATFORM STREQUAL "arm")
set_target_properties(host_sdk_sample PROPERTIES
LINK_FLAGS "-Wl,--no-as-needed -Wl,--rpath=${LIB_DIR}"
)
message(STATUS "Adding ARM-specific link options and RPATH")
endif()
# Add debug information
message(STATUS "=======================================")
message(STATUS "Project: ${PROJECT_NAME}")
message(STATUS "ROS_VERSION: ${ROS_VERSION}")
message(STATUS "Target platform: ${TARGET_PLATFORM}")
message(STATUS "lydHostApi library: ${LYD_HOST_API_LIB}")
message(STATUS "libusb library: ${LIBUSB_LIBRARIES}")
message(STATUS "=======================================")
@@ -0,0 +1,201 @@
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@@ -0,0 +1,567 @@
# Odin_ROS_Driver 说明文档
Odin 传感器模块 ROS 驱动套件(Manifold Tech Ltd.
Odin1 Wikihttps://manifoldtechltd.github.io/wiki/Odin1/Cover.html
## Odin_ROS_Driver
兼容性:
● ROS 1(推荐 LTS 版本:Noetic
● ROS 2(推荐 LTS 版本:Humble
## 重要提示:
本驱动包提供点云 SLAM 应用的核心功能,面向特定使用场景。仅供专业技术人员进行二次开发使用。最终用户需根据实际部署环境进行场景优化和定制开发,以满足运行需求。
## 1. 版本
当前版本:v0.10.2
所需设备固件版本:v0.10.0
## 2. 准备工作
### 2.1 操作系统要求
● ROS Noetic 和 ROS2 Foxy 需 Ubuntu 20.04
● ROS2 Humble 需 Ubuntu 22.04
● 当前不支持 Ubuntu 18.04
● Ubuntu 24.04 尚未官方支持,但可能经过一定修改后运行。
### 2.2 依赖项
● OpenCV >= 4.2.0(推荐 4.5.5/4.8.0,请确保仅安装一个 OpenCV 版本)
● yaml-cpp
● thread
● OpenSSL
● Eigen3
### 2.3 依赖安装
#### 2.3.1 系统基础
```shell
sudo apt update
sudo apt-get install build-essential cmake git libgtk2.0-dev pkg-config libavcodec-dev libavformat-dev libswscale-dev
```
#### 2.3.2 yaml-cpp
```shell
sudo apt update
sudo apt install -y libyaml-cpp-dev
```
#### 2.3.3 libusb
```shell
sudo apt update
sudo apt install -y libusb-1.0-0-dev
```
#### 2.3.4 OpenCV
```shell
sudo apt update
sudo apt-get install libopencv-dev
```
#### 2.3.5 ROS 安装
ROS Noetic 安装请参考:
[ROS Noetic 安装指南](https://wiki.ros.org/noetic/Installation)
ROS2 Foxy 安装请参考:
[ROS Foxy 安装指南](https://docs.ros.org/en/foxy/Installation/Ubuntu-Install-Debians.html)
ROS2 Humble 安装请参考:
[ROS Humble 安装指南](https://docs.ros.org/en/humble/Installation/Ubuntu-Install-Debians.html)
## 3. 准备
### 3.1 创建 Udev 规则
```shell
sudo vim /etc/udev/rules.d/99-odin-usb.rules
```
在 99-odin-usb.rules 文件中添加以下内容:
```shell
SUBSYSTEM=="usb", ATTR{idVendor}=="2207", ATTR{idProduct}=="0019", MODE="0666", GROUP="plugdev"
```
重新加载规则并重新插拔设备:
```shell
sudo udevadm control --reload
sudo udevadm trigger
```
### 3.2 克隆源码
```shell
git clone https://github.com/manifoldsdk/odin_ros_driver.git catkin_ws/src/odin_ros_driver
```
注意:
请将源码克隆到 "[ros_workspace]/src/" 目录下,否则会导致编译错误。
### 3.3 编译
#### 3.3.1 ROS1(以 Noetic 为例):
```shell
source /opt/ros/noetic/setup.bash
./script/build_ros.sh
```
#### 3.3.2 ROS2(以 Foxy 为例):
```shell
source /opt/ros/foxy/setup.bash
./script/build_ros2.sh
```
### 3.4 运行:
#### 3.4.1 ROS1(以 Noetic 为例):
```shell
source [ros_workspace]/devel/setup.bash
roslaunch odin_ros_driver [launch file]
```
● odin_ros_driver:包名;
● launch file:启动文件名;
● ros_workspace:用户的 ROS 环境工作区;
```shell
roslaunch odin_ros_driver odin1_ros1.launch
```
#### 3.4.2 ROS2(以 Foxy 为例):
```shell
source [ros2_workspace]/install/setup.bash
ros2 launch odin_ros_driver [launch file]
```
● odin_ros_driver:包名;
● launch file:启动文件名;
● ros2_workspace:用户的 ROS2 环境工作区;
ROS2 Demo 启动命令:
```shell
ros2 launch odin_ros_driver odin1_ros2.launch.py
```
### 3.5 运行模式:
可通过 `config/control_command.yaml` 中的 `custom_map_mode` 参数配置运行模式。
#### 里程计模式
设置 `custom_map_mode = 0` 启用里程计模式。此模式下,map 坐标系与 odom 坐标系共享同一位姿。
若发现里程计数据漂移,可使用脚本命令 `./set_param.sh algo_reset 1` 动态复位算法。
#### SLAM 建图模式
设置 `custom_map_mode = 1` 启用 SLAM 模式。此模式在里程计模式基础上,提供**回环检测**和**地图保存**功能。
启动驱动后,odin1 将自动进行建图并缓存地图数据。场景采集完成后,需在驱动源码目录下执行 `./set_param.sh save_map 1` 以保存自程序启动以来采集的所有地图数据。地图将保存至 `config/control_command.yaml``mapping_result_dest_dir``mapping_result_file_name` 参数指定的路径。若未指定这些参数,将使用默认值。
首次保存后,可再次执行该命令保存新地图。每次保存操作都会生成一个新的地图文件。(连续保存操作之间请间隔至少 5 秒)
地图原点对应程序启动时 odom 坐标系的起点。
##### 重定位模式
要启用重定位,设置 `custom_map_mode = 2`,并通过 `config/control_command.yaml` 中的 `relocalization_map_abs_path` 参数指定预建地图的绝对路径。
启动后,odin1 将基于当前视点和指定地图启动重定位过程。为保障高成功率,建议在距 SLAM 轨迹原始位置 1 米、±10° 范围内启动。
注意,重定位性能高度依赖环境条件。在特征丰富的场景中,成功匹配可能发生在 1m/10° 范围之外,而其他环境可能需要更严格的条件。建议在实际部署环境中测试,以确定实际容忍范围。
若初始重定位失败,系统将临时以降级 SLAM 模式运行(此状态下地图保存功能禁用)。在此过程中可自由移动 odin1,它将在后台持续尝试重定位。一旦成功,将发布 map 与 odom 坐标系之间的 TF。(提示:初始化后轻轻晃动或移动设备有助于提高重定位准确率。)
以下话题在 odom 坐标系下发布:`/odin1/cloud_slam``/odin1/odom``/odin1/highodom``/odin1/path`。若需在 map 坐标系下获取这些数据,请应用从 odom 坐标系到 map 坐标系的 TF 变换。
## 4. 文件结构与数据格式
### 4.1 文件结构
```shell
Odin_ROS_Driver/ // ROS1/ROS2 驱动包
3rdparty/ // 第三方库
src/
host_sdk_sample.cpp // 示例源码
yaml_parser.cpp // YAML 参数读取源码
rawCloudRender.cpp // RenderCloud 渲染源码
depth_image_ros_node.cpp // depth_image_ros_node 节点
depth_image_ros2_node.cpp // depth_image_ros2_node 节点
pcd2depth_ros.cpp // pcd2depth_ros 源码
pcd2depth_ros2.cpp // pcd2depth_ros2 源码
pointcloud_depth_converter.cpp // pointcloud_depth_converter 源码
cloud_reprojection_ros.cpp // 云重投影节点源码 (ROS1/ROS2)
cloud_reprojector.cpp // 云重投影核心逻辑
lib/
liblydHostApi_amd.a // AMD 平台静态库
liblydHostApi_arm.a // ARM 平台静态库
include/
host_sdk_sample.h // 示例头文件
lidar_api_type.h // API 数据结构头文件
lidar_api.h // API 函数声明
yaml_parser.h // 参数文件读取头文件
rawCloudRender.h // RenderCloud 相关 API
data_logger.h // 数据保存日志
depth_image_ros_node.hpp // depth_image_ros_node 头文件
depth_image_ros2_node.hpp // depth_image_ros2_node 头文件
pointcloud_depth_converter.hpp // pointcloud_depth_convert 头文件
cloud_reprojection_ros_node.hpp // cloud_reprojection_ros_node 头文件 (ROS1/ROS2)
cloud_reprojector.hpp // 云重投影核心类
config/
control_command.yaml // 驱动控制参数文件
calib.yaml // 设备标定参数 yaml,每个设备独一无二。每次连接 ROS 驱动时从设备读取
launch_ROS1/
odin1_ros1.launch // ROS1 启动文件
launch_ROS2/
odin1_ros2.launch.py // ROS2 启动文件
script/
build_ros1.sh // ROS1 安装脚本
build_ros2.sh // ROS2 安装脚本
recorddata/ // 存放可导入 MindCloud 的录制数据
log/ // 存放日志文件
Driver_{timestamp}/ // 每次启动驱动时生成的日志文件夹
Conn_{timestamp}/ // 每次 odin1 设备连接时生成的日志文件
dev_status.csv // 设备状态日志
README.md // 使用说明
CMakeLists.txt // CMake 构建文件
License // 许可证文件
```
### 4.2 启动文件
| 启动文件名 | 说明 |
|--------------------------|-------------|
| odin1_ros1.launch | ROS1 启动文件 - Odin1 基础操作演示 |
| odin1_ros2.launch.py | ROS2 启动文件 - Odin1 基础操作演示 |
### 4.3 ROS 话题
Odin ROS 驱动的内部参数定义在 config/control_command.yaml 中。以下是常用参数说明:
| 话题 | control_command.yaml | 详细说明 |
|---------------------------|----------------------|----------------------|
| odin1/imu | sendimu | IMU 话题 |
| odin1/image | sendrgb | RGB 相机话题,由设备原始 JPEG 数据解码,bgr8 格式 |
| odin1/image_undistort | sendrgbundistort | 去畸变 RGB 相机话题,经设备 calib.yaml 标定参数处理 |
| odin1/image/compressed | sendrgbcompressed | RGB 相机压缩话题,设备原始 JPEG 数据 |
| odin1/cloud_raw | senddtof | 原始点云话题 |
| odin1/cloud_render | sendcloudrender | 渲染点云话题,经原始点云、RGB 图像及设备 calib.yaml 处理 |
| odin1/cloud_slam | sendcloudslam | SLAM 点云话题 |
| odin1/odometry | sendodom | 里程计话题 |
| odin1/odometry_high | sendodom | 高频里程计话题 |
| odin1/path | showpath | 里程计路径话题 |
| tf | sendodom | TF 树话题 |
| odin1/depth_img_competetion | senddepth | 稠密深度图话题。需较高算力,仅作演示。与 odin1/image_undistort 一一对应。使用时请直接订阅本话题而非 echo。原始值即为深度数据,无需额外转换。 |
| odin1/depth_img_competetion_cloud | senddepth | 稠密深度点云话题。需较高算力,仅作演示 |
| odin1/reprojected_image | sendreprojection | 重投影像素话题。利用里程计将 cloud_slam 投影至相机图像。在主机端处理。 |
### 4.4 数据格式
1. 原始点云(cloud_raw)包含以下字段:
```
float32 x // X 轴,单位:米
float32 y // Y 轴,单位:米
float32 z // Z 轴,单位:米
uint8 intensity // 反射率,范围 0255
uint16 confidence // 点置信度,典型场景下取值范围约 0–1300,数值越高可靠性越强。推荐过滤阈值 30-35,应结合实际环境调整。
float32 offset_time // 相对基准时间戳的时间偏移量,单位:秒
```
要在 PCL 中使用此自定义格式,首先定义点类型:
```cpp
/*** LS ***/
namespace ls_ros {
struct EIGEN_ALIGN16 Point {
float x;
float y;
float z;
uint8_t intensity;
uint16_t confidence;
float offset_time;
EIGEN_MAKE_ALIGNED_OPERATOR_NEW
};
} // namespace ls_ros
POINT_CLOUD_REGISTER_POINT_STRUCT(ls_ros::Point,
(float, x, x)
(float, y, y)
(float, z, z)
(uint8_t, intensity, intensity)
(uint16_t, confidence, confidence)
(float offset_time , offset_time)
)
```
然后即可轻松将 ROS sensor_msgs::PointCloud2 消息转换为 PCL 点云:
```
pcl::PointCloud<ls_ros::Point> ls_cloud;
pcl::fromROSMsg(*msg, ls_cloud);
```
2. SLAM 点云(cloud_slam)与直接渲染点云(cloud_render)包含以下字段:
```
float32 x // X 轴,单位:米
float32 y // Y 轴,单位:米
float32 z // Z 轴,单位:米
float32 rgb // RGB 颜色值
```
### 4.5 其他功能
| control_command.yaml 参数 | 详细说明 |
|----------------------------|----------------------|
| use_host_ros_time | 时间同步模式:0 - 使用 odin 内部系统时间作为数据时间戳(典型用法,推荐);1 - 接收时使用主机 ROS 时间(不推荐大多数用户使用);2 - 通过类 NTP 同步将 odin1 时间对齐至主机时间,时间戳为传感器数据在主机时间轴上的接收时间。 |
| strict_usb3.0_check | 严格 USB3.0 检查,关闭后即使 USB 连接低于 3.0 标准也允许连接 |
| recorddata | 以特定格式记录数据,可导入 MindCloud(TM) 进行后处理。请注意这将消耗大量存储空间,测试显示 10 分钟数据约占 9.5GB。 |
| devstatuslog | 设备状态日志记录,当前将设备状态(SoC 温度、CPU 占用率、RAM 占用率、dToF 传感器温度等)及数据发送/接收速率保存至 log 目录下的 devstatus.csv。每次启动驱动时创建新文件。 |
| showcamerapose | 显示相机位姿及视野范围。 |
| custom_map_mode | 运行模式:模式 0 - 里程计模式:map 坐标系与 odom 坐标系共享同一位姿。模式 1 - 建图模式(带回环检测):该模式支持地图保存。模式 2 - 重定位模式:需指定地图文件绝对路径,重定位成功后将输出 map 与 odom 坐标系之间的 TF 关系。|
| custom_init_pos | 初始化位置(当前未启用)。 |
| relocalization_map_abs_path | 地图文件绝对路径:用于重定位模式。 |
| mapping_result_dest_dir 和 mapping_result_file_name | 建图模式下地图保存路径与文件名:若未指定,将使用默认值。 |
## 5. 常见问题
### 5.1 重新启动宿主 SDK 时出现段错误
**错误信息**
60 秒内未连接任何设备
**解决方案**
1. 请重新为 Odin 模块上电 # 断开并重新连接 odin 电源
2. 重新初始化 Odin SDK # 设备重启后执行 SDK
### 5.2 编译时出现库链接失败
**错误信息**
ld: cannot find -llydHostApi 或符号查找错误
**解决方案**
1. 清理之前的构建产物
ROS1
```shell
rm -rf devel/ build/
```
ROS2
```shell
rm -rf devel/ install/ log/
```
2. 重新运行脚本安装
### 5.3 Docker GUI 透传失败
**错误信息**
Unable to open X display 或 No protocol specified
**解决方案**
```shell
xhost + # 此命令启用 Docker 容器的图形透传
```
### 5.4 ROS 驱动以"获取版本失败"错误退出
**错误信息**
```shell
<ERROR><api.cpp:lidar_get_version:672>: get device version fail.
get version failed.
```
**解决方案**
设备固件版本过低,请升级至最新版本。
### 5.5 RVIZ 长时间无响应
**错误信息**
Rviz 无响应,稍后终端打印"Device disconnected, waiting for reconnection..."
**解决方案**
请重新为 Odin 模块上电
### 5.6 设备无响应
**错误信息**
Missed ok response from device, probably wrong interaction procedure.
**解决方案**
请采用 5.1 所述的解决方案
### 5.7 设备无外部标定文件
**错误信息**
ERRORMissing camera node 'cam_0'
**解决方案**
请重新插拔 USB
### 5.8 ROS 驱动在数据流启动后立即报设备断开
**错误信息**
```shell
Device ready and streams activated
Device detaching...
Wating for device reconnection...
Device disconnected, waiting for reconnection...
```
**原因**
多见于 ROS2 环境且连接到复杂网络环境(如办公 WiFi 和以太网)的情况。ROS2 默认为广播模式,复杂网络环境可能导致 ROS2 发布阻塞,从而引发设备断开。
**解决方案**
若不需要跨设备通信,请将 ROS2 限制为仅本地通信:
```shell
export ROS_LOCALHOST_ONLY=1
```
若需要跨设备通信,请尽量简化网络环境。建议使用仅包含必要设备的小型局域网。
### 5.9 ROS 驱动在数据流启动后立即崩溃
**错误信息**
```shell
Device ready and streams activated
[host_sdk_sample-2] process has died ......
```
**测试**
在 control_command.yaml 中设置 sendrgb = 0 禁用 odin1/image,然后重试。若驱动此时正常工作,则问题很可能与系统安装了多个 OpenCV 版本有关。
**解决方案**
卸载多余的 OpenCV 版本,仅保留单一完整版本,然后重新编译驱动并重试。
### 5.10 ROS 驱动打印"TF_OLD_DATA ignoring data"警告
**错误信息**
```shell
[rviz2-3] Warning: TF_OLD_DATA ignoring data from the past for frame odin1_base_link at time 20.547632 according to authority Authority undetectable
[rviz2-3] Possible reasons are listed at http://wiki.ros.org/tf/Errors%20explained
[rviz2-3] at line 294 in ./src/buffer_core.cpp
```
**原因**
这是 ROS 和 rviz 的一项功能,用于警告用户某些 TF 数据因时间戳冲突而被忽略。常见于用户保持 ROS 驱动运行的同时对 odin 设备断电重启,导致 odin 内部系统时间被重置,新数据时间戳与 rviz 上次运行期间接收的旧数据产生冲突。
**解决方案**
rviz GUI 底部有一个重置按钮。点击此按钮将重置 rviz 内部状态并停止警告。
### 5.11 ROS 驱动打印"unknown cmd code: xx"错误
**错误信息**
```shell
<ERROR><api.cpp:cmd_data_deal:418>: unknow command code 21.
```
**原因**
这是由于 ROS 驱动版本与设备固件版本不匹配,导致 ROS 驱动无法解码新版固件新增的数据。
**解决方案**
请确保使用最新版本的 ROS 驱动和设备固件。
### 5.12 USB 设备访问错误(LIBUSB_ERROR_BUSY 或 LIBUSB_ERROR_ACCESS
**错误信息**
```shell
libusb: error [udev_hotplug_event] ignoring udev action bind
LIBUSB_ERROR_BUSY
```
```shell
libusb: error [_get_usbfs_fd] libusb couldn't open USB device /dev/bus/usb/xxx/xxx, errno=13
LIBUSB_ERROR_ACCESS
```
**原因**
- **LIBUSB_ERROR_BUSY**:另一个进程正在使用该 USB 设备。常见于多个 ROS 驱动实例正在运行,或其他应用程序(如之前崩溃的实例)仍持有设备句柄。
- **LIBUSB_ERROR_ACCESS**:当前用户无权访问 USB 设备。通常因缺少 udev 规则或用户权限不足导致。
**解决方案**
针对 **LIBUSB_ERROR_BUSY**
1. 检查是否有其他驱动实例正在运行:
```shell
ps aux | grep host_sdk_sample
```
2. 终止所有现存实例:
```shell
killall host_sdk_sample
```
3. 若问题仍然存在,请拔插 USB 设备以重置设备状态。
针对 **LIBUSB_ERROR_ACCESS**
1. 添加设备 udev 规则。创建文件 `/etc/udev/rules.d/99-odin.rules`,内容如下:
```shell
SUBSYSTEM=="usb", ATTR{idVendor}=="2207", ATTR{idProduct}=="0019", MODE="0666", GROUP="plugdev"
```
2. 重新加载 udev 规则:
```shell
sudo udevadm control --reload-rules
sudo udevadm trigger
```
3. 或者,使用 sudo 运行驱动(不推荐用于生产环境):
```shell
sudo -E ros2 launch odin_ros_driver odin_ros_driver.launch.py
```
4. 确保当前用户属于 `plugdev` 用户组:
```shell
sudo usermod -aG plugdev $USER
```
然后注销并重新登录,使组变更生效。
## 6. 联系方式
您可通过 support@manifoldtech.cn 联系我们的技术支持。
为帮助诊断问题,请向我们的 FAE 工程师提供以下信息:
1. 当前固件版本
```shell
[device_version_capture]: ros_driver_version: [版本号]
```
2. 正在使用的电源适配器和转换线缆照片。
3. 问题是偶发性还是持续性的?
4. 提供问题场景的图像。
5. **第 V 节**中的故障排除方法是否解决了问题?
6. 问题解决的预期时间线。
@@ -0,0 +1,642 @@
# Relocalization Guide / 重定位使用指南
This guide explains how to use the relocalization feature in Odin ROS Driver, including automatic relocalization and init position relocalization modes.
本指南介绍如何使用 Odin ROS Driver 的重定位功能,包括自动重定位和指定初始位置重定位两种模式。
---
## Table of Contents / 目录
1. [Overview / 概述](#overview--概述)
2. [Prerequisites / 前提条件](#prerequisites--前提条件)
3. [Mode 1: Auto Relocalization / 自动重定位](#mode-1-auto-relocalization--自动重定位)
4. [Mode 2: Init Position Relocalization / 指定初始位置重定位](#mode-2-init-position-relocalization--指定初始位置重定位)
5. [init_pos Format / init_pos 格式说明](#init_pos-format--init_pos-格式说明)
6. [Configuration Examples / 配置示例](#configuration-examples--配置示例)
7. [Programmatic API / 编程接口](#programmatic-api--编程接口)
8. [Troubleshooting / 故障排除](#troubleshooting--故障排除)
---
## Overview / 概述
### English
Relocalization mode (`custom_map_mode: 2`) allows Odin to localize itself within a pre-built map. There are two approaches:
| Mode | Description | Use Case |
|------|-------------|----------|
| **Auto Relocalization** | Algorithm automatically searches for position in the map | Starting position is unknown or within recommended range |
| **Init Position Relocalization** | User provides an initial pose estimate via `init_pos` | Starting position is known, faster convergence needed |
### 中文
重定位模式(`custom_map_mode: 2`)允许 Odin 在预先构建的地图中进行自我定位。有两种方式:
| 模式 | 描述 | 适用场景 |
|------|------|----------|
| **自动重定位** | 算法自动在地图中搜索位置 | 起始位置未知,或在推荐范围内 |
| **指定初始位置重定位** | 用户通过 `init_pos` 提供初始位姿估计 | 起始位置已知,需要更快收敛 |
---
## Prerequisites / 前提条件
### English
1. **Pre-built map file**: A `.bin` map file created in SLAM mode (`custom_map_mode: 1`)
2. **Map file path**: Know the absolute path to your map file
3. **Starting position**: For init position mode, know the approximate starting pose in map coordinates
### 中文
1. **预构建的地图文件**:在 SLAM 模式(`custom_map_mode: 1`)下创建的 `.bin` 地图文件
2. **地图文件路径**:知道地图文件的绝对路径
3. **起始位置**:对于指定初始位置模式,需要知道在地图坐标系中的大致起始位姿
---
## Mode 1: Auto Relocalization / 自动重定位
### English
In auto relocalization mode, the algorithm automatically searches for the device's position within the map based on current sensor observations.
**Configuration** (`config/control_command.yaml`):
```yaml
register_keys:
custom_map_mode: 2
relocalization_map_abs_path: "/path/to/your/map.bin"
# custom_init_pos is NOT set or uses default [0,0,0,0,0,0,1]
```
**Recommended Starting Conditions**:
- Within **1 meter** of a position on the original SLAM trajectory
- Within **±10 degrees** of the original orientation
- In a visually distinctive area of the map
**Behavior**:
1. On startup, Odin attempts to match current observations with the map
2. If successful, TF between `map` and `odom` frames is published
3. If unsuccessful, system operates in fallback SLAM mode (map saving disabled)
4. Relocalization attempts continue in background until successful
**Tips**:
- Gently shaking or moving the device after startup can improve relocalization accuracy
- Highly distinctive scenes may allow successful matching beyond the 1m/10° range
### 中文
在自动重定位模式下,算法根据当前传感器观测自动在地图中搜索设备位置。
**配置** (`config/control_command.yaml`):
```yaml
register_keys:
custom_map_mode: 2
relocalization_map_abs_path: "/path/to/your/map.bin"
# custom_init_pos 不设置或使用默认值 [0,0,0,0,0,0,1]
```
**推荐起始条件**
- 距离原始 SLAM 轨迹上某点 **1 米**以内
- 朝向与原始方向偏差在 **±10 度**以内
- 位于地图中视觉特征明显的区域
**行为**
1. 启动时,Odin 尝试将当前观测与地图匹配
2. 如果成功,发布 `map``odom` 坐标系之间的 TF
3. 如果失败,系统进入后备 SLAM 模式(地图保存功能禁用)
4. 后台持续尝试重定位直到成功
**提示**
- 启动后轻轻晃动或移动设备可以提高重定位精度
- 在特征明显的场景中,可能在超出 1m/10° 范围时也能成功匹配
---
## Mode 2: Init Position Relocalization / 指定初始位置重定位
### English
In init position relocalization mode, you provide an initial pose estimate to help the algorithm converge faster.
**Configuration** (`config/control_command.yaml`):
```yaml
register_keys:
custom_map_mode: 2
relocalization_map_abs_path: "/path/to/your/map.bin"
custom_init_pos: [x, y, z, qx, qy, qz, qw]
```
**When to Use**:
- You know the approximate starting position (e.g., from external localization system)
- Starting position is far from the recommended 1m/10° range
- You need faster relocalization convergence
- Deploying in a fixed docking station with known pose
**Behavior**:
1. Algorithm uses provided `init_pos` as initial pose estimate
2. Searches for matches in the vicinity of the provided position
3. Faster convergence compared to auto mode when estimate is accurate
### 中文
在指定初始位置重定位模式下,您提供初始位姿估计以帮助算法更快收敛。
**配置** (`config/control_command.yaml`):
```yaml
register_keys:
custom_map_mode: 2
relocalization_map_abs_path: "/path/to/your/map.bin"
custom_init_pos: [x, y, z, qx, qy, qz, qw]
```
**适用场景**
- 您知道大致的起始位置(例如,来自外部定位系统)
- 起始位置远离推荐的 1m/10° 范围
- 需要更快的重定位收敛速度
- 部署在已知位姿的固定充电桩
**行为**
1. 算法使用提供的 `init_pos` 作为初始位姿估计
2. 在提供位置的附近搜索匹配
3. 当估计准确时,比自动模式收敛更快
---
## init_pos Format / init_pos 格式说明
### English
`init_pos` is an array of **7 float values** representing position and orientation:
```yaml
custom_init_pos: [x, y, z, qx, qy, qz, qw]
```
| Index | Parameter | Description | Unit |
|-------|-----------|-------------|------|
| 0 | x | X position in map frame | meters |
| 1 | y | Y position in map frame | meters |
| 2 | z | Z position in map frame | meters |
| 3 | qx | Quaternion X component | - |
| 4 | qy | Quaternion Y component | - |
| 5 | qz | Quaternion Z component | - |
| 6 | qw | Quaternion W component | - |
**Important Notes**:
- The quaternion must be normalized: `sqrt(qx² + qy² + qz² + qw²) ≈ 1.0`
- Coordinates are relative to the **map frame** (world frame at SLAM start)
- Default value `[0, 0, 0, 0, 0, 0, 1]` represents origin with no rotation
**Common Quaternion Values**:
| Orientation | qx | qy | qz | qw |
|-------------|----|----|----|----|
| No rotation (identity) | 0 | 0 | 0 | 1 |
| 90° around Z-axis | 0 | 0 | 0.707 | 0.707 |
| 180° around Z-axis | 0 | 0 | 1 | 0 |
| -90° around Z-axis | 0 | 0 | -0.707 | 0.707 |
### 中文
`init_pos` 是一个包含 **7 个 float 值**的数组,表示位置和朝向:
```yaml
custom_init_pos: [x, y, z, qx, qy, qz, qw]
```
| 索引 | 参数 | 描述 | 单位 |
|------|------|------|------|
| 0 | x | 地图坐标系中的 X 位置 | 米 |
| 1 | y | 地图坐标系中的 Y 位置 | 米 |
| 2 | z | 地图坐标系中的 Z 位置 | 米 |
| 3 | qx | 四元数 X 分量 | - |
| 4 | qy | 四元数 Y 分量 | - |
| 5 | qz | 四元数 Z 分量 | - |
| 6 | qw | 四元数 W 分量 | - |
**重要说明**
- 四元数必须归一化:`sqrt(qx² + qy² + qz² + qw²) ≈ 1.0`
- 坐标相对于**地图坐标系**(SLAM 启动时的世界坐标系)
- 默认值 `[0, 0, 0, 0, 0, 0, 1]` 表示原点且无旋转
**常用四元数值**
| 朝向 | qx | qy | qz | qw |
|------|----|----|----|----|
| 无旋转(单位四元数) | 0 | 0 | 0 | 1 |
| 绕 Z 轴旋转 90° | 0 | 0 | 0.707 | 0.707 |
| 绕 Z 轴旋转 180° | 0 | 0 | 1 | 0 |
| 绕 Z 轴旋转 -90° | 0 | 0 | -0.707 | 0.707 |
---
## Configuration Examples / 配置示例
### Example 1: Auto Relocalization / 自动重定位示例
```yaml
register_keys:
custom_map_mode: 2
relocalization_map_abs_path: "/home/user/maps/office_map.bin"
```
### Example 2: Init Position at Origin / 在原点指定初始位置
```yaml
register_keys:
custom_map_mode: 2
relocalization_map_abs_path: "/home/user/maps/office_map.bin"
custom_init_pos: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0]
```
### Example 3: Init Position with Offset / 带偏移的初始位置
Position at (5.2, -3.1, 0) with 90° rotation around Z-axis:
位置在 (5.2, -3.1, 0),绕 Z 轴旋转 90°:
```yaml
register_keys:
custom_map_mode: 2
relocalization_map_abs_path: "/home/user/maps/warehouse_map.bin"
custom_init_pos: [5.2, -3.1, 0.0, 0.0, 0.0, 0.707, 0.707]
```
### Example 4: Docking Station Pose / 充电桩位置
Known docking station at (10.5, 2.3, 0) facing -X direction (180° rotation):
已知充电桩位置在 (10.5, 2.3, 0),朝向 -X 方向(旋转 180°):
```yaml
register_keys:
custom_map_mode: 2
relocalization_map_abs_path: "/home/user/maps/factory_map.bin"
custom_init_pos: [10.5, 2.3, 0.0, 0.0, 0.0, 1.0, 0.0]
```
---
## Programmatic API / 编程接口
### English
You can also set `init_pos` programmatically using the `lidar_set_custom_parameter` API. This is useful for:
- Dynamic relocalization during runtime
- Integration with external localization systems
- Setting initial pose from robot's last known position
#### API Function
```cpp
#include "lidar_api.h"
/**
* @brief Set a custom parameter on the device
* @param device Device handle obtained from lidar_open_device()
* @param param_name Parameter name (e.g., "init_pos")
* @param value_data Pointer to the parameter data
* @param value_length Size of the data in bytes
* @return 0 on success, -1 on error, -2 if file transfer in progress
*/
int lidar_set_custom_parameter(device_handle device,
const char* param_name,
const void* value_data,
size_t value_length);
```
#### Complete Example
```cpp
#include "lidar_api.h"
#include <cstdio>
#include <cmath>
// Helper function to create quaternion from yaw angle (rotation around Z-axis)
void yaw_to_quaternion(float yaw_rad, float* qx, float* qy, float* qz, float* qw) {
*qx = 0.0f;
*qy = 0.0f;
*qz = sinf(yaw_rad / 2.0f);
*qw = cosf(yaw_rad / 2.0f);
}
int set_init_position(device_handle device,
float x, float y, float z,
float qx, float qy, float qz, float qw) {
// init_pos format: [x, y, z, qx, qy, qz, qw] - 7 floats
float init_pos[7] = {x, y, z, qx, qy, qz, qw};
int result = lidar_set_custom_parameter(
device,
"init_pos", // Parameter name
init_pos, // Data pointer
sizeof(init_pos) // 7 * sizeof(float) = 28 bytes
);
if (result == 0) {
printf("Successfully set init_pos: [%.3f, %.3f, %.3f, %.3f, %.3f, %.3f, %.3f]\n",
x, y, z, qx, qy, qz, qw);
} else {
printf("Failed to set init_pos, error code: %d\n", result);
}
return result;
}
// Usage examples:
// Example 1: Set position at origin with no rotation
void example_origin(device_handle device) {
set_init_position(device,
0.0f, 0.0f, 0.0f, // x, y, z
0.0f, 0.0f, 0.0f, 1.0f // qx, qy, qz, qw (identity)
);
}
// Example 2: Set position with 90° yaw rotation
void example_with_rotation(device_handle device) {
float qx, qy, qz, qw;
float yaw_degrees = 90.0f;
float yaw_rad = yaw_degrees * M_PI / 180.0f;
yaw_to_quaternion(yaw_rad, &qx, &qy, &qz, &qw);
set_init_position(device,
5.2f, -3.1f, 0.0f, // x, y, z
qx, qy, qz, qw // quaternion from yaw
);
}
// Example 3: Set position from external localization system
void example_from_external_localization(device_handle device,
double ext_x, double ext_y, double ext_yaw) {
float qx, qy, qz, qw;
yaw_to_quaternion((float)ext_yaw, &qx, &qy, &qz, &qw);
set_init_position(device,
(float)ext_x, (float)ext_y, 0.0f,
qx, qy, qz, qw
);
}
```
#### ROS Integration Example
```cpp
#include "lidar_api.h"
#include <geometry_msgs/PoseWithCovarianceStamped.h> // ROS1
// or
#include <geometry_msgs/msg/pose_with_covariance_stamped.hpp> // ROS2
// Callback for /initialpose topic (from RViz "2D Pose Estimate" tool)
void initialPoseCallback(const geometry_msgs::PoseWithCovarianceStamped::ConstPtr& msg,
device_handle device) {
float init_pos[7] = {
(float)msg->pose.pose.position.x,
(float)msg->pose.pose.position.y,
(float)msg->pose.pose.position.z,
(float)msg->pose.pose.orientation.x,
(float)msg->pose.pose.orientation.y,
(float)msg->pose.pose.orientation.z,
(float)msg->pose.pose.orientation.w
};
int result = lidar_set_custom_parameter(device, "init_pos", init_pos, sizeof(init_pos));
if (result == 0) {
ROS_INFO("Set init_pos from RViz: [%.2f, %.2f, %.2f]",
init_pos[0], init_pos[1], init_pos[2]);
} else {
ROS_ERROR("Failed to set init_pos: %d", result);
}
}
```
#### Important Notes
1. **Call timing**: Set `init_pos` **before** starting the stream with `lidar_start_stream()`
2. **Map mode**: Ensure `custom_map_mode` is set to `2` (relocalization mode)
3. **Map file**: The relocalization map must be set via `lidar_set_relocalization_map()` or YAML config
4. **Thread safety**: `lidar_set_custom_parameter` is thread-safe but blocks until response received
### 中文
您也可以使用 `lidar_set_custom_parameter` API 以编程方式设置 `init_pos`。适用于:
- 运行时动态重定位
- 与外部定位系统集成
- 从机器人上次已知位置设置初始位姿
#### API 函数
```cpp
#include "lidar_api.h"
/**
* @brief 在设备上设置自定义参数
* @param device 从 lidar_open_device() 获取的设备句柄
* @param param_name 参数名称(如 "init_pos"
* @param value_data 指向参数数据的指针
* @param value_length 数据大小(字节)
* @return 成功返回 0,错误返回 -1,文件传输中返回 -2
*/
int lidar_set_custom_parameter(device_handle device,
const char* param_name,
const void* value_data,
size_t value_length);
```
#### 完整示例
```cpp
#include "lidar_api.h"
#include <cstdio>
#include <cmath>
// 辅助函数:从偏航角(绕 Z 轴旋转)创建四元数
void yaw_to_quaternion(float yaw_rad, float* qx, float* qy, float* qz, float* qw) {
*qx = 0.0f;
*qy = 0.0f;
*qz = sinf(yaw_rad / 2.0f);
*qw = cosf(yaw_rad / 2.0f);
}
int set_init_position(device_handle device,
float x, float y, float z,
float qx, float qy, float qz, float qw) {
// init_pos 格式: [x, y, z, qx, qy, qz, qw] - 7 个 float
float init_pos[7] = {x, y, z, qx, qy, qz, qw};
int result = lidar_set_custom_parameter(
device,
"init_pos", // 参数名
init_pos, // 数据指针
sizeof(init_pos) // 7 * sizeof(float) = 28 字节
);
if (result == 0) {
printf("成功设置 init_pos: [%.3f, %.3f, %.3f, %.3f, %.3f, %.3f, %.3f]\n",
x, y, z, qx, qy, qz, qw);
} else {
printf("设置 init_pos 失败,错误码: %d\n", result);
}
return result;
}
// 使用示例:
// 示例 1:在原点设置位置,无旋转
void example_origin(device_handle device) {
set_init_position(device,
0.0f, 0.0f, 0.0f, // x, y, z
0.0f, 0.0f, 0.0f, 1.0f // qx, qy, qz, qw(单位四元数)
);
}
// 示例 2:设置带 90° 偏航旋转的位置
void example_with_rotation(device_handle device) {
float qx, qy, qz, qw;
float yaw_degrees = 90.0f;
float yaw_rad = yaw_degrees * M_PI / 180.0f;
yaw_to_quaternion(yaw_rad, &qx, &qy, &qz, &qw);
set_init_position(device,
5.2f, -3.1f, 0.0f, // x, y, z
qx, qy, qz, qw // 从偏航角计算的四元数
);
}
// 示例 3:从外部定位系统设置位置
void example_from_external_localization(device_handle device,
double ext_x, double ext_y, double ext_yaw) {
float qx, qy, qz, qw;
yaw_to_quaternion((float)ext_yaw, &qx, &qy, &qz, &qw);
set_init_position(device,
(float)ext_x, (float)ext_y, 0.0f,
qx, qy, qz, qw
);
}
```
#### ROS 集成示例
```cpp
#include "lidar_api.h"
#include <geometry_msgs/PoseWithCovarianceStamped.h> // ROS1
// 或
#include <geometry_msgs/msg/pose_with_covariance_stamped.hpp> // ROS2
// /initialpose 话题的回调函数(来自 RViz 的 "2D Pose Estimate" 工具)
void initialPoseCallback(const geometry_msgs::PoseWithCovarianceStamped::ConstPtr& msg,
device_handle device) {
float init_pos[7] = {
(float)msg->pose.pose.position.x,
(float)msg->pose.pose.position.y,
(float)msg->pose.pose.position.z,
(float)msg->pose.pose.orientation.x,
(float)msg->pose.pose.orientation.y,
(float)msg->pose.pose.orientation.z,
(float)msg->pose.pose.orientation.w
};
int result = lidar_set_custom_parameter(device, "init_pos", init_pos, sizeof(init_pos));
if (result == 0) {
ROS_INFO("从 RViz 设置 init_pos: [%.2f, %.2f, %.2f]",
init_pos[0], init_pos[1], init_pos[2]);
} else {
ROS_ERROR("设置 init_pos 失败: %d", result);
}
}
```
#### 重要说明
1. **调用时机**:在调用 `lidar_start_stream()` 启动数据流**之前**设置 `init_pos`
2. **地图模式**:确保 `custom_map_mode` 设置为 `2`(重定位模式)
3. **地图文件**:必须通过 `lidar_set_relocalization_map()` 或 YAML 配置设置重定位地图
4. **线程安全**`lidar_set_custom_parameter` 是线程安全的,但会阻塞直到收到响应
---
## Troubleshooting / 故障排除
### Relocalization Fails / 重定位失败
**English**:
- Ensure starting position is within recommended range (1m/10°)
- Check that the map file path is correct and file exists
- Verify the environment hasn't changed significantly since mapping
- Try gently moving the device to provide more observations
**中文**
- 确保起始位置在推荐范围内(1m/10°)
- 检查地图文件路径是否正确且文件存在
- 验证环境自建图以来没有显著变化
- 尝试轻轻移动设备以提供更多观测
### init_pos Not Taking Effect / init_pos 未生效
**English**:
- Verify `custom_map_mode` is set to `2`
- Check that `custom_init_pos` has exactly 7 values
- Ensure quaternion is normalized (sum of squares ≈ 1)
- Restart the driver after modifying configuration
**中文**
- 验证 `custom_map_mode` 设置为 `2`
- 检查 `custom_init_pos` 是否恰好有 7 个值
- 确保四元数已归一化(平方和 ≈ 1)
- 修改配置后重启驱动程序
### TF Not Published / TF 未发布
**English**:
- Relocalization may still be in progress
- Check ROS logs for relocalization status messages
- System operates in fallback mode until relocalization succeeds
**中文**
- 重定位可能仍在进行中
- 检查 ROS 日志中的重定位状态消息
- 系统在重定位成功前以后备模式运行
### Map File Not Found / 地图文件未找到
**English**:
- Use absolute path (starting with `/`)
- Check file permissions
- Verify file extension is `.bin`
**中文**
- 使用绝对路径(以 `/` 开头)
- 检查文件权限
- 验证文件扩展名为 `.bin`
---
## Related Topics / 相关话题
| Topic | Description |
|-------|-------------|
| `/odin1/odometry` | Odometry in odom frame |
| `/odin1/odometry_highfreq` | High-frequency odometry |
| `/odin1/cloud_slam` | SLAM point cloud in odom frame |
| `/tf` | Transform tree (includes map→odom after successful relocalization) |
---
## See Also / 参见
- [README.md](README.md) - Main documentation
- [config/control_command.yaml](config/control_command.yaml) - Configuration file
@@ -0,0 +1,30 @@
# O1-P040100042
cam_num: 1
img_topic_0: /camera/rgb
Tcl_0: [
-0.00916, -0.99996, 0.00032, 0.03215,
0.00239, -0.00034, -1.00000, -0.01185,
0.99996, -0.00916, 0.00239, 0.00536,
0.00000, 0.00000, 0.00000, 1.00000
]
cam_0:
cam_model: FishPoly
image_width: 1600
image_height: 1296
k2: 6.5597407531742908e-04
k3: -2.8960653254664001e-02
k4: 4.5390330144604515e-02
k5: -6.4513398102889072e-02
k6: 3.8735200610848508e-02
k7: -9.9027101865577991e-03
p1: 0.
p2: 0.
A11: 7.3689426240691989e+02
A12: -1.6114977429994648e-01
A22: 7.3661135398452438e+02
u0: 8.0612553451014685e+02
v0: 6.3965071026991302e+02
isFast: 0
numDiff: 3000
maxIncidentAngle: 120
@@ -0,0 +1,106 @@
register_keys:
# if off, allow connection even if usb connection is below usb 3.0
# ATTENTION: usb 3.0 is always recommended, as advance functionality like SLAM mode requires usb 3.0 for reliable map file transfer
strict_usb3.0_check: 0 # 0: off: 1: on;
# 0: use odin internal system time as data time stamp, typical and recommended;
# 1: use host ros time (upon receive) as data time stamp, only use if you specifically require this setup, not recommended for most users
# 2: align odin1 time to host time, timestamp is the sensor data reception time on host time axis
use_host_ros_time: 1
streamctrl: 1 # 0: off; 1: on
# original rgb data in jpeg format from device
sendrgbcompressed: 1 # 0: off; 1: on
# RGB data, decoded from original jpeg data from device, bgr8 format
# Processed on host device
sendrgb: 1 # 0: off; 1: on
# undistort rgb image processed from decoded rgb data.
# depends on sendrgb. related camera parameters can be found in ws/src/odin_ros_driver/config/calib.yaml
# Processed on host device
sendrgbundistort: 0 # 0: off; 1: on.
# IMU data
sendimu: 1 # 0: off; 1: on
# SDK IMU smooth sending feature
# When enabled, SDK will send IMU data at precise intervals (default 400Hz)
# using a dedicated high-priority thread to reduce jitter and timing variance
enable_imu_smooth: 1 # 0: disable SDK IMU smooth sending; 1: enable (default)
# SDK IMU smooth sending frequency in Hz (only effective when enable_imu_smooth = 1)
imu_smooth_frequency: 400 # 1-1000 Hz, recommended 400 Hz
# Odometry data
sendodom: 1 # 0: off; 1: on
# TF from odom to base_link. Leave it on unless you specifically need it off.
# ATTENTION: critical for rviz to show cloud_raw.
send_odom_baselink_tf: 0 # 0: off; 1: on.
# raw dtof data
senddtof: 1 # 0: off; 1: on
cloud_raw_confidence_threshold: 35 # please refer to readme for more details
# dtof sensor frame rate. Supported values: 100 (10fps) or 145 (14.5fps)
# Higher frame rate provides smoother point cloud data but may increase data bandwidth
# Note: value is multiplied by 10 (e.g., 145 means 14.5fps)
dtof_fps: 100 # 100: 10fps; 145: 14.5fps
# slam cloud data
sendcloudslam: 1 # 0: off; 1: on
# processed with raw point cloud, rgb image, and calib.yaml from device
# Processed on host device
sendcloudrender: 1 # 0: off; 1: on
# depth completion demo, high computing resource usage.
# for more information please refer to the readme file.
# Processed on host device
senddepth: 0 # 0: off; 1: on
# cloud reprojection demo, projects cloud_slam to camera image using odometry
# Processed on host device
sendreprojection: 0 # 0: off; 1: on
# image overlay settings - overlays reprojected points on camera image
# Processed on host device
sendoverlay: 0 # 0: off; 1: on
overlay_reprojected_topic: "/odin1/reprojected_image" # reprojected image topic
overlay_camera_topic: "/odin1/image/undistorted" # camera image topic (undistorted)
overlay_output_topic: "/odin1/overlay_image" # output overlay image topic
overlay_alpha: 0.6 # blend alpha (0.0-1.0, higher = more reproj color)
# record rgb, odometry, and slam cloud data as proprietary olx format for further processing in MindCloud(TM) software.
# save path: ws/src/odin_ros_driver/recorddata/{record_start_time}/
# ATTENTION: please copy the full folder for post-processing.
recorddata: 0 # 0: off; 1: on
# Save device runtime status info to ws/src/odin_ros_driver/log/Driver_{drvier_start_time}/Conn_{device_connection_time}/dev_status.csv
devstatuslog: 1 # 0: off; 1: on.
save_log: 0 # 0: off; 1: on;
# raw dtof sensor intensity data in gray format, mostly for debug purpose.
pubintensitygray: 0 # 0: off; 1: on
showpath: 0 # 0: off; 1: on
showcamerapose: 0 # 0: off; 1: on
custom_map_mode: 0 # 0: Odometry mode 1: SLAM mode 2: Relocalization mode. Keep 0 for pure odom fallback to avoid Odin map/odom TF conflicts.
custom_init_pos: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0]
relocalization_map_abs_path: "/absolute/path/to/map_a.bin" # unused in pure odometry mode; edit before Relocalization
# To get the mapping result file, please use the set_param.sh script provided: "./set_param.sh save_map 1"
mapping_result_dest_dir: "" # "": if not specified, save to default location of {ws}/src/odin_ros_driver/map/{driver_start_time}/
mapping_result_file_name: "" # "": if not specified, save to location above with default file name of map_{map_save_time}.bin
# Image mask transfer settings
sendimagemask: 0 # 0: off; 1: on - transfer image mask to device on startup
image_mask_abs_path: "" # absolute path to the image mask file (e.g., /path/to/mask.png(1600x1296 resolution)
# Algorithm reset settings
resetalgo: 0 # 0: off; 1: on - send algo_reset command to device on startup
@@ -0,0 +1,438 @@
Panels:
- Class: rviz/Displays
Help Height: 0
Name: Displays
Property Tree Widget:
Expanded:
- /Global Options1
- /Odometry1
- /slam1
- /TF1/Frames1
Splitter Ratio: 0.4993045926094055
Tree Height: 495
- Class: rviz/Selection
Name: Selection
- Class: rviz/Tool Properties
Expanded:
- /2D Pose Estimate1
- /2D Nav Goal1
- /Publish Point1
Name: Tool Properties
Splitter Ratio: 0.5886790156364441
- Class: rviz/Views
Expanded:
- /Current View1
Name: Views
Splitter Ratio: 0.5
- Class: rviz/Time
Name: Time
SyncMode: 0
SyncSource: Image
Preferences:
PromptSaveOnExit: true
Toolbars:
toolButtonStyle: 2
Visualization Manager:
Class: ""
Displays:
- Alpha: 0.5
Cell Size: 1
Class: rviz/Grid
Color: 160; 160; 164
Enabled: true
Line Style:
Line Width: 0.029999999329447746
Value: Lines
Name: Grid
Normal Cell Count: 0
Offset:
X: 0
Y: 0
Z: 0
Plane: XY
Plane Cell Count: 10
Reference Frame: <Fixed Frame>
Value: true
- Class: rviz/Image
Enabled: true
Image Topic: /odin1/image
Max Value: 1
Median window: 5
Min Value: 0
Name: Image
Normalize Range: true
Queue Size: 2
Transport Hint: raw
Unreliable: false
Value: true
- Class: rviz/Image
Enabled: false
Image Topic: /odin1/image/undistorted
Max Value: 1
Median window: 5
Min Value: 0
Name: Image_undistort
Normalize Range: true
Queue Size: 2
Transport Hint: raw
Unreliable: false
Value: false
- Class: rviz/Image
Enabled: false
Image Topic: /odin1/reprojected_image
Max Value: 1
Median window: 5
Min Value: 0
Name: cloudslam_reprojected
Normalize Range: true
Queue Size: 2
Transport Hint: raw
Unreliable: false
Value: false
- Alpha: 1
Autocompute Intensity Bounds: true
Autocompute Value Bounds:
Max Value: 10
Min Value: -10
Value: true
Axis: Z
Channel Name: intensity
Class: rviz/PointCloud2
Color: 255; 255; 255
Color Transformer: Intensity
Decay Time: 0
Enabled: false
Invert Rainbow: false
Max Color: 255; 255; 255
Min Color: 0; 0; 0
Name: raw
Position Transformer: XYZ
Queue Size: 10
Selectable: true
Size (Pixels): 3
Size (m): 0.009999999776482582
Style: Points
Topic: /odin1/cloud_raw
Unreliable: false
Use Fixed Frame: true
Use rainbow: true
Value: false
- Alpha: 1
Autocompute Intensity Bounds: true
Autocompute Value Bounds:
Max Value: 1.8493094444274902
Min Value: -0.13891705870628357
Value: true
Axis: Z
Channel Name: intensity
Class: rviz/PointCloud2
Color: 255; 255; 255
Color Transformer: RGB8
Decay Time: 0
Enabled: false
Invert Rainbow: false
Max Color: 255; 255; 255
Min Color: 0; 0; 0
Name: render
Position Transformer: XYZ
Queue Size: 10
Selectable: true
Size (Pixels): 3
Size (m): 0.009999999776482582
Style: Points
Topic: /odin1/cloud_render
Unreliable: false
Use Fixed Frame: true
Use rainbow: true
Value: false
- Class: rviz/Group
Displays:
- Angle Tolerance: 0.10000000149011612
Class: rviz/Odometry
Covariance:
Orientation:
Alpha: 0.5
Color: 255; 255; 127
Color Style: Unique
Frame: Local
Offset: 1
Scale: 1
Value: true
Position:
Alpha: 0.30000001192092896
Color: 204; 51; 204
Scale: 1
Value: true
Value: false
Enabled: true
Keep: 1
Name: Odometry
Position Tolerance: 0.10000000149011612
Queue Size: 1
Shape:
Alpha: 1
Axes Length: 1
Axes Radius: 0.10000000149011612
Color: 255; 25; 0
Head Length: 0.30000001192092896
Head Radius: 0.10000000149011612
Shaft Length: 1
Shaft Radius: 0.05000000074505806
Value: Axes
Topic: /odin1/odometry
Unreliable: false
Value: true
- Angle Tolerance: 0.10000000149011612
Class: rviz/Odometry
Covariance:
Orientation:
Alpha: 0.5
Color: 255; 255; 127
Color Style: Unique
Frame: Local
Offset: 1
Scale: 1
Value: true
Position:
Alpha: 0.30000001192092896
Color: 204; 51; 204
Scale: 1
Value: true
Value: true
Enabled: false
Keep: 100
Name: Odometry_high
Position Tolerance: 0.10000000149011612
Queue Size: 10
Shape:
Alpha: 1
Axes Length: 1
Axes Radius: 0.10000000149011612
Color: 52; 101; 164
Head Length: 0.30000001192092896
Head Radius: 0.10000000149011612
Shaft Length: 1
Shaft Radius: 0.05000000074505806
Value: Arrow
Topic: /odin1/odometry_highfreq
Unreliable: false
Value: false
- Class: rviz/MarkerArray
Enabled: false
Marker Topic: /odin1/camera_pose_visual
Name: camera_view
Namespaces:
{}
Queue Size: 100
Value: false
Enabled: true
Name: Odometry
- Class: rviz/Group
Displays:
- Alpha: 1
Autocompute Intensity Bounds: true
Autocompute Value Bounds:
Max Value: 10
Min Value: -10
Value: true
Axis: Z
Channel Name: rgb
Class: rviz/PointCloud2
Color: 255; 255; 255
Color Transformer: RGB8
Decay Time: 0
Enabled: true
Invert Rainbow: false
Max Color: 255; 255; 255
Min Color: 0; 0; 0
Name: slam_current
Position Transformer: XYZ
Queue Size: 10
Selectable: true
Size (Pixels): 3
Size (m): 0.009999999776482582
Style: Points
Topic: /odin1/cloud_slam
Unreliable: false
Use Fixed Frame: true
Use rainbow: true
Value: true
- Alpha: 0.10000000149011612
Autocompute Intensity Bounds: true
Autocompute Value Bounds:
Max Value: 10
Min Value: -10
Value: true
Axis: Z
Channel Name: intensity
Class: rviz/PointCloud2
Color: 255; 255; 255
Color Transformer: RGB8
Decay Time: 5
Enabled: false
Invert Rainbow: false
Max Color: 255; 255; 255
Min Color: 0; 0; 0
Name: slam_decay
Position Transformer: XYZ
Queue Size: 10
Selectable: true
Size (Pixels): 1
Size (m): 0.009999999776482582
Style: Points
Topic: /odin1/cloud_slam
Unreliable: false
Use Fixed Frame: true
Use rainbow: true
Value: false
- Class: rviz/MarkerArray
Enabled: false
Marker Topic: /odin1/path
Name: path
Namespaces:
{}
Queue Size: 100
Value: false
Enabled: true
Name: slam
- Class: rviz/Group
Displays:
- Class: rviz/Image
Enabled: true
Image Topic: /odin1/depth_img_competetion
Max Value: 1
Median window: 5
Min Value: 0
Name: dense_depth_image
Normalize Range: true
Queue Size: 2
Transport Hint: raw
Unreliable: false
Value: true
- Alpha: 1
Autocompute Intensity Bounds: true
Autocompute Value Bounds:
Max Value: 10
Min Value: -10
Value: true
Axis: Z
Channel Name: intensity
Class: rviz/PointCloud2
Color: 255; 255; 255
Color Transformer: RGB8
Decay Time: 0
Enabled: false
Invert Rainbow: false
Max Color: 255; 255; 255
Min Color: 0; 0; 0
Name: dense_depth_cloud
Position Transformer: XYZ
Queue Size: 10
Selectable: true
Size (Pixels): 3
Size (m): 0.009999999776482582
Style: Points
Topic: /odin1/depth_img_competetion_cloud
Unreliable: false
Use Fixed Frame: true
Use rainbow: true
Value: false
Enabled: false
Name: dense_depth_demo
- Class: rviz/TF
Enabled: true
Filter (blacklist): ""
Filter (whitelist): ""
Frame Timeout: 15
Frames:
All Enabled: true
odin1_base_link:
Value: true
odom:
Value: true
Marker Alpha: 1
Marker Scale: 1
Name: TF
Show Arrows: true
Show Axes: true
Show Names: true
Tree:
odom:
odin1_base_link:
{}
Update Interval: 0
Value: true
Enabled: true
Global Options:
Background Color: 48; 48; 48
Default Light: true
Fixed Frame: odom
Frame Rate: 30
Name: root
Tools:
- Class: rviz/Interact
Hide Inactive Objects: true
- Class: rviz/MoveCamera
- Class: rviz/Select
- Class: rviz/FocusCamera
- Class: rviz/Measure
- Class: rviz/SetInitialPose
Theta std deviation: 0.2617993950843811
Topic: /initialpose
X std deviation: 0.5
Y std deviation: 0.5
- Class: rviz/SetGoal
Topic: /move_base_simple/goal
- Class: rviz/PublishPoint
Single click: true
Topic: /clicked_point
Value: true
Views:
Current:
Class: rviz/Orbit
Distance: 16.25591278076172
Enable Stereo Rendering:
Stereo Eye Separation: 0.05999999865889549
Stereo Focal Distance: 1
Swap Stereo Eyes: false
Value: false
Field of View: 0.7853981852531433
Focal Point:
X: 0
Y: 0
Z: 0
Focal Shape Fixed Size: false
Focal Shape Size: 0.05000000074505806
Invert Z Axis: false
Name: Current View
Near Clip Distance: 0.009999999776482582
Pitch: 1.010398030281067
Target Frame: odom
Yaw: 0.8753980994224548
Saved: ~
Window Geometry:
Displays:
collapsed: false
Height: 1016
Hide Left Dock: false
Hide Right Dock: false
Image:
collapsed: false
Image_undistort:
collapsed: false
QMainWindow State: 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
Selection:
collapsed: false
Time:
collapsed: false
Tool Properties:
collapsed: false
Views:
collapsed: false
Width: 1848
X: 72
Y: 27
cloudslam_reprojected:
collapsed: false
dense_depth_image:
collapsed: false
@@ -0,0 +1,499 @@
Panels:
- Class: rviz_common/Displays
Help Height: 0
Name: Displays
Property Tree Widget:
Expanded:
- /Global Options1
- /Status1
- /Image1/Topic1
- /cloudslam_reprojected1
- /Odometry1
- /slam1
- /dense_depth_demo1
Splitter Ratio: 0.5
Tree Height: 593
- Class: rviz_common/Selection
Name: Selection
- Class: rviz_common/Tool Properties
Expanded:
- /2D Goal Pose1
- /Publish Point1
Name: Tool Properties
Splitter Ratio: 0.5886790156364441
- Class: rviz_common/Views
Expanded:
- /Current View1
Name: Views
Splitter Ratio: 0.5
Visualization Manager:
Class: ""
Displays:
- Alpha: 0.5
Cell Size: 1
Class: rviz_default_plugins/Grid
Color: 160; 160; 164
Enabled: true
Line Style:
Line Width: 0.029999999329447746
Value: Lines
Name: Grid
Normal Cell Count: 0
Offset:
X: 0
Y: 0
Z: 0
Plane: XY
Plane Cell Count: 10
Reference Frame: <Fixed Frame>
Value: true
- Class: rviz_default_plugins/Image
Enabled: true
Max Value: 1
Median window: 5
Min Value: 0
Name: Image
Normalize Range: true
Topic:
Depth: 5
Durability Policy: Volatile
History Policy: Keep Last
Reliability Policy: Reliable
Value: /odin1/image
Value: true
- Class: rviz_default_plugins/Image
Enabled: false
Max Value: 1
Median window: 5
Min Value: 0
Name: Image_undistort
Normalize Range: true
Topic:
Depth: 5
Durability Policy: Volatile
History Policy: Keep Last
Reliability Policy: Reliable
Value: /odin1/image/undistorted
Value: false
- Class: rviz_default_plugins/Image
Enabled: false
Max Value: 1
Median window: 5
Min Value: 0
Name: cloudslam_reprojected
Normalize Range: true
Topic:
Depth: 5
Durability Policy: Volatile
History Policy: Keep Last
Reliability Policy: Reliable
Value: /odin1/reprojected_image
Value: false
- Alpha: 1
Autocompute Intensity Bounds: true
Autocompute Value Bounds:
Max Value: 10
Min Value: -10
Value: true
Axis: Z
Channel Name: intensity
Class: rviz_default_plugins/PointCloud2
Color: 255; 255; 255
Color Transformer: Intensity
Decay Time: 0
Enabled: false
Invert Rainbow: false
Max Color: 255; 255; 255
Max Intensity: 239
Min Color: 0; 0; 0
Min Intensity: 0
Name: raw
Position Transformer: XYZ
Selectable: true
Size (Pixels): 3
Size (m): 0.009999999776482582
Style: Points
Topic:
Depth: 5
Durability Policy: Volatile
Filter size: 10
History Policy: Keep Last
Reliability Policy: Reliable
Value: /odin1/cloud_raw
Use Fixed Frame: true
Use rainbow: true
Value: false
- Alpha: 1
Autocompute Intensity Bounds: true
Autocompute Value Bounds:
Max Value: 1.8730175495147705
Min Value: -0.12252448499202728
Value: true
Axis: Z
Channel Name: rgb
Class: rviz_default_plugins/PointCloud2
Color: 255; 255; 255
Color Transformer: RGB8
Decay Time: 0
Enabled: false
Invert Rainbow: false
Max Color: 255; 255; 255
Max Intensity: 2.3509885615147286e-38
Min Color: 0; 0; 0
Min Intensity: 0
Name: render
Position Transformer: XYZ
Selectable: true
Size (Pixels): 3
Size (m): 0.009999999776482582
Style: Points
Topic:
Depth: 5
Durability Policy: Volatile
Filter size: 10
History Policy: Keep Last
Reliability Policy: Reliable
Value: /odin1/cloud_render
Use Fixed Frame: true
Use rainbow: true
Value: false
- Class: rviz_common/Group
Displays:
- Angle Tolerance: 0.10000000149011612
Class: rviz_default_plugins/Odometry
Covariance:
Orientation:
Alpha: 0.5
Color: 255; 255; 127
Color Style: Unique
Frame: Local
Offset: 1
Scale: 1
Value: true
Position:
Alpha: 0.30000001192092896
Color: 204; 51; 204
Scale: 1
Value: true
Value: true
Enabled: true
Keep: 1
Name: Odometry
Position Tolerance: 0.10000000149011612
Shape:
Alpha: 1
Axes Length: 1
Axes Radius: 0.10000000149011612
Color: 255; 25; 0
Head Length: 0.30000001192092896
Head Radius: 0.10000000149011612
Shaft Length: 1
Shaft Radius: 0.05000000074505806
Value: Axes
Topic:
Depth: 5
Durability Policy: Volatile
Filter size: 10
History Policy: Keep Last
Reliability Policy: Reliable
Value: /odin1/odometry
Value: true
- Angle Tolerance: 0.10000000149011612
Class: rviz_default_plugins/Odometry
Covariance:
Orientation:
Alpha: 0.5
Color: 255; 255; 127
Color Style: Unique
Frame: Local
Offset: 1
Scale: 1
Value: true
Position:
Alpha: 0.30000001192092896
Color: 204; 51; 204
Scale: 1
Value: true
Value: true
Enabled: false
Keep: 10
Name: Odometry_high
Position Tolerance: 0.10000000149011612
Shape:
Alpha: 1
Axes Length: 1
Axes Radius: 0.10000000149011612
Color: 98; 160; 234
Head Length: 0.30000001192092896
Head Radius: 0.10000000149011612
Shaft Length: 1
Shaft Radius: 0.05000000074505806
Value: Arrow
Topic:
Depth: 5
Durability Policy: Volatile
Filter size: 10
History Policy: Keep Last
Reliability Policy: Reliable
Value: /odin1/odometry_highfreq
Value: false
- Class: rviz_default_plugins/MarkerArray
Enabled: false
Name: camera_view
Namespaces:
{}
Topic:
Depth: 5
Durability Policy: Volatile
History Policy: Keep Last
Reliability Policy: Reliable
Value: /odin1/camera_pose_visual
Value: false
Enabled: true
Name: Odometry
- Class: rviz_common/Group
Displays:
- Alpha: 1
Autocompute Intensity Bounds: true
Autocompute Value Bounds:
Max Value: 10
Min Value: -10
Value: true
Axis: Z
Channel Name: intensity
Class: rviz_default_plugins/PointCloud2
Color: 255; 255; 255
Color Transformer: RGB8
Decay Time: 0
Enabled: true
Invert Rainbow: false
Max Color: 255; 255; 255
Max Intensity: 4096
Min Color: 0; 0; 0
Min Intensity: 0
Name: slam_current
Position Transformer: XYZ
Selectable: true
Size (Pixels): 3
Size (m): 0.009999999776482582
Style: Points
Topic:
Depth: 5
Durability Policy: Volatile
Filter size: 10
History Policy: Keep Last
Reliability Policy: Reliable
Value: /odin1/cloud_slam
Use Fixed Frame: true
Use rainbow: true
Value: true
- Alpha: 0.10000000149011612
Autocompute Intensity Bounds: true
Autocompute Value Bounds:
Max Value: 10
Min Value: -10
Value: true
Axis: Z
Channel Name: intensity
Class: rviz_default_plugins/PointCloud2
Color: 255; 255; 255
Color Transformer: RGB8
Decay Time: 10
Enabled: false
Invert Rainbow: false
Max Color: 255; 255; 255
Max Intensity: 4096
Min Color: 0; 0; 0
Min Intensity: 0
Name: slam_deacy
Position Transformer: XYZ
Selectable: true
Size (Pixels): 1
Size (m): 0.009999999776482582
Style: Points
Topic:
Depth: 5
Durability Policy: Volatile
Filter size: 10
History Policy: Keep Last
Reliability Policy: Reliable
Value: /odin1/cloud_slam
Use Fixed Frame: true
Use rainbow: true
Value: false
- Class: rviz_default_plugins/MarkerArray
Enabled: false
Name: path
Namespaces:
{}
Topic:
Depth: 5
Durability Policy: Volatile
History Policy: Keep Last
Reliability Policy: Reliable
Value: /odin1/path
Value: false
Enabled: true
Name: slam
- Class: rviz_common/Group
Displays:
- Class: rviz_default_plugins/Image
Enabled: true
Max Value: 1
Median window: 5
Min Value: 0
Name: dense_depth_image
Normalize Range: true
Topic:
Depth: 5
Durability Policy: Volatile
History Policy: Keep Last
Reliability Policy: Reliable
Value: /odin1/depth_img_competetion
Value: true
- Alpha: 1
Autocompute Intensity Bounds: true
Autocompute Value Bounds:
Max Value: 10
Min Value: -10
Value: true
Axis: Z
Channel Name: intensity
Class: rviz_default_plugins/PointCloud2
Color: 255; 255; 255
Color Transformer: RGB8
Decay Time: 0
Enabled: false
Invert Rainbow: false
Max Color: 255; 255; 255
Max Intensity: 4096
Min Color: 0; 0; 0
Min Intensity: 0
Name: dense_depth_cloud
Position Transformer: XYZ
Selectable: true
Size (Pixels): 3
Size (m): 0.009999999776482582
Style: Flat Squares
Topic:
Depth: 5
Durability Policy: Volatile
Filter size: 10
History Policy: Keep Last
Reliability Policy: Reliable
Value: /odin1/depth_img_competetion_cloud
Use Fixed Frame: true
Use rainbow: true
Value: false
Enabled: false
Name: dense_depth_demo
- Class: rviz_default_plugins/TF
Enabled: true
Frame Timeout: 15
Frames:
All Enabled: true
odin1_base_link:
Value: true
odom:
Value: true
Marker Scale: 1
Name: TF
Show Arrows: true
Show Axes: true
Show Names: false
Tree:
odom:
odin1_base_link:
{}
Update Interval: 0
Value: true
Enabled: true
Global Options:
Background Color: 48; 48; 48
Fixed Frame: odom
Frame Rate: 30
Name: root
Tools:
- Class: rviz_default_plugins/Interact
Hide Inactive Objects: true
- Class: rviz_default_plugins/MoveCamera
- Class: rviz_default_plugins/Select
- Class: rviz_default_plugins/FocusCamera
- Class: rviz_default_plugins/Measure
Line color: 128; 128; 0
- Class: rviz_default_plugins/SetInitialPose
Covariance x: 0.25
Covariance y: 0.25
Covariance yaw: 0.06853891909122467
Topic:
Depth: 5
Durability Policy: Volatile
History Policy: Keep Last
Reliability Policy: Reliable
Value: /initialpose
- Class: rviz_default_plugins/SetGoal
Topic:
Depth: 5
Durability Policy: Volatile
History Policy: Keep Last
Reliability Policy: Reliable
Value: /goal_pose
- Class: rviz_default_plugins/PublishPoint
Single click: true
Topic:
Depth: 5
Durability Policy: Volatile
History Policy: Keep Last
Reliability Policy: Reliable
Value: /clicked_point
Transformation:
Current:
Class: rviz_default_plugins/TF
Value: true
Views:
Current:
Class: rviz_default_plugins/Orbit
Distance: 10.9336576461792
Enable Stereo Rendering:
Stereo Eye Separation: 0.05999999865889549
Stereo Focal Distance: 1
Swap Stereo Eyes: false
Value: false
Focal Point:
X: 0.2568470537662506
Y: 2.1451337337493896
Z: 0.3774382472038269
Focal Shape Fixed Size: false
Focal Shape Size: 0.05000000074505806
Invert Z Axis: false
Name: Current View
Near Clip Distance: 0.009999999776482582
Pitch: 0.3953983187675476
Target Frame: odom
Value: Orbit (rviz)
Yaw: 3.230407953262329
Saved: ~
Window Geometry:
Displays:
collapsed: false
Height: 1016
Hide Left Dock: false
Hide Right Dock: false
Image:
collapsed: false
Image_undistort:
collapsed: false
QMainWindow State: 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
Selection:
collapsed: false
Tool Properties:
collapsed: false
Views:
collapsed: false
Width: 1920
X: 540
Y: 124
cloudslam_reprojected:
collapsed: false
dense_depth_image:
collapsed: false
@@ -0,0 +1,78 @@
/*
Copyright 2025 Manifold Tech Ltd.(www.manifoldtech.com.co)
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
*/
#pragma once
#ifdef ROS2
#include <rclcpp/rclcpp.hpp>
#include <std_msgs/msg/color_rgba.hpp>
#include <std_msgs/msg/header.hpp>
#include <visualization_msgs/msg/marker.hpp>
#include <visualization_msgs/msg/marker_array.hpp>
#else
#include <ros/ros.h>
#include <std_msgs/ColorRGBA.h>
#include <std_msgs/Header.h>
#include <visualization_msgs/Marker.h>
#include <visualization_msgs/MarkerArray.h>
#endif
#include <Eigen/Dense>
#include <Eigen/Geometry>
class camera_pose_visualization {
public:
std::string m_marker_ns;
camera_pose_visualization(float r, float g, float b, float a);
void setImageBoundaryColor(float r, float g, float b, float a = 1.0);
void setOpticalCenterConnectorColor(float r, float g, float b, float a = 1.0);
void setScale(double s);
void setLineWidth(double width);
void add_pose(const Eigen::Vector3d& p, const Eigen::Quaterniond& q);
void reset();
#ifdef ROS2
using ColorRGBA = std_msgs::msg::ColorRGBA;
using Marker = visualization_msgs::msg::Marker;
using MarkerArray = visualization_msgs::msg::MarkerArray;
using Header = std_msgs::msg::Header;
using Publisher = rclcpp::Publisher<MarkerArray>;
#else
using ColorRGBA = std_msgs::ColorRGBA;
using Marker = visualization_msgs::Marker;
using MarkerArray = visualization_msgs::MarkerArray;
using Header = std_msgs::Header;
using Publisher = ros::Publisher;
#endif
void publish_by(Publisher& pub, const Header& header);
void add_edge(const Eigen::Vector3d& p0, const Eigen::Vector3d& p1);
void add_loopedge(const Eigen::Vector3d& p0, const Eigen::Vector3d& p1);
private:
std::vector<Marker> m_markers;
ColorRGBA m_image_boundary_color;
ColorRGBA m_optical_center_connector_color;
double m_scale;
double m_line_width;
static const Eigen::Vector3d imlt;
static const Eigen::Vector3d imlb;
static const Eigen::Vector3d imrt;
static const Eigen::Vector3d imrb;
static const Eigen::Vector3d oc ;
static const Eigen::Vector3d lt0 ;
static const Eigen::Vector3d lt1 ;
static const Eigen::Vector3d lt2 ;
};
@@ -0,0 +1,112 @@
/*
Copyright 2025 Manifold Tech Ltd.(www.manifoldtech.com.co)
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
*/
#pragma once
#ifdef ROS2
#include <rclcpp/rclcpp.hpp>
#include <sensor_msgs/msg/point_cloud2.hpp>
#include <sensor_msgs/msg/image.hpp>
#include <nav_msgs/msg/odometry.hpp>
#include <image_transport/image_transport.hpp>
#include <cv_bridge/cv_bridge.h>
#include <message_filters/subscriber.h>
#include <message_filters/time_synchronizer.h>
#include <message_filters/sync_policies/exact_time.h>
#include <message_filters/sync_policies/approximate_time.h>
#else
#include <ros/ros.h>
#include <sensor_msgs/PointCloud2.h>
#include <sensor_msgs/Image.h>
#include <nav_msgs/Odometry.h>
#include <cv_bridge/cv_bridge.h>
#include <message_filters/subscriber.h>
#include <message_filters/time_synchronizer.h>
#include <message_filters/sync_policies/exact_time.h>
#include <message_filters/sync_policies/approximate_time.h>
#endif
#include <pcl_conversions/pcl_conversions.h>
#include <pcl/point_types.h>
#include <pcl/point_cloud.h>
#include "cloud_reprojector.hpp"
#include <string>
#include <memory>
#ifdef ROS2
class CloudReprojectionRosNode : public rclcpp::Node
{
public:
CloudReprojectionRosNode(const rclcpp::NodeOptions& options = rclcpp::NodeOptions());
private:
using PointCloud2 = sensor_msgs::msg::PointCloud2;
using Odometry = nav_msgs::msg::Odometry;
using Image = sensor_msgs::msg::Image;
std::string cloud_slam_topic_;
std::string odometry_topic_;
std::string wiwc_topic_;
std::string reprojected_image_topic_;
message_filters::Subscriber<PointCloud2> cloud_sub_;
message_filters::Subscriber<Odometry> odom_sub_;
message_filters::Subscriber<Odometry> wiwc_sub_;
typedef message_filters::sync_policies::ApproximateTime<PointCloud2, Odometry, Odometry> MySyncPolicy;
typedef message_filters::Synchronizer<MySyncPolicy> Sync;
std::shared_ptr<Sync> sync_;
image_transport::Publisher reprojected_image_pub_;
std::unique_ptr<CloudReprojector> reprojector_;
void loadParameters();
void syncCallback(const PointCloud2::ConstSharedPtr& cloud_msg,
const Odometry::ConstSharedPtr& odom_msg,
const Odometry::ConstSharedPtr& wiwc_msg);
};
#else
class CloudReprojectionRosNode
{
public:
CloudReprojectionRosNode(ros::NodeHandle& nh, ros::NodeHandle& pnh);
private:
ros::NodeHandle nh_, pnh_;
std::string cloud_slam_topic_;
std::string odometry_topic_;
std::string wiwc_topic_;
std::string reprojected_image_topic_;
message_filters::Subscriber<sensor_msgs::PointCloud2> cloud_sub_;
message_filters::Subscriber<nav_msgs::Odometry> odom_sub_;
message_filters::Subscriber<nav_msgs::Odometry> wiwc_sub_;
typedef message_filters::sync_policies::ApproximateTime<sensor_msgs::PointCloud2, nav_msgs::Odometry, nav_msgs::Odometry> MySyncPolicy;
typedef message_filters::Synchronizer<MySyncPolicy> Sync;
std::shared_ptr<Sync> sync_;
ros::Publisher reprojected_image_pub_;
std::unique_ptr<CloudReprojector> reprojector_;
void loadParameters();
void syncCallback(const sensor_msgs::PointCloud2ConstPtr& cloud_msg,
const nav_msgs::OdometryConstPtr& odom_msg,
const nav_msgs::OdometryConstPtr& wiwc_msg);
};
#endif
@@ -0,0 +1,86 @@
/*
Copyright 2025 Manifold Tech Ltd.(www.manifoldtech.com.co)
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
*/
#pragma once
#include <Eigen/Dense>
#include <Eigen/Geometry>
#include <opencv2/opencv.hpp>
#include <pcl/point_types.h>
#include <pcl/point_cloud.h>
#include <pcl/common/transforms.h>
#include "polynomial_camera.hpp"
#include <memory>
class CloudReprojector
{
public:
struct CameraParams
{
int image_width = 1600;
int image_height = 1296;
double A11 = 0.0, A12 = 0.0, A22 = 0.0;
double u0 = 0.0, v0 = 0.0;
double k2 = 0.0, k3 = 0.0, k4 = 0.0, k5 = 0.0, k6 = 0.0, k7 = 0.0;
};
struct ExtrinsicParams
{
Eigen::Matrix4d Tcl = Eigen::Matrix4d::Identity(); // camera to lidar
Eigen::Matrix4d Til = Eigen::Matrix4d::Identity(); // lidar to imu (fixed)
Eigen::Matrix4d Tic = Eigen::Matrix4d::Identity(); // camera to imu (calculated)
};
struct OdomPose
{
Eigen::Quaterniond orientation = Eigen::Quaterniond::Identity();
Eigen::Vector3d position = Eigen::Vector3d::Zero();
};
CloudReprojector();
~CloudReprojector() = default;
bool initialize(const CameraParams& cam_params, const ExtrinsicParams& ext_params);
cv::Mat reprojectCloud(const pcl::PointCloud<pcl::PointXYZRGB>& cloud_odom,
const OdomPose& odom_pose);
void setPointRadius(int radius) { point_radius_ = radius; }
int getPointRadius() const { return point_radius_; }
const CameraParams& getCameraParams() const { return camera_params_; }
const ExtrinsicParams& getExtrinsicParams() const { return extrinsic_params_; }
// Update extrinsic parameters at runtime with real-time values from module
void updateExtrinsics(const Eigen::Matrix4d& Tcl, const Eigen::Matrix4d& Til) {
extrinsic_params_.Tcl = Tcl;
extrinsic_params_.Til = Til;
extrinsic_params_.Tic = calculateTic(Tcl, Til);
}
static Eigen::Matrix4d calculateTic(const Eigen::Matrix4d& Tcl, const Eigen::Matrix4d& Til);
private:
Eigen::Matrix4d odomPoseToMatrix(const OdomPose& odom) const;
cv::Mat projectCloudToImage(const pcl::PointCloud<pcl::PointXYZRGB>& cloud_in_cam) const;
CameraParams camera_params_;
ExtrinsicParams extrinsic_params_;
std::unique_ptr<mini_vikit::PolynomialCamera> camera_model_;
int point_radius_ = 4;
bool initialized_ = false;
};
@@ -0,0 +1,240 @@
/*
Copyright 2025 Manifold Tech Ltd.(www.manifoldtech.com.co)
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
*/
#pragma once
#include <cstdint>
#include <string>
#include <vector>
#include <thread>
#include <mutex>
#include <condition_variable>
#include <atomic>
#include <deque>
#include <filesystem>
#include <fstream>
#include <chrono>
#include <memory>
#include <cstdlib>
#include <ctime>
#include <sstream>
class BinaryDataLogger {
public:
struct Options {
public:
// Number of frames per batch before flushing to disk
size_t batch_size = 20;
// Base directory to place time-stamped run directory under
// If empty, will use $ODIN_DATA_DIR or default to $HOME/OdinData
std::filesystem::path base_dir;
};
explicit BinaryDataLogger(const Options& opts) {
namespace fs = std::filesystem;
// Determine base dir
fs::path base = opts.base_dir;
if (base.empty()) {
const char* env_dir = std::getenv("ODIN_DATA_DIR");
if (env_dir && *env_dir) {
base = fs::path(env_dir);
} else {
const char* home = std::getenv("HOME");
base = home && *home ? fs::path(home) / "OdinData" : fs::current_path() / "OdinData";
}
}
// Make timestamp folder name: YYYYMMDD_HHMMSS
auto now = std::chrono::system_clock::now();
std::time_t t = std::chrono::system_clock::to_time_t(now);
std::tm tm{};
#ifdef _WIN32
localtime_s(&tm, &t);
#else
localtime_r(&t, &tm);
#endif
char buf[32];
std::strftime(buf, sizeof(buf), "%Y%m%d_%H%M%S", &tm);
root_dir_ = base / buf;
fs::create_directories(root_dir_);
fs::create_directories(root_dir_ / "image");
// Convert to std::string
std::string timestamp(buf);
created_at_ = timestamp;
// Compose file name
std::filesystem::path pcFile = root_dir_ / ("MT" + timestamp + ".olx");
// Create placeholder files (device_id / firmware / algorithm filled later via update_info_file)
write_info_file_unlocked();
write_text_file(root_dir_ / "image" / "cam_in_ex.txt", "# camera intrinsics/extrinsics TBD\n");
// Init writers
pose_writer_ = std::make_unique<Writer>(root_dir_ / "OdinPose.bin", opts.batch_size);
cloud_writer_ = std::make_unique<Writer>(pcFile, opts.batch_size);
//cloud_writer_ = std::make_unique<Writer>(root_dir_ / "OdinPointCloud.olx", opts.batch_size);
image_writer_ = std::make_unique<Writer>(root_dir_ / "OdinImage.bin", opts.batch_size);
roatation_writer_ = std::make_unique<Writer>(root_dir_ / "OdinRotate.bin", opts.batch_size);
imu_writer_ = std::make_unique<Writer>(root_dir_ / "OdinIMU.bin", opts.batch_size);
}
~BinaryDataLogger() {
// Ensure all writers flush on destruction
if (pose_writer_) pose_writer_->shutdown();
if (cloud_writer_) cloud_writer_->shutdown();
if (image_writer_) image_writer_->shutdown();
if (roatation_writer_) roatation_writer_->shutdown();
if (imu_writer_) imu_writer_->shutdown();
}
const std::filesystem::path& root_dir() const { return root_dir_; }
// Update info.txt with device_id (SN) / firmware (SoC) / algorithm version.
// Safe to call multiple times; latest values win.
void update_info_file(const std::string& device_id,
const std::string& firmware_version,
const std::string& algorithm_version) {
std::lock_guard<std::mutex> lk(info_mtx_);
device_id_ = device_id;
firmware_version_ = firmware_version;
algorithm_version_ = algorithm_version;
write_info_file_unlocked();
}
// Enqueue ready-to-write frame blobs (already formatted as per spec)
void enqueuePoseFrame(std::vector<uint8_t>&& blob) {
if (pose_writer_) pose_writer_->enqueue(std::move(blob));
}
void enqueuePointCloudFrame(std::vector<uint8_t>&& blob) {
if (cloud_writer_) cloud_writer_->enqueue(std::move(blob));
}
void enqueueImageFrame(std::vector<uint8_t>&& blob) {
if (image_writer_) image_writer_->enqueue(std::move(blob));
}
void enqueueRotateFrame(std::vector<uint8_t>&& blob) {
if (roatation_writer_) roatation_writer_->enqueue(std::move(blob));
}
void enqueueIMUFrame(std::vector<uint8_t>&& blob) {
if (imu_writer_) imu_writer_->enqueue(std::move(blob));
}
private:
struct Writer {
explicit Writer(const std::filesystem::path& filepath, size_t batch)
: file_path(filepath), batch_size(batch), stop(false) {
worker = std::thread([this]() { this->run(); });
}
~Writer() {
shutdown();
}
void enqueue(std::vector<uint8_t>&& frame) {
{
std::lock_guard<std::mutex> lk(mtx);
pending.emplace_back(std::move(frame));
if (pending.size() >= batch_size) {
swap_pending_unlocked();
}
}
cv.notify_one();
}
void shutdown() {
{
std::lock_guard<std::mutex> lk(mtx);
if (stop) return;
// Move leftovers to write buffer
if (!pending.empty()) {
swap_pending_unlocked();
}
stop = true;
}
cv.notify_one();
if (worker.joinable()) worker.join();
}
private:
void swap_pending_unlocked() {
if (!pending.empty()) {
write_queue.emplace_back(std::move(pending));
pending.clear();
}
}
void run() {
std::ofstream out(file_path, std::ios::binary | std::ios::app);
if (!out.is_open()) {
// If file can't be opened, silently drop (or could add logging hook)
return;
}
for (;;) {
std::vector<std::vector<uint8_t>> batch;
{
std::unique_lock<std::mutex> lk(mtx);
cv.wait(lk, [&]{ return stop || !write_queue.empty(); });
if (!write_queue.empty()) {
batch = std::move(write_queue.front());
write_queue.pop_front();
} else if (stop) {
break;
}
}
if (!batch.empty()) {
for (auto& frame : batch) {
if (!frame.empty()) {
out.write(reinterpret_cast<const char*>(frame.data()), static_cast<std::streamsize>(frame.size()));
}
}
out.flush();
}
}
}
std::filesystem::path file_path;
size_t batch_size;
std::mutex mtx;
std::condition_variable cv;
std::vector<std::vector<uint8_t>> pending; // accumulate frames
std::deque<std::vector<std::vector<uint8_t>>> write_queue; // queued batches
std::thread worker;
bool stop;
};
static void write_text_file(const std::filesystem::path& p, const std::string& content) {
std::ofstream f(p, std::ios::out | std::ios::trunc);
if (f.is_open()) {
f << content;
}
}
// Render info.txt from current member fields. Caller must hold info_mtx_.
void write_info_file_unlocked() {
std::ostringstream oss;
oss << "device=OdinOne\n"
<< "pointcloud=xyzrgbi\n";
if (!device_id_.empty()) {
oss << "device_id=" << device_id_ << "\n";
}
if (!firmware_version_.empty()) {
oss << "firmware_version=" << firmware_version_ << "\n";
}
if (!algorithm_version_.empty()) {
oss << "algorithm_version=" << algorithm_version_ << "\n";
}
oss << "created_at=" << created_at_ << "\n";
write_text_file(root_dir_ / "image" / "info.txt", oss.str());
}
std::filesystem::path root_dir_;
std::string created_at_;
std::string device_id_;
std::string firmware_version_;
std::string algorithm_version_;
std::mutex info_mtx_;
std::unique_ptr<Writer> pose_writer_;
std::unique_ptr<Writer> cloud_writer_;
std::unique_ptr<Writer> image_writer_;
std::unique_ptr<Writer> roatation_writer_;
std::unique_ptr<Writer> imu_writer_;
};
@@ -0,0 +1,85 @@
/*
Copyright 2025 Manifold Tech Ltd.(www.manifoldtech.com.co)
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
*/
#pragma once
#include <rclcpp/rclcpp.hpp>
#include <sensor_msgs/msg/point_cloud2.hpp>
#include <sensor_msgs/msg/image.hpp>
#include <sensor_msgs/msg/compressed_image.hpp>
#include <pcl_conversions/pcl_conversions.h>
#include <pcl/point_types.h>
#include <pcl/point_cloud.h>
#include <opencv2/opencv.hpp>
#include <cv_bridge/cv_bridge.h>
#include <image_transport/image_transport.hpp>
#include <message_filters/subscriber.h>
#include <message_filters/time_synchronizer.h>
#include <message_filters/sync_policies/approximate_time.h>
#include <Eigen/Dense>
#include "pointcloud_depth_converter.hpp"
#include <string>
#include <memory>
class DepthImageRos2Node : public rclcpp::Node
{
public:
explicit DepthImageRos2Node(const rclcpp::NodeOptions & options = rclcpp::NodeOptions());
void initialize();
private:
std::string cloud_raw_topic_;
std::string color_compressed_topic_;
std::string color_raw_topic_;
std::string depth_image_topic_;
std::string depth_cloud_topic_;
message_filters::Subscriber<sensor_msgs::msg::PointCloud2> cloud_sub_;
message_filters::Subscriber<sensor_msgs::msg::CompressedImage> color_compressed_sub_;
message_filters::Subscriber<sensor_msgs::msg::Image> color_sub_;
typedef message_filters::sync_policies::ApproximateTime<
sensor_msgs::msg::PointCloud2,
// sensor_msgs::msg::CompressedImage,
sensor_msgs::msg::Image> MySyncPolicy;
typedef message_filters::Synchronizer<MySyncPolicy> Sync;
std::shared_ptr<Sync> sync_;
std::shared_ptr<image_transport::ImageTransport> it_;
image_transport::Publisher depth_image_pub_;
rclcpp::Publisher<sensor_msgs::msg::PointCloud2>::SharedPtr depth_cloud_pub_;
std::unique_ptr<PointCloudToDepthConverter> depth_converter_;
PointCloudToDepthConverter::CameraParams loadCameraParams();
void syncCallback(const sensor_msgs::msg::PointCloud2::ConstSharedPtr cloud_msg,
// const sensor_msgs::msg::CompressedImage::ConstSharedPtr image_msg,
const sensor_msgs::msg::Image::ConstSharedPtr color_msg);
void publishDepthImage(const cv::Mat &img,
const std_msgs::msg::Header &header,
const std::string &encoding = "32FC1");
void publishDepthCloud(const pcl::PointCloud<pcl::PointXYZRGB> &colored_cloud,
const std_msgs::msg::Header &header);
};
@@ -0,0 +1,83 @@
/*
Copyright 2025 Manifold Tech Ltd.(www.manifoldtech.com.co)
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
*/
#pragma once
#include <ros/ros.h>
#include <sensor_msgs/PointCloud2.h>
#include <sensor_msgs/Image.h>
#include <sensor_msgs/CompressedImage.h>
#include <pcl_conversions/pcl_conversions.h>
#include <pcl/point_types.h>
#include <pcl/point_cloud.h>
#include <opencv2/opencv.hpp>
#include <cv_bridge/cv_bridge.h>
#include <image_transport/image_transport.h>
#include <message_filters/subscriber.h>
#include <message_filters/time_synchronizer.h>
#include <message_filters/sync_policies/approximate_time.h>
#include <Eigen/Dense>
#include "pointcloud_depth_converter.hpp"
#include <string>
#include <memory>
class DepthImageRosNode
{
public:
DepthImageRosNode(ros::NodeHandle &nh, ros::NodeHandle &pnh);
private:
ros::NodeHandle nh_, pnh_;
image_transport::ImageTransport it_;
std::string cloud_raw_topic_;
std::string color_raw_topic_;
std::string color_compressed_topic_;
std::string depth_image_topic_;
std::string depth_cloud_topic_;
message_filters::Subscriber<sensor_msgs::PointCloud2> cloud_sub_;
message_filters::Subscriber<sensor_msgs::Image> color_sub_;
message_filters::Subscriber<sensor_msgs::CompressedImage> color_compressed_sub_;
typedef message_filters::sync_policies::ApproximateTime<sensor_msgs::PointCloud2, sensor_msgs::Image> MySyncPolicy;
typedef message_filters::Synchronizer<MySyncPolicy> Sync;
std::shared_ptr<Sync> sync_;
image_transport::Publisher depth_image_pub_;
ros::Publisher depth_cloud_pub_;
std::unique_ptr<PointCloudToDepthConverter> depth_converter_;
PointCloudToDepthConverter::CameraParams loadCameraParams();
void syncCallback(const sensor_msgs::PointCloud2ConstPtr &cloud_msg,
const sensor_msgs::ImageConstPtr &image_msg);
void publishDepthImage(const cv::Mat &img,
const std_msgs::Header &header,
const std::string &encoding = "32FC1");
void publishDepthCloud(const pcl::PointCloud<pcl::PointXYZRGB> &colored_cloud,
const std_msgs::Header &header);
};
File diff suppressed because it is too large Load Diff
@@ -0,0 +1,91 @@
/*
Copyright 2025 Manifold Tech Ltd.(www.manifoldtech.com.co)
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
*/
#pragma once
#ifdef ROS2
#include <rclcpp/rclcpp.hpp>
#include <sensor_msgs/msg/image.hpp>
#include <cv_bridge/cv_bridge.h>
#include <mutex>
#else
#include <ros/ros.h>
#include <sensor_msgs/Image.h>
#include <cv_bridge/cv_bridge.h>
#include <message_filters/subscriber.h>
#include <message_filters/sync_policies/approximate_time.h>
#include <message_filters/synchronizer.h>
#endif
#include <opencv2/opencv.hpp>
#include <string>
#include <memory>
#ifdef ROS2
class ImageOverlayNode : public rclcpp::Node
{
public:
ImageOverlayNode(const rclcpp::NodeOptions& options = rclcpp::NodeOptions());
private:
using Image = sensor_msgs::msg::Image;
std::string reprojected_topic_;
std::string camera_topic_;
std::string overlay_topic_;
double alpha_; // blend alpha for overlay
rclcpp::Subscription<Image>::SharedPtr reproj_sub_;
rclcpp::Subscription<Image>::SharedPtr camera_sub_;
rclcpp::Publisher<Image>::SharedPtr overlay_pub_;
// Cache latest images
cv::Mat latest_reproj_img_;
cv::Mat latest_camera_img_;
std_msgs::msg::Header latest_header_;
std::mutex mutex_;
void reprojCallback(Image::ConstSharedPtr msg);
void cameraCallback(Image::ConstSharedPtr msg);
void publishOverlay();
};
#else
#include <mutex>
class ImageOverlayNode
{
public:
ImageOverlayNode(ros::NodeHandle& nh, ros::NodeHandle& pnh);
private:
ros::NodeHandle nh_, pnh_;
std::string reprojected_topic_;
std::string camera_topic_;
std::string overlay_topic_;
double alpha_; // blend alpha for overlay
ros::Subscriber reproj_sub_;
ros::Subscriber camera_sub_;
ros::Publisher overlay_pub_;
// Cache latest images
cv::Mat latest_reproj_img_;
cv::Mat latest_camera_img_;
std_msgs::Header latest_header_;
std::mutex mutex_;
void reprojCallback(const sensor_msgs::ImageConstPtr& msg);
void cameraCallback(const sensor_msgs::ImageConstPtr& msg);
void publishOverlay();
};
#endif
@@ -0,0 +1,532 @@
#ifndef LIDAR_API_H
#define LIDAR_API_H
/**
* @file lidar_api.h
* @brief LiDAR device API for controlling and accessing LiDAR sensor data
*
* This header provides the public interface for interacting with LiDAR devices.
* It includes functions for device management, data streaming control, and
* device configuration.
*
* @copyright Copyright (c) 2025, Manifold Tech Limited, All Rights Reserved
* @version 1.0
*
* ┌──────────────────────────────────────────────────────────────────────────────┐
* │ QUICK START │
* ├──────────────────────────────────────────────────────────────────────────────┤
* │ 1. API Call Sequence (typical usage): │
* │ │
* │ lidar_system_init(device_cb) // Initialize system, register device cb│
* │ ↓ │
* │ [Wait for device_cb with attach=true to get device info] │
* │ ↓ │
* │ lidar_create_device(&dev_info, &handle) // Create device handle │
* │ ↓ │
* │ lidar_register_stream_callback(handle, cb_info) // Register data callback│
* │ ↓ │
* │ lidar_open_device(handle) // Connect to device │
* │ ↓ │
* │ lidar_set_mode(handle, mode) // Set RAW or SLAM mode │
* │ ↓ │
* │ lidar_start_stream(handle, type, odr) // Start specific data stream │
* │ ↓ │
* │ [Data arrives via registered callback] │
* │ ↓ │
* │ lidar_stop_stream(handle, type) // Stop data stream │
* │ ↓ │
* │ lidar_close_device(handle) // Disconnect │
* │ ↓ │
* │ lidar_destory_device(handle) // Release device handle │
* │ ↓ │
* │ lidar_system_deinit() // Cleanup system resources │
* │ │
* ├──────────────────────────────────────────────────────────────────────────────┤
* │ 2. Operating Modes and Available Data Types: │
* │ │
* │ LIDAR_MODE_RAW: │
* │ - LIDAR_DT_RAW_RGB (RGB camera image, NV12 format) │
* │ - LIDAR_DT_RAW_IMU (IMU data at 400Hz) │
* │ - LIDAR_DT_RAW_DTOF (DTOF depth + point cloud + confidence) │
* │ - LIDAR_DT_DEV_STATUS (Device status info) │
* │ - LIDAR_DT_NTP (PTP/NTP sync data) │
* │ │
* │ LIDAR_MODE_SLAM: │
* │ - All RAW mode types, plus: │
* │ - LIDAR_DT_SLAM_CLOUD (SLAM point cloud, XYZRGBA) │
* │ - LIDAR_DT_SLAM_ODOMETRY (SLAM odometry at ~10Hz) │
* │ - LIDAR_DT_SLAM_ODOMETRY_HIGHFREQ (Odometry at IMU rate ~400Hz) │
* │ - LIDAR_DT_SLAM_ODOMETRY_TF (Map-Odom transform) │
* │ │
* ├──────────────────────────────────────────────────────────────────────────────┤
* │ 3. activate_stream_type vs start_stream: │
* │ │
* │ lidar_activate_stream_type(): │
* │ - Configure which stream types are enabled in device │
* │ - Can enable multiple types before starting │
* │ - Does NOT start data transmission │
* │ │
* │ lidar_start_stream(): │
* │ - Actually starts data transmission for the specified type │
* │ - Callback will begin receiving data after this call │
* │ │
* │ Typical flow: │
* │ activate_stream_type(LIDAR_DT_RAW_IMU); // Enable IMU │
* │ activate_stream_type(LIDAR_DT_RAW_DTOF); // Enable DTOF │
* │ start_stream(LIDAR_DT_RAW_IMU, ...); // Start IMU stream │
* │ start_stream(LIDAR_DT_RAW_DTOF, ...); // Start DTOF stream │
* │ │
* ├──────────────────────────────────────────────────────────────────────────────┤
* │ 4. Callback Notes (IMPORTANT): │
* │ │
* │ Thread Safety: │
* │ - Callbacks are invoked from internal SDK threads │
* │ - Different data types may use different threads │
* │ - User callback code must be thread-safe │
* │ │
* │ Data Lifetime: │
* │ - Data pointers (pAddr) are ONLY valid during callback execution │
* │ - If you need to keep data, COPY it before callback returns │
* │ - Do NOT store or dereference pAddr after callback returns │
* │ │
* │ Performance: │
* │ - Avoid blocking or time-consuming operations in callback │
* │ - Long callback execution may cause data loss or jitter │
* │ - For heavy processing, copy data and process in separate thread │
* │ │
* ├──────────────────────────────────────────────────────────────────────────────┤
* │ 5. Error Codes: │
* │ 0 : Success │
* │ -1 : General failure │
* │ -2 : Invalid parameter │
* │ -3 : Device not found / not connected │
* │ -4 : Operation timeout │
* │ -5 : Resource allocation failed │
* │ │
* └──────────────────────────────────────────────────────────────────────────────┘
*/
#include "lidar_api_type.h"
#ifdef __cplusplus
extern "C" {
#endif
/**
* @brief Initialize the LiDAR system
*
* Must be called before any other lidar function to set up the system resources.
* This function starts device discovery and will invoke the callback when devices
* are found or disconnected.
*
* @param cb Callback function for device events:
* - Called with attach=true when a new device is discovered
* - Called with attach=false when a device is disconnected
* - The lidar_device_info_t contains serial number to identify the device
*
* Example callback:
* void device_callback(const lidar_device_info_t* info, bool attach) {
* if (attach) {
* printf("Device connected: %s\n", info->serial);
* // Save info for lidar_create_device()
* } else {
* printf("Device disconnected: %s\n", info->serial);
* }
* }
*
* @return int 0 on success, negative error code on failure
*/
int lidar_system_init(lidar_device_callback_t cb);
/**
* @brief Deinitialize the LiDAR system
*
* Releases all resources allocated by the system. Should be called when
* application is shutting down.
*
* @return int 0 on success, negative error code on failure
*/
int lidar_system_deinit(void);
/**
* @brief Create a handle for a LiDAR device
*
* Creates a device handle using the device info received from the device callback.
* The handle is used for all subsequent operations on the device.
*
* @param dev_info Information about the LiDAR device (from lidar_system_init callback)
* - serial: Device serial number (required, used to identify device)
* - model: Device model string
* - online: Device connection status
* @param device [OUTPUT] Pointer to receive the device handle upon success
* @return int 0 on success, negative error code on failure
*
* @note The dev_info should be the same structure received from the device callback
*/
int lidar_create_device(lidar_device_info_t *dev_info, device_handle *device);
/**
* @brief Destroy a LiDAR device handle
*
* Releases resources associated with the device handle. Must be called
* when the device is no longer needed.
*
* @param device Handle to the device to destroy
* @return int 0 on success, negative error code on failure
*/
int lidar_destory_device(device_handle device);
/**
* @brief Register callback function for receiving LiDAR data streams
*
* Sets up a callback function that will be called when new data is available.
* All data types use the same callback; use lidar_data_t.type to distinguish.
*
* @param device Handle to the target device
* @param cb Callback info structure:
* - data_callback: Function pointer, signature: void(const lidar_data_t*, void*)
* - user_data: User context pointer passed to callback (can be NULL)
*
* Example:
* void data_callback(const lidar_data_t* data, void* user_data) {
* switch (data->type) {
* case LIDAR_DT_RAW_IMU:
* imu_convert_data_t* imu = (imu_convert_data_t*)data->stream.imageList[0].pAddr;
* // Process IMU data (COPY if needed, pointer invalid after return)
* break;
* case LIDAR_DT_RAW_DTOF:
* // data->stream.imageList[0]: depth
* // data->stream.imageList[1]: point cloud XYZ
* // data->stream.imageList[2]: confidence
* // data->stream.imageList[3]: intensity
* break;
* }
* }
*
* @return int 0 on success, negative error code on failure
*
* @warning Callback is invoked from SDK internal threads. Avoid blocking operations.
* @warning Data pointers are only valid during callback execution. Copy if needed.
*/
int lidar_register_stream_callback(device_handle device, lidar_data_callback_info_t cb);
/**
* @brief Unregister stream callback for a device
*
* Stops the device from calling back when new data is available.
*
* @param device Handle to the target device
* @return int 0 on success, negative error code on failure
*/
int lidar_unregister_stream_callback(device_handle device);
/**
* @brief Open a LiDAR device for communication
*
* Establishes a connection to the physical device.
*
* @param device Handle to the device to open
* @return int 0 on success, negative error code on failure
*/
int lidar_open_device(device_handle device);
/**
* @brief Close a LiDAR device
*
* Closes the connection to the physical device.
*
* @param device Handle to the device to close
* @return int 0 on success, negative error code on failure
*/
int lidar_close_device(device_handle device);
/**
* @brief Set the operating mode of the LiDAR device
*
* Must be called after lidar_open_device() and before lidar_start_stream().
* Mode determines which data types are available for streaming.
*
* @param device Handle to the target device
* @param mode Operating mode to set:
* - LIDAR_MODE_RAW: Raw sensor data (RGB, IMU, DTOF)
* - LIDAR_MODE_SLAM: SLAM processing enabled (adds odometry, point cloud)
* @return int 0 on success, negative error code on failure
*/
int lidar_set_mode(device_handle device, int mode);
/**
* @brief Start data streaming from the device
*
* Begins the flow of data from the device for the specified type.
* After calling this function, registered callbacks will start receiving data.
*
* @param device Handle to the target device
* @param type Type of data stream to start (lidar_data_type_e):
* - LIDAR_DT_RAW_RGB: RGB camera frames
* - LIDAR_DT_RAW_IMU: IMU data at 400Hz
* - LIDAR_DT_RAW_DTOF: Depth sensor data
* - LIDAR_DT_SLAM_CLOUD: SLAM point cloud (requires SLAM mode)
* - LIDAR_DT_SLAM_ODOMETRY: SLAM odometry (requires SLAM mode)
* - etc. (see lidar_data_type_e in lidar_api_type.h)
* @param dtof_subframe_odr [OUTPUT] Returns DTOF subframe interval in microseconds.
* Only meaningful when type=LIDAR_DT_RAW_DTOF.
* For other types, this value can be ignored.
* @return int 0 on success, negative error code on failure
*
* @note You can start multiple stream types simultaneously by calling this
* function multiple times with different types.
*/
int lidar_start_stream(device_handle device, int type, uint32_t &dtof_subframe_odr);
/**
* @brief Stop data streaming from the device
*
* Stops the flow of data from the device for the specified type.
*
* @param device Handle to the target device
* @param type Type of data stream to stop
* @return int 0 on success, negative error code on failure
*/
int lidar_stop_stream(device_handle device, int type);
/**
* @brief Activate a specific stream type on the device
*
* Enables a specific data stream type in the device configuration.
* This configures the device to be ready for the stream type, but does NOT
* start actual data transmission. Call lidar_start_stream() to begin streaming.
*
* Use this to pre-configure multiple stream types before starting them:
* activate_stream_type(handle, LIDAR_DT_RAW_IMU);
* activate_stream_type(handle, LIDAR_DT_RAW_DTOF);
* start_stream(handle, LIDAR_DT_RAW_IMU, odr);
* start_stream(handle, LIDAR_DT_RAW_DTOF, odr);
*
* @param device Handle to the target device
* @param type Type of data stream to activate (lidar_data_type_e)
* @return int 0 on success, negative error code on failure
*
* @see lidar_start_stream() to actually begin data transmission
* @see lidar_deactivate_stream_type() to disable a stream type
*/
int lidar_activate_stream_type(device_handle device, int type);
/**
* @brief Deactivate a specific stream type on the device
*
* Disables a specific data stream type in the device configuration.
*
* @param device Handle to the target device
* @param type Type of data stream to deactivate
* @return int 0 on success, negative error code on failure
*/
int lidar_deactivate_stream_type(device_handle device, int type);
/**
* @brief Get the calibration file from the device
* @param device Handle to the target device
* @return int 0 on success, negative error code on failure
*/
int lidar_get_calib_file(device_handle device, const char* path);
/**
* @brief Get device calibration parameters
*
* Retrieves the current calibration parameters from the device.
*
* @param device Handle to the target device
* @param param Pointer to receive the calibration parameters
* @return int 0 on success, negative error code on failure
*/
int lidar_get_calibration(device_handle device, lidar_calibration_t* param);
/**
* @brief Set device calibration parameters
*
* Applies new calibration parameters to the device.
*
* @param device Handle to the target device
* @param param Pointer to the calibration parameters to set
* @return int 0 on success, negative error code on failure
*/
int lidar_set_calibration(device_handle device, const lidar_calibration_t *param);
/**
* @brief Set log verbosity level
*
* Controls the amount of log information generated by the LiDAR API.
*
* @param level Log level to set (see level definitions in lidar_api_type.h)
*/
void lidar_log_set_level(lidar_log_level_e level);
/**
* @brief Get the version information of the LiDAR device
*
* Retrieves version information including firmware, system, and application versions.
*
* @param device Handle to the target device
* @param version struct Pointer to receive the version information
* @return int 0 on success, negative error code on failure
*/
int lidar_get_version(device_handle device,lidar_fireware_version_t *version);
/**
* @brief Set custom algorithm parameters for the device
*
* Sends custom parameter settings to the device.
*
* @param device Handle to the target device
* @param param_name String name of the parameter to set
* @param value_data Pointer to the value data to set for the parameter
* @param value_length Length of the value data in bytes
* @return int 0 on success, negative error code on failure
*/
int lidar_set_custom_parameter(device_handle device, const char* param_name, const void* value_data, size_t value_length);
/**
* @brief Get custom algorithm parameters for the device
*
* Get custom parameter settings from the device.
*
* @param device Handle to the target device
* @param param_name String name of the parameter to get
* @param value Integer value to get for the parameter
* @return int 0 on success, negative error code on failure
*/
int lidar_get_custom_parameter(device_handle device, const char* param_name, int* value);
/**
* @brief Get custom algorithm parameters for the device
*
* Get custom parameter settings from the device.
*
* @param device Handle to the target device
* @param param_name String name of the parameter to get
* @param value Integer value to get for the parameter
* @return int 0 on success, negative error code on failure
*/
int lidar_get_custom_parameter(device_handle device, const char* param_name, int* value);
/**
* @brief Set the map file used for relocalization
*
* Read & send specified map file to device for relocalization
*
* @param device Handle to the target device
* @param abs_path Absolute path to the map file
* @return int 0 on success, otherwise on failure
*/
int lidar_set_relocalization_map(device_handle device, const char* abs_path);
/**
* @brief Get the mapping result file from device
*
* Read & send specified map file from device to host
*
* @param device Handle to the target device
* @param dest_dir Destination directory to save the map file
* @param file_name File name to save the map file
* @return int 0 on success, -1 on failure without error code, error code (> 0) otherwise
*/
int lidar_get_mapping_result(device_handle device, const char* dest_dir, const char* file_name);
/**
* @brief Save the current map to a file on the host. Synchronous, all-in-one API.
*
* Internally drives the complete save-map state machine, so callers do not need
* to coordinate the multi-step protocol themselves:
* 1) Set custom parameter save_map = 1 on the device (kicks off generation).
* 2) Poll save_map until the device resets it to 0 (map generation finished).
* 3) Pull the resulting file via the standard mapping-result transfer (same as
* lidar_get_mapping_result).
* 4) Return the final status to the caller.
*
* This is the recommended entry point for saving a map. The lower-level pair
* (lidar_set_custom_parameter("save_map") + lidar_get_mapping_result) remains
* available for advanced use cases.
*
* 同步保存地图到主机上的文件。一站式 API,内部完成「触发生成 → 等待设备完成 →
* 拉取文件」的完整流程,调用方无需自行轮询和处理异步状态。
*
* @param device Device handle.
* @param dest_dir Host directory to save the file into (must already exist).
* @param file_name File name (e.g. "map.bin").
* @param gen_timeout_ms Maximum time (ms) to wait for the device to finish
* generating the map. Pass 0 to use the default (120000 ms).
* @return int
* 0 success, file saved at dest_dir/file_name
* -1 invalid arguments (null device/dir, SDK not initialized, etc.)
* -2 device is busy with another file transfer
* -3 timed out waiting for the device to finish map generation
* -4 file transfer stalled or failed (see logs for details)
* other negative values are propagated from the underlying transfer.
*/
int lidar_save_map(device_handle device,
const char *dest_dir,
const char *file_name,
uint32_t gen_timeout_ms);
/**
* @brief Set the image mask file for the device
*
* Read & send specified image mask file to device
*
* @param device Handle to the target device
* @param abs_path Absolute path to the image mask file (e.g., mask.png)
* @return int 0 on success, -1 on failure, -2 if file transfer in progress
*/
int lidar_set_image_mask(device_handle device, const char* abs_path);
/**
* @brief enable encrypted device log
*
* enable encrypted device log, save to specified directory
* Please send to our support when needed
*
* @param device Handle to the target device
* @param dest_dir Destination directory to save the encrypted logs
* @return int 0 on success, -1 on failure
*/
int lidar_enable_encrypted_device_log(device_handle device, const char* dest_dir);
/**
* @brief Set the depth parameters for the device
*
* This function must be called before starting data stream.
*
* @param device Handle to the target device
* @param params Pointer to the depth parameters to set
* @return int 0 on success, negative error code on failure
*/
int lidar_set_depth_parameter(device_handle device, const lidar_depth_para_t *params);
/**
* @brief Enable or disable IMU smooth sending feature
*
* When enabled, IMU data will be sent at precise intervals (default 400Hz)
* using a dedicated high-priority thread to reduce jitter and timing variance.
* When disabled, IMU data will be sent immediately upon reception.
*
* @param enable 1 to enable smooth sending, 0 to disable
* @return int 0 on success, -1 on failure
*/
int lidar_enable_imu_smooth_sending(int enable);
/**
* @brief Set IMU smooth sending frequency
*
* Set the target frequency for IMU smooth sending. Only effective when
* smooth sending is enabled via lidar_enable_imu_smooth_sending().
*
* @param frequency_hz Target frequency in Hz (1-1000 Hz, recommended 400 Hz)
* @return int 0 on success, -1 on failure
*/
int lidar_set_imu_smooth_frequency(uint32_t frequency_hz);
#ifdef __cplusplus
}
#endif
#endif // LIDAR_API_H
@@ -0,0 +1,242 @@
/*
Copyright 2025 Manifold Tech Ltd.(www.manifoldtech.com.co)
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
*/
#ifndef LIDAR_TYPES_H
#define LIDAR_TYPES_H
#include <stdbool.h>
#include <stdlib.h>
#include <stdint.h>
#ifdef __cplusplus
extern "C" {
#endif
#define LIDAR_SERIAL_MAX 64
#define LIDAR_MODEL_MAX 64
#define LIDAR_IP_MAX 64
typedef void * device_handle;
typedef enum {
LIDAR_LOG_ERROR = 0,
LIDAR_LOG_WARN,
LIDAR_LOG_INFO,
LIDAR_LOG_DEBUG,
} lidar_log_level_e;
typedef enum {
LIDAR_OTA_ALGORITHM,
LIDAR_OTA_FIRMWARE,
LIDAR_OTA_SCRIPT,
LIDAR_OTA_CALIBRATION
} lidar_ota_type_e;
typedef enum {
LIDAR_MODE_RAW,
LIDAR_MODE_SLAM,
} lidar_mode_e;
typedef enum {
LIDAR_DT_NONE = 0,
LIDAR_DT_RAW_RGB,
LIDAR_DT_RAW_IMU,
LIDAR_DT_RAW_DTOF,
LIDAR_DT_SLAM_CLOUD,
LIDAR_DT_SLAM_ODOMETRY,
LIDAR_DT_DEV_STATUS,
LIDAR_DT_SLAM_ODOMETRY_HIGHFREQ,
LIDAR_DT_SLAM_ODOMETRY_TF,
LIDAR_DT_SLAM_WIWC,
LIDAR_DT_NTP
} lidar_data_type_e;
typedef struct {
int8_t serial[LIDAR_SERIAL_MAX];
int8_t model[LIDAR_MODEL_MAX];
bool online;
uint32_t initial_state;
} lidar_device_info_t;
typedef struct {
float x, y, z;
float intensity;
} lidar_point_t;
typedef struct {
float intrinsics[9];
float extrinsics[16];
} lidar_calibration_t;
#define DEVICE_MAX_CH_NUMBER 4
typedef struct {
uint64_t timestamp_ns;
int64_t pos[3];
int64_t orient[4];
} ros2_odom_convert_t;
typedef struct {
uint64_t timestamp_ns;
int64_t pos[3];
int64_t orient[4];
int64_t linear_velocity[3];
int64_t angular_velocity[3];
double pose_cov[36];
double twist_cov[36];
} ros_odom_convert_complete_t;
typedef struct {
float accel_x;
float accel_y;
float accel_z;
float gyro_x;
float gyro_y;
float gyro_z;
uint64_t stamp;
uint64_t sequence;
} imu_convert_data_t;
typedef struct {
uint32_t length;
uint64_t sequence;
uint64_t timestamp;
uint64_t interval;
void* pAddr;
uint32_t width;
uint32_t height;
} buffer_List_t;
typedef struct {
double delay;
double offset;
} ptp_sync_data_t;
typedef struct capture_Image_List_t {
uint32_t imageCount;
buffer_List_t imageList[DEVICE_MAX_CH_NUMBER];
} capture_Image_List_t;
typedef struct {
uint32_t type;
capture_Image_List_t stream;
} lidar_data_t;
typedef void (*lidar_device_callback_t)(const lidar_device_info_t* device, bool attach);
typedef void (*lidar_data_callback_t)(const lidar_data_t *data, void *user_data);
typedef struct {
lidar_data_callback_t data_callback;
void *user_data;
} lidar_data_callback_info_t;
typedef struct {
int major;
int minor;
int patch;
}lidar_version_t;
typedef struct {
lidar_version_t kernel_version;
lidar_version_t mcu_version;
lidar_version_t soc_version;
lidar_version_t Daemon_proc_version;
lidar_version_t slam_version;
} lidar_fireware_version_t;
/**
* @brief RGB image sensor frame rate
*
*/
typedef struct{
int configured_odr; /* rgb image sensor configured output data rate */
int tx_odr; /* rgb image sensor tx output data rate */
} lidar_rgb_sensor_status_t;
/**
* @brief DTOF Lidar frame rate
*
*/
typedef struct{
int configured_odr; /* dtof lidar sensor configured output data rate */
int tx_odr; /* dtof lidar sensor tx output data rate */
int subframe_odr; /* dtof lidar sensor subframe output data rate */
short tx_temp; /* dtof lidar tx module temp */
short rx_temp; /* dtof lidar rx module temp */
} lidar_dtof_sensor_status_t;
/**
* @brief IMU Sensor
*
*/
typedef struct{
int configured_odr; /* imu sensor configured output data rate */
int tx_odr; /* imu sensor tx output data rate */
} lidar_imu_sensor_status_t;
typedef struct{
int package_temp; /* soc package temp */
int cpu_temp; /* cpu temp */
int center_temp; /* center temp */
int gpu_temp; /* gpu temp */
int npu_temp; /* npu temp */
} lidar_soc_thermal_t;
typedef struct
{
double uptime_seconds;
lidar_soc_thermal_t soc_thermal;
int cpu_use_rate[8]; /* cpu usage rate */
int ram_use_rate; /* ram usage rate */
lidar_rgb_sensor_status_t rgb_sensor;
lidar_dtof_sensor_status_t dtof_sensor;
lidar_imu_sensor_status_t imu_sensor;
int slam_cloud_tx_odr; /* slam cloud tx output data rate */
int slam_odom_tx_odr; /* slam odom tx output data rate */
int slam_odom_highfreq_tx_odr; /* slam odom high freq tx output data rate */
} lidar_device_status_t;
typedef enum {
LIDAR_DEVICE_NONE = 0,
LIDAR_DEVICE_NOT_INITIALIZED,
LIDAR_DEVICE_INITIALIZED,
LIDAR_DEVICE_STREAMING,
LIDAR_DEVICE_STREAM_STOPPED,
} lidar_device_initial_state_e;
typedef enum {
LIDAR_DEPTH_ODR_10HZ = 0,
LIDAR_DEPTH_ODR_14_5HZ,
} lidar_depth_odr_e;
typedef struct {
lidar_depth_odr_e odr;
} lidar_depth_para_t;
#ifdef __cplusplus
}
#endif
#endif
@@ -0,0 +1,93 @@
/*
Copyright 2025 Manifold Tech Ltd.(www.manifoldtech.com.co)
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
*/
#pragma once
#include <pcl/point_types.h>
#include <pcl/point_cloud.h>
#include <pcl/common/transforms.h>
#include <opencv2/opencv.hpp>
#include <Eigen/Dense>
#include <vector>
class PointCloudToDepthConverter
{
public:
struct CameraParams
{
int image_width;
int image_height;
double A11, A12, A22;
double u0, v0;
double k2, k3, k4, k5, k6, k7;
double scale;
int point_sampling_rate;
Eigen::Matrix4d Tcl;
};
struct ProcessResult
{
cv::Mat depth_image;
pcl::PointCloud<pcl::PointXYZRGB> colored_cloud;
bool success;
std::string error_message;
};
explicit PointCloudToDepthConverter(const CameraParams &params);
ProcessResult processCloudAndImage(const pcl::PointCloud<pcl::PointXYZ> &cloud,
const cv::Mat &image);
cv::Mat customResize(const cv::Mat& src, const cv::Size& size);
const CameraParams &getCameraParams() const { return params_; }
void updateCameraParams(const CameraParams &params);
private:
CameraParams params_;
Eigen::Matrix3d K_;
Eigen::Matrix3d Kl_;
Eigen::Matrix4d K_4x4_;
Eigen::Matrix4d Kcl_;
cv::Mat map_x_, map_y_;
cv::Mat inv_map_x_, inv_map_y_;
int scaled_width_, scaled_height_;
void initializeInternalParams();
void createDistortionMaps();
cv::Mat projectCloudToDepth(const pcl::PointCloud<pcl::PointXYZ> &cloud_in_cam);
cv::Mat postProcessDepthImage(const cv::Mat &depth_img);
pcl::PointCloud<pcl::PointXYZRGB> generateColoredCloud(const cv::Mat &depth_img,
const cv::Mat &color_img);
std::pair<bool, std::string> validateInputs(const pcl::PointCloud<pcl::PointXYZ> &cloud,
const cv::Mat &image);
};
@@ -0,0 +1,144 @@
/*
Copyright 2025 Manifold Tech Ltd.(www.manifoldtech.com.co)
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
*/
#pragma once
#include <Eigen/Dense>
#include <cmath>
namespace mini_vikit {
using namespace Eigen;
class PolynomialCamera {
private:
const double fx_, fy_;
const double cx_, cy_;
const double skew_;
bool distortion_;
double k2_, k3_, k4_, k5_, k6_, k7_;
public:
EIGEN_MAKE_ALIGNED_OPERATOR_NEW
PolynomialCamera(double width, double height,
double fx, double fy, double cx, double cy, double skew,
double k2=0.0, double k3=0.0, double k4=0.0,
double k5=0.0, double k6=0.0, double k7=0.0)
: fx_(fx), fy_(fy), cx_(cx), cy_(cy), skew_(skew),
distortion_(std::abs(k2) > 1e-7) {
k2_ = k2; k3_ = k3; k4_ = k4; k5_ = k5; k6_ = k6; k7_ = k7;
}
Vector3d cam2world(const double& u, const double& v) const {
Vector3d xyz;
if (!distortion_) {
double y = (v - cy_) / fy_;
double x = (u - cx_ - y * skew_) / fx_;
xyz << x, y, 1.0;
} else {
double y = (v - cy_) / fy_;
double x = (u - cx_ - y * skew_) / fx_;
const double thetad = std::sqrt(x * x + y * y);
double theta = thetad;
for (int i = 0; i < 7; ++i) {
const double theta2 = theta * theta;
const double theta3 = theta2 * theta;
const double theta4 = theta3 * theta;
const double theta5 = theta4 * theta;
const double theta6 = theta5 * theta;
theta = thetad / (1.0 + k2_ * theta + k3_ * theta2 + k4_ * theta3 +
k5_ * theta4 + k6_ * theta5 + k7_ * theta6);
}
const double scaling = std::tan(theta) / thetad;
x *= scaling;
y *= scaling;
xyz << x, y, 1.0;
}
return xyz.normalized();
}
Vector3d cam2world(const Vector2d& px) const {
return cam2world(px[0], px[1]);
}
Vector2d world2cam(const Vector3d& xyz) const {
Vector2d px;
if (!distortion_) {
px[0] = fx_ * xyz[0] + cx_;
px[1] = fy_ * xyz[1] + cy_;
} else {
double xd, yd;
const double r = std::sqrt(xyz(1) * xyz(1) + xyz(0) * xyz(0));
const double theta = std::acos(xyz(2) / xyz.norm());
const double thetad = thetad_from_theta(theta);
const double scaling = thetad / r;
xd = xyz[0] * scaling;
yd = xyz[1] * scaling;
px[0] = xd * fx_ + yd * skew_ + cx_;
px[1] = yd * fy_ + cy_;
}
return px;
}
Vector2d world2cam(const Vector2d& uv) const {
Vector2d px;
if (!distortion_) {
px[0] = fx_ * uv[0] + cx_;
px[1] = fy_ * uv[1] + cy_;
} else {
double xd, yd;
const double r = uv.norm();
if (r < 1e-8) {
return uv;
}
const double theta = std::atan(r);
const double thetad = thetad_from_theta(theta);
const double scaling = thetad / r;
xd = uv[0] * scaling;
yd = uv[1] * scaling;
px[0] = xd * fx_ + yd * skew_ + cx_;
px[1] = yd * fy_ + cy_;
}
return px;
}
inline double thetad_from_theta(const double theta) const {
const double theta2 = theta * theta;
const double theta3 = theta2 * theta;
const double theta4 = theta3 * theta;
const double theta5 = theta4 * theta;
const double theta6 = theta5 * theta;
const double theta7 = theta6 * theta;
const double thetad = theta + k2_ * theta2 + k3_ * theta3 +
k4_ * theta4 + k5_ * theta5 + k6_ * theta6 + k7_ * theta7;
return thetad;
}
double fx() const { return fx_; }
double fy() const { return fy_; }
double cx() const { return cx_; }
double cy() const { return cy_; }
double skew() const { return skew_; }
bool has_distortion() const { return distortion_; }
double k2() const { return k2_; }
double k3() const { return k3_; }
double k4() const { return k4_; }
double k5() const { return k5_; }
double k6() const { return k6_; }
double k7() const { return k7_; }
};
} // namespace mini_vikit
@@ -0,0 +1,83 @@
/*
Copyright 2025 Manifold Tech Ltd.(www.manifoldtech.com.co)
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
*/
#ifndef RGBCLOUD_H
#define RGBCLOUD_H
#include <vector>
#include <string>
#include <Eigen/Dense>
#include "lidar_api.h"
namespace GlobalCameraParams {
extern float g_fx;
extern float g_fy;
extern float g_cx;
extern float g_cy;
extern float g_skew;
extern float g_k2;
extern float g_k3;
extern float g_k4;
extern float g_k5;
extern float g_k6;
extern float g_k7;
extern Eigen::Matrix4f g_T_camera_lidar;
}
class rawCloudRender {
public:
bool init(const std::string& yamlFilePath);
void nv12buffer_2_rgb(buffer_List_t &image, std::vector<std::vector<float>>& rgb_image);
void render(std::vector<std::vector<float>>& rgb_image, capture_Image_List_t* pcdStream, int pcdIdx, std::vector<float>& rgbCloud_flat);
void print_camera_calib();
int getImageWidth() const { return image_width_; }
int getImageHeight() const { return image_height_; }
private:
std::string model_type_;
std::string camera_name_;
int image_width_;
int image_height_;
int frame_size_;
bool opencv_available_;
// 4x4 transformation matrix (T_camera_lidar)
Eigen::Matrix4f T_camera_lidar_;
float k2_;
float k3_;
float k4_;
float k5_;
float k6_;
float k7_;
float p1_;
float p2_;
float A11_fx_;
float A12_skew_;
float A22_fy_;
float u0_cx_;
float v0_cy_;
bool isFast_;
int numDiff_;
float maxIncidentAngle_;
};
#endif // RGBCLOUD_H
@@ -0,0 +1,110 @@
/*
Copyright 2025 Manifold Tech Ltd.(www.manifoldtech.com.co)
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
*/
#ifndef YAML_PARSER_H
#define YAML_PARSER_H
#include <cstdio>
#include <string>
#include <map>
#include <unordered_set>
#include <vector>
#include <memory>
#include <yaml-cpp/yaml.h>
#include "lidar_api.h"
namespace odin_ros_driver {
// Data type enum for supporting different value types
enum class DataType {
INT_TYPE,
FLOAT_ARRAY_TYPE,
INT_ARRAY_TYPE,
};
// Generic parameter value holder
struct ParameterValue {
DataType type;
std::vector<uint8_t> data;
ParameterValue() : type(DataType::INT_TYPE) {}
template<typename T>
void setData(const T& value) {
const uint8_t* ptr = reinterpret_cast<const uint8_t*>(&value);
data.assign(ptr, ptr + sizeof(T));
}
template<typename T>
void setArray(const std::vector<T>& arr) {
const uint8_t* ptr = reinterpret_cast<const uint8_t*>(arr.data());
data.assign(ptr, ptr + arr.size() * sizeof(T));
}
size_t getSize() const {
return data.size();
}
const void* getData() const {
return data.empty() ? nullptr : data.data();
}
};
class YamlParser {
public:
YamlParser(const std::string& config_file);
bool loadConfig();
const std::map<std::string, int>& getRegisterKeys() const;
const std::map<std::string, std::string>& getRegisterKeysStrVal() const;
const std::map<std::string, ParameterValue>& getCustomParameters() const;
void printConfig() const;
bool applyCustomParameters(device_handle device);
int getCustomParameterInt(const std::string& param_name, int default_value) const;
int getCustomMapMode(int default_value) const {
auto it = custom_parameters_.find("map_mode");
if (it != custom_parameters_.end() && it->second.type == DataType::INT_TYPE) {
printf("custom_map_mode = %d\n", *(int*)it->second.getData());
return *(int*)it->second.getData();
} else {
return default_value;
}
};
private:
std::string config_file_;
std::map<std::string, int> register_keys_;
std::map<std::string, std::string> register_keys_str_val_;
std::map<std::string, double> register_keys_float_val_;
std::map<std::string, ParameterValue> custom_parameters_;
// Keys whose YAML value is a string (not int).
std::unordered_set<std::string> allowed_key_w_str_val = {
"relocalization_map_abs_path",
"mapping_result_dest_dir",
"mapping_result_file_name",
"image_mask_abs_path",
"overlay_reprojected_topic",
"overlay_camera_topic",
"overlay_output_topic"
};
// Keys whose YAML value is a floating-point number (not int).
std::unordered_set<std::string> allowed_key_w_float_val = {
"overlay_alpha"
};
};
}
#endif
@@ -0,0 +1,37 @@
<launch>
<!--
Usage: roslaunch odin_ros_driver odin1_ros1.launch
-->
<!-- Set node name -->
<arg name="node_name" default="host_sdk_sample"/>
<!-- Set parameter file path -->
<arg name="config_file" default="$(find odin_ros_driver)/config/control_command.yaml"/>
<!-- Set RViz configuration file path -->
<arg name="rviz_config" default="$(find odin_ros_driver)/config/odin_ros.rviz"/>
<!-- Launch main node -->
<node name="$(arg node_name)" pkg="odin_ros_driver" type="host_sdk_sample" output="screen">
<param name="config_file" value="$(arg config_file)"/>
</node>
<node pkg="odin_ros_driver" type="pcd2depth_node" name="pcd2depth_node" output="screen" >
<rosparam command="load" file="$(find odin_ros_driver)/config/control_command.yaml"/>
<param name="calib_file_path" value="$(find odin_ros_driver)/config/calib.yaml"/>
</node>
<node pkg="odin_ros_driver" type="cloud_reprojection_node" name="cloud_reprojection_node" output="screen" >
<rosparam command="load" file="$(find odin_ros_driver)/config/control_command.yaml"/>
<param name="calib_file_path" value="$(find odin_ros_driver)/config/calib.yaml"/>
</node>
<!-- Image overlay node - overlays reprojected points on camera image -->
<node pkg="odin_ros_driver" type="image_overlay_node" name="image_overlay_node" output="screen" >
<rosparam command="load" file="$(find odin_ros_driver)/config/control_command.yaml"/>
</node>
<!-- Launch RViz with configuration -->
<node name="rviz" pkg="rviz" type="rviz" args="-d $(arg rviz_config)" output="screen"/>
</launch>
@@ -0,0 +1,111 @@
# USAGE: ros2 launch odin_ros_driver odin1_ros2.launch.py
import os
import yaml
from ament_index_python.packages import get_package_share_directory
from launch import LaunchDescription
from launch.actions import DeclareLaunchArgument
from launch.substitutions import LaunchConfiguration
from launch.conditions import IfCondition
from launch_ros.actions import Node
def generate_launch_description():
# Get package directory
package_dir = get_package_share_directory('odin_ros_driver')
# Declare configuration parameter
config_file_arg = DeclareLaunchArgument(
'config_file',
default_value=os.path.join(package_dir, 'config', 'control_command.yaml'),
description='Path to the control config YAML file'
)
# Add RViz2 configuration file parameter
rviz_config_arg = DeclareLaunchArgument(
'rviz_config',
default_value=os.path.join(package_dir, 'config', 'odin_ros2.rviz'),
description='Path to RViz2 config file'
)
# Declare launch rviz parameter
launch_rviz_arg = DeclareLaunchArgument(
'launch_rviz',
default_value='false',
description='Whether to launch RViz2'
)
# Create main node
host_sdk_node = Node(
package='odin_ros_driver',
executable='host_sdk_sample',
name='host_sdk_sample',
output='screen',
# arguments=['--ros-args', '--log-level', 'debug'],
parameters=[{
'config_file': LaunchConfiguration('config_file')
}]
)
pcd2depth_config_path = os.path.join(package_dir, 'config', 'control_command.yaml')
with open(pcd2depth_config_path, 'r') as f:
pcd2depth_params = yaml.safe_load(f)
pcd2depth_calib_path = os.path.join(package_dir, 'config', 'calib.yaml')
pcd2depth_params['calib_file_path'] = pcd2depth_calib_path
pcd2depth_node = Node(
package='odin_ros_driver',
executable='pcd2depth_ros2_node',
name='pcd2depth_ros2_node',
output='screen',
parameters=[pcd2depth_params]
)
# Cloud reprojection node
reprojection_config_path = os.path.join(package_dir, 'config', 'control_command.yaml')
with open(reprojection_config_path, 'r') as f:
reprojection_params = yaml.safe_load(f)
reprojection_calib_path = os.path.join(package_dir, 'config', 'calib.yaml')
reprojection_params['calib_file_path'] = reprojection_calib_path
cloud_reprojection_node = Node(
package='odin_ros_driver',
executable='cloud_reprojection_ros2_node',
name='cloud_reprojection_ros2_node',
output='screen',
parameters=[reprojection_params]
)
# Image overlay node - overlays reprojected points on camera image
overlay_config_path = os.path.join(package_dir, 'config', 'control_command.yaml')
with open(overlay_config_path, 'r') as f:
overlay_params = yaml.safe_load(f)
image_overlay_node = Node(
package='odin_ros_driver',
executable='image_overlay_node',
name='image_overlay_node',
output='screen',
parameters=[overlay_params]
)
# Create RViz2 node - loads specified configuration file
rviz_node = Node(
package='rviz2',
executable='rviz2',
name='rviz2',
output='screen',
arguments=['-d', LaunchConfiguration('rviz_config')],
condition=IfCondition(LaunchConfiguration('launch_rviz'))
)
# Create launch description
ld = LaunchDescription()
ld.add_action(config_file_arg)
ld.add_action(rviz_config_arg) # Add RViz configuration argument
ld.add_action(launch_rviz_arg) # Add launch_rviz argument
ld.add_action(host_sdk_node)
ld.add_action(pcd2depth_node)
ld.add_action(cloud_reprojection_node)
ld.add_action(image_overlay_node)
ld.add_action(rviz_node) # Add RViz node
return ld
@@ -0,0 +1,28 @@
<?xml version="1.0"?>
<package format="3">
<name>odin_ros_driver</name>
<version>0.0.1</version>
<description>ROS2 driver for Odin sensor</description>
<maintainer email="oliveiratang@manifoldtech.cn">rlk</maintainer>
<license>Apache 2.0</license>
<!-- ROS2 uses colcon as the build tool -->
<buildtool_depend>ament_cmake</buildtool_depend>
<!-- ROS2 dependencies -->
<depend>rclcpp</depend>
<!-- System dependencies -->
<depend>std_msgs</depend>
<depend>sensor_msgs</depend>
<depend>nav_msgs</depend>
<depend>geometry_msgs</depend>
<depend>cv_bridge</depend>
<depend>image_transport</depend>
<depend>pcl_conversions</depend>
<depend>message_filters</depend>
<depend>tf2</depend>
<depend>tf2_ros</depend>
<depend>tf2_geometry_msgs</depend>
<!-- Specify build type as ament -->
<export>
<build_type>ament_cmake</build_type>
</export>
</package>
@@ -0,0 +1,29 @@
<?xml version="1.0"?>
<package format="3">
<name>odin_ros_driver</name>
<version>0.0.1</version>
<description>ROS driver for Odin sensor</description>
<maintainer email="oliveiratang@manifoldtech.cn">rlk</maintainer>
<license>Apache 2.0</license>
<!-- ROS1 uses catkin as the build tool -->
<buildtool_depend>catkin</buildtool_depend>
<!-- ROS1 dependencies -->
<depend>roscpp</depend>
<depend>std_msgs</depend>
<depend>sensor_msgs</depend>
<depend>nav_msgs</depend>
<depend>cv_bridge</depend>
<depend>image_transport</depend>
<!-- System dependencies -->
<depend>eigen</depend>
<depend>opencv</depend>
<depend>yaml-cpp</depend>
<!-- Specify build type as catkin -->
<export>
<build_type>catkin</build_type>
</export>
</package>
@@ -0,0 +1,28 @@
<?xml version="1.0"?>
<package format="3">
<name>odin_ros_driver</name>
<version>0.0.1</version>
<description>ROS2 driver for Odin sensor</description>
<maintainer email="oliveiratang@manifoldtech.cn">rlk</maintainer>
<license>Apache 2.0</license>
<!-- ROS2 uses colcon as the build tool -->
<buildtool_depend>ament_cmake</buildtool_depend>
<!-- ROS2 dependencies -->
<depend>rclcpp</depend>
<!-- System dependencies -->
<depend>std_msgs</depend>
<depend>sensor_msgs</depend>
<depend>nav_msgs</depend>
<depend>geometry_msgs</depend>
<depend>cv_bridge</depend>
<depend>image_transport</depend>
<depend>pcl_conversions</depend>
<depend>message_filters</depend>
<depend>tf2</depend>
<depend>tf2_ros</depend>
<depend>tf2_geometry_msgs</depend>
<!-- Specify build type as ament -->
<export>
<build_type>ament_cmake</build_type>
</export>
</package>
@@ -0,0 +1,128 @@
#!/bin/bash
# Get the directory where the script is located (Odin_ROS_Driver directory)
PKG_DIR="$(cd "$(dirname "$0")/.."; pwd)"
# Calculate the workspace root directory (contains devel, build, src)
WORKSPACE_ROOT="$(dirname "$(dirname "$PKG_DIR")")"
# Workspace source directory (contains all packages)
WORKSPACE_SRC="${WORKSPACE_ROOT}/src"
PROJECT_NAME="odin_ros_driver"
# Define color codes
RED='\033[0;31m'
GREEN='\033[0;32m'
YELLOW='\033[1;33m'
NC='\033[0m'
# Clean workspace function
clean_workspace() {
echo -e "${YELLOW}Cleaning build directories${NC}"
# Clean build artifacts in workspace
rm -rf "${WORKSPACE_ROOT}/build"
rm -rf "${WORKSPACE_ROOT}/install"
rm -rf "${WORKSPACE_ROOT}/log"
rm -rf "${WORKSPACE_ROOT}/devel"
echo -e "${GREEN}Cleanup complete${NC}"
}
# Run node function
run_node() {
echo -e "${YELLOW}Running ROS1 node${NC}"
# Check if environment file exists
if [ ! -f "${WORKSPACE_ROOT}/devel/setup.bash" ]; then
echo -e "${RED}Could not find devel/setup.bash, please build the project with ./build_ros1.sh first${NC}"
return 1
fi
# Source environment and run node
source "${WORKSPACE_ROOT}/devel/setup.bash"
}
# Build workspace function
build_workspace() {
echo -e "${YELLOW}Workspace structure:${NC}"
echo " Workspace root: ${WORKSPACE_ROOT}"
echo " Source directory: ${WORKSPACE_SRC}"
echo " Package directory: ${PKG_DIR}"
echo " ROS version: ROS1"
echo -e "${YELLOW}Starting ROS1 project build...${NC}"
# Clean
cd $WS_DIR
rm -rf build devel install
# Ensure ROS1 environment is loaded
if [ -f "/opt/ros/noetic/setup.bash" ]; then
source "/opt/ros/noetic/setup.bash"
elif [ -f "/opt/ros/melodic/setup.bash" ]; then
source "/opt/ros/melodic/setup.bash"
else
echo -e "${RED}Could not find ROS1 setup.bash file. Please ensure ROS1 is installed.${NC}"
return 1
fi
# Create temporary package.xml
if [ -f "${PKG_DIR}/package_ros1.xml" ]; then
echo "Creating temporary package.xml (using package_ros1.xml)"
cp "${PKG_DIR}/package_ros1.xml" "${PKG_DIR}/package.xml"
TEMP_PACKAGE=true
elif [ -f "${PKG_DIR}/package.xml" ]; then
echo "Using existing package.xml"
else
echo -e "${RED}Could not find package.xml in package directory${NC}"
return 1
fi
# Set build system variable
export BUILD_SYSTEM=ROS1
# Switch to workspace root and build
cd "${WORKSPACE_ROOT}" || return 1
catkin_make -DBUILD_SYSTEM=ROS1 -DCMAKE_EXPORT_COMPILE_COMMANDS=ON -j$(nproc)
BUILD_RESULT=$?
# If build successful, source environment
if [[ $BUILD_RESULT -eq 0 ]]; then
echo -e "${GREEN}ROS1 build successful, loading environment: source devel/setup.bash${NC}"
source "${WORKSPACE_ROOT}/devel/setup.bash"
else
echo -e "${RED}ROS1 build failed, please check error logs${NC}"
fi
}
# Help function
show_help() {
echo -e "${YELLOW}Usage:${NC}"
echo " ./build_ros.sh # Build project"
echo " ./build_ros.sh -c # Clean build artifacts"
echo " ./build_ros.sh -h # Show help information"
echo ""
echo -e "${YELLOW}Current configuration:${NC}"
echo " Project name: ${PROJECT_NAME}"
echo " Package directory: ${PKG_DIR}"
echo " Workspace root: ${WORKSPACE_ROOT}"
echo " Source directory: ${WORKSPACE_SRC}"
}
# Main
case "$1" in
-c|--clean)
clean_workspace
;;
-r|--run)
run_node
;;
-h|--help)
show_help
;;
*)
build_workspace
;;
esac
@@ -0,0 +1,158 @@
#!/bin/bash
# Get the directory where the script is located (Odin_ROS_Driver directory)
PKG_DIR="$(cd "$(dirname "$0")/.."; pwd)"
# Calculate the workspace root directory (contains devel, build, src)
WORKSPACE_ROOT="$(dirname "$(dirname "$PKG_DIR")")"
# Workspace source directory (contains all packages)
WORKSPACE_SRC="${WORKSPACE_ROOT}/src"
PROJECT_NAME="odin_ros_driver"
PACKAGE_DIR_NAME=$(basename "$PKG_DIR")
# Define color codes
RED='\033[0;31m'
GREEN='\033[0;32m'
YELLOW='\033[1;33m'
NC='\033[0m'
# Extract package name from package.xml
get_package_name() {
local package_xml="$1"
if [ -f "$package_xml" ]; then
# Extract content of <name> tag
grep -oP '<name>\K[^<]+' "$package_xml" | head -1
else
echo ""
fi
}
# Clean workspace function
clean_workspace() {
echo -e "${YELLOW}Cleaning build directories${NC}"
# Clean build artifacts in workspace
rm -rf "${WORKSPACE_ROOT}/build"
rm -rf "${WORKSPACE_ROOT}/install"
rm -rf "${WORKSPACE_ROOT}/log"
rm -rf "${WORKSPACE_ROOT}/devel"
echo -e "${GREEN}Cleanup complete${NC}"
}
# Run node function
run_node() {
echo -e "${YELLOW}Running ROS2 node${NC}"
# Check if environment file exists
if [ ! -f "${WORKSPACE_ROOT}/install/setup.bash" ]; then
echo -e "${RED}Could not find install/setup.bash, please build the project with ./build_ros2.sh first${NC}"
return 1
fi
# Source environment and run node
source "${WORKSPACE_ROOT}/install/setup.bash"
}
# Build workspace function
build_workspace() {
echo -e "${YELLOW}Workspace structure:${NC}"
echo " Workspace root: ${WORKSPACE_ROOT}"
echo " Source directory: ${WORKSPACE_SRC}"
echo " Package directory: ${PKG_DIR}"
echo " Directory name: ${PACKAGE_DIR_NAME}"
echo " ROS version: ROS2"
echo -e "${YELLOW}Starting ROS2 project build...${NC}"
cd $WS_DIR
rm -rf build install log
# Ensure ROS2 environment is loaded
if [ -f "/opt/ros/foxy/setup.bash" ]; then
source "/opt/ros/foxy/setup.bash"
elif [ -f "/opt/ros/galactic/setup.bash" ]; then
source "/opt/ros/galactic/setup.bash"
elif [ -f "/opt/ros/humble/setup.bash" ]; then
source "/opt/ros/humble/setup.bash"
else
echo -e "${RED}Could not find ROS2 setup.bash file. Please ensure ROS2 is installed.${NC}"
return 1
fi
# Create temporary package.xml
if [ -f "${PKG_DIR}/package_ros2.xml" ]; then
echo "Creating temporary package.xml (using package_ros2.xml)"
cp "${PKG_DIR}/package_ros2.xml" "${PKG_DIR}/package.xml"
TEMP_PACKAGE=true
elif [ -f "${PKG_DIR}/package.xml" ]; then
echo "Using existing package.xml"
TEMP_PACKAGE=false
else
echo -e "${RED}Could not find package.xml in package directory${NC}"
return 1
fi
# Extract package name from package.xml
PACKAGE_NAME=$(get_package_name "${PKG_DIR}/package.xml")
if [ -z "$PACKAGE_NAME" ]; then
echo -e "${RED}Failed to extract package name from package.xml${NC}"
return 1
fi
echo " Package name: ${PACKAGE_NAME}"
# Set build system variable
export BUILD_SYSTEM=ROS2
# Switch to workspace root and build
cd "${WORKSPACE_ROOT}" || return 1
# Build with correct package name
colcon build \
--packages-select "${PACKAGE_NAME}" \
--parallel-workers $(nproc) \
--cmake-args \
-DBUILD_SYSTEM=ROS2 \
-DCMAKE_EXPORT_COMPILE_COMMANDS=ON
BUILD_RESULT=$?
# If build successful, source environment
if [[ $BUILD_RESULT -eq 0 ]]; then
echo -e "${GREEN}ROS2 build successful, loading environment: source install/setup.bash${NC}"
source "${WORKSPACE_ROOT}/install/setup.bash"
else
echo -e "${RED}ROS2 build failed, please check error logs${NC}"
fi
}
# Help function
show_help() {
echo -e "${YELLOW}Usage:${NC}"
echo " ./build_ros2.sh # Build project"
echo " ./build_ros2.sh -c # Clean build artifacts"
echo " ./build_ros2.sh -h # Show help information"
echo ""
echo -e "${YELLOW}Current configuration:${NC}"
echo " Project name: ${PROJECT_NAME}"
echo " Package directory: ${PKG_DIR}"
echo " Workspace root: ${WORKSPACE_ROOT}"
echo " Source directory: ${WORKSPACE_SRC}"
}
# Main program
case "$1" in
-c|--clean)
clean_workspace
;;
-r|--run)
run_node
;;
-h|--help)
show_help
;;
*)
build_workspace
;;
esac
@@ -0,0 +1,21 @@
#!/bin/bash
# Usage: ./set_param.sh <parameter_name> <value>
# Example: ./set_param.sh save_map 1
if [ $# -ne 2 ]; then
echo "Usage: $0 <parameter_name> <value>"
echo "Example: $0 save_map 1"
exit 1
fi
PARAM_NAME=$1
VALUE=$2
COMMAND_FILE="/tmp/odin_command.txt"
# Create the command file with the parameter
echo "set $PARAM_NAME $VALUE" > "$COMMAND_FILE"
echo "Command sent: set $PARAM_NAME $VALUE"
echo "Command file: $COMMAND_FILE"
@@ -0,0 +1,232 @@
/*
Copyright 2025 Manifold Tech Ltd.(www.manifoldtech.com.co)
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
*/
#include "camera_pose_visualization.h"
const Eigen::Vector3d camera_pose_visualization::imlt = Eigen::Vector3d(-1.0, -0.5, 1.0);
const Eigen::Vector3d camera_pose_visualization::imrt = Eigen::Vector3d( 1.0, -0.5, 1.0);
const Eigen::Vector3d camera_pose_visualization::imlb = Eigen::Vector3d(-1.0, 0.5, 1.0);
const Eigen::Vector3d camera_pose_visualization::imrb = Eigen::Vector3d( 1.0, 0.5, 1.0);
const Eigen::Vector3d camera_pose_visualization::lt0 = Eigen::Vector3d(-0.7, -0.5, 1.0);
const Eigen::Vector3d camera_pose_visualization::lt1 = Eigen::Vector3d(-0.7, -0.2, 1.0);
const Eigen::Vector3d camera_pose_visualization::lt2 = Eigen::Vector3d(-1.0, -0.2, 1.0);
const Eigen::Vector3d camera_pose_visualization::oc = Eigen::Vector3d(0.0, 0.0, 0.0);
#ifdef ROS2
using GeometryPoint = geometry_msgs::msg::Point;
#else
using GeometryPoint = geometry_msgs::Point;
#endif
void Eigen2Point(const Eigen::Vector3d& v, GeometryPoint& p) {
p.x = v.x();
p.y = v.y();
p.z = v.z();
}
camera_pose_visualization::camera_pose_visualization(float r, float g, float b, float a)
: m_marker_ns("camera_pose_visualization"), m_scale(0.3), m_line_width(0.03) {
m_image_boundary_color.r = r;
m_image_boundary_color.g = g;
m_image_boundary_color.b = b;
m_image_boundary_color.a = a;
m_optical_center_connector_color.r = r;
m_optical_center_connector_color.g = g;
m_optical_center_connector_color.b = b;
m_optical_center_connector_color.a = a;
}
void camera_pose_visualization::setImageBoundaryColor(float r, float g, float b, float a) {
m_image_boundary_color.r = r;
m_image_boundary_color.g = g;
m_image_boundary_color.b = b;
m_image_boundary_color.a = a;
}
void camera_pose_visualization::setOpticalCenterConnectorColor(float r, float g, float b, float a) {
m_optical_center_connector_color.r = r;
m_optical_center_connector_color.g = g;
m_optical_center_connector_color.b = b;
m_optical_center_connector_color.a = a;
}
void camera_pose_visualization::setScale(double s) {
m_scale = s;
}
void camera_pose_visualization::setLineWidth(double width) {
m_line_width = width;
}
void camera_pose_visualization::add_edge(const Eigen::Vector3d& p0, const Eigen::Vector3d& p1) {
Marker marker;
marker.ns = m_marker_ns;
marker.id = m_markers.size() + 1;
#ifdef ROS2
marker.type = Marker::LINE_LIST;
marker.action = Marker::ADD;
#else
marker.type = visualization_msgs::Marker::LINE_LIST;
marker.action = visualization_msgs::Marker::ADD;
#endif
marker.scale.x = 0.005;
marker.color.g = 1.0f;
marker.color.a = 1.0;
GeometryPoint point0, point1;
Eigen2Point(p0, point0);
Eigen2Point(p1, point1);
marker.points.push_back(point0);
marker.points.push_back(point1);
m_markers.push_back(marker);
}
void camera_pose_visualization::add_loopedge(const Eigen::Vector3d& p0, const Eigen::Vector3d& p1) {
Marker marker;
marker.ns = m_marker_ns;
marker.id = m_markers.size() + 1;
#ifdef ROS2
marker.type = Marker::LINE_LIST;
marker.action = Marker::ADD;
#else
marker.type = visualization_msgs::Marker::LINE_LIST;
marker.action = visualization_msgs::Marker::ADD;
#endif
marker.scale.x = 0.04;
//marker.scale.x = 0.3;
marker.color.r = 1.0f;
marker.color.b = 1.0f;
marker.color.a = 1.0;
GeometryPoint point0, point1;
Eigen2Point(p0, point0);
Eigen2Point(p1, point1);
marker.points.push_back(point0);
marker.points.push_back(point1);
m_markers.push_back(marker);
}
void camera_pose_visualization::add_pose(const Eigen::Vector3d& p, const Eigen::Quaterniond& q) {
Marker marker;
marker.ns = m_marker_ns;
marker.id = m_markers.size() + 1;
#ifdef ROS2
marker.type = Marker::LINE_STRIP;
marker.action = Marker::ADD;
#else
marker.type = visualization_msgs::Marker::LINE_STRIP;
marker.action = visualization_msgs::Marker::ADD;
#endif
marker.scale.x = m_line_width;
marker.pose.position.x = 0.0;
marker.pose.position.y = 0.0;
marker.pose.position.z = 0.0;
marker.pose.orientation.w = 1.0;
marker.pose.orientation.x = 0.0;
marker.pose.orientation.y = 0.0;
marker.pose.orientation.z = 0.0;
GeometryPoint pt_lt, pt_lb, pt_rt, pt_rb, pt_oc, pt_lt0, pt_lt1, pt_lt2;
Eigen2Point(q * (m_scale * imlt) + p, pt_lt);
Eigen2Point(q * (m_scale * imlb) + p, pt_lb);
Eigen2Point(q * (m_scale * imrt) + p, pt_rt);
Eigen2Point(q * (m_scale * imrb) + p, pt_rb);
Eigen2Point(q * (m_scale * lt0 ) + p, pt_lt0);
Eigen2Point(q * (m_scale * lt1 ) + p, pt_lt1);
Eigen2Point(q * (m_scale * lt2 ) + p, pt_lt2);
Eigen2Point(q * (m_scale * oc ) + p, pt_oc);
// image boundaries
marker.points.push_back(pt_lt);
marker.points.push_back(pt_lb);
marker.colors.push_back(m_image_boundary_color);
marker.colors.push_back(m_image_boundary_color);
marker.points.push_back(pt_lb);
marker.points.push_back(pt_rb);
marker.colors.push_back(m_image_boundary_color);
marker.colors.push_back(m_image_boundary_color);
marker.points.push_back(pt_rb);
marker.points.push_back(pt_rt);
marker.colors.push_back(m_image_boundary_color);
marker.colors.push_back(m_image_boundary_color);
marker.points.push_back(pt_rt);
marker.points.push_back(pt_lt);
marker.colors.push_back(m_image_boundary_color);
marker.colors.push_back(m_image_boundary_color);
// top-left indicator
marker.points.push_back(pt_lt0);
marker.points.push_back(pt_lt1);
marker.colors.push_back(m_image_boundary_color);
marker.colors.push_back(m_image_boundary_color);
marker.points.push_back(pt_lt1);
marker.points.push_back(pt_lt2);
marker.colors.push_back(m_image_boundary_color);
marker.colors.push_back(m_image_boundary_color);
// optical center connector
marker.points.push_back(pt_lt);
marker.points.push_back(pt_oc);
marker.colors.push_back(m_optical_center_connector_color);
marker.colors.push_back(m_optical_center_connector_color);
marker.points.push_back(pt_lb);
marker.points.push_back(pt_oc);
marker.colors.push_back(m_optical_center_connector_color);
marker.colors.push_back(m_optical_center_connector_color);
marker.points.push_back(pt_rt);
marker.points.push_back(pt_oc);
marker.colors.push_back(m_optical_center_connector_color);
marker.colors.push_back(m_optical_center_connector_color);
marker.points.push_back(pt_rb);
marker.points.push_back(pt_oc);
marker.colors.push_back(m_optical_center_connector_color);
marker.colors.push_back(m_optical_center_connector_color);
m_markers.push_back(marker);
}
void camera_pose_visualization::reset() {
m_markers.clear();
}
void camera_pose_visualization::publish_by( Publisher& pub, const Header& header ) {
MarkerArray markerArray_msg;
for (auto& marker : m_markers) {
marker.header = header;
markerArray_msg.markers.push_back(marker);
}
pub.publish(markerArray_msg);
}
@@ -0,0 +1,503 @@
/*
Copyright 2025 Manifold Tech Ltd.(www.manifoldtech.com.co)
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
*/
#include "cloud_reprojection_ros_node.hpp"
#include <fstream>
#include <sys/stat.h>
#include <thread>
#include <chrono>
#ifdef ROS2
#include <functional>
#include <yaml-cpp/yaml.h>
#include <rcpputils/filesystem_helper.hpp>
#else
#include <boost/bind.hpp>
#endif
// Fixed Til (T_imu_lidar): lidar position in imu frame, transforms from lidar to imu
// TODO: Fill in the actual Til values for your sensor setup
static Eigen::Matrix4d getFixedTil()
{
Eigen::Matrix4d Til = Eigen::Matrix4d::Identity();
Til(0, 3) = -0.02663;
Til(1, 3) = 0.03447;
Til(2, 3) = 0.02174;
return Til;
}
static bool fileExists(const std::string& filename) {
struct stat buffer;
return (stat(filename.c_str(), &buffer) == 0);
}
#ifdef ROS2
// Helper function to get package source directory for ROS2
static std::string get_package_source_directory() {
std::string current_file = __FILE__;
size_t pos = current_file.find("/src/cloud_reprojection_ros.cpp");
if (pos != std::string::npos) {
return current_file.substr(0, pos);
}
return "";
}
// ==================== ROS2 Implementation ====================
CloudReprojectionRosNode::CloudReprojectionRosNode(const rclcpp::NodeOptions& options)
: Node("cloud_reprojection_node", options)
{
loadParameters();
RCLCPP_INFO_STREAM(this->get_logger(),
"\n cloud_slam_topic: " << cloud_slam_topic_
<< "\n odometry_topic: " << odometry_topic_
<< "\n wiwc_topic: " << wiwc_topic_
<< "\n reprojected_image_topic: " << reprojected_image_topic_);
cloud_sub_.subscribe(this, cloud_slam_topic_);
odom_sub_.subscribe(this, odometry_topic_);
wiwc_sub_.subscribe(this, wiwc_topic_);
sync_ = std::make_shared<Sync>(MySyncPolicy(10), cloud_sub_, odom_sub_, wiwc_sub_);
sync_->registerCallback(std::bind(&CloudReprojectionRosNode::syncCallback, this,
std::placeholders::_1, std::placeholders::_2, std::placeholders::_3));
reprojected_image_pub_ = image_transport::create_publisher(this, reprojected_image_topic_);
RCLCPP_INFO(this->get_logger(), "CloudReprojectionRosNode initialized successfully");
}
void CloudReprojectionRosNode::loadParameters()
{
// Declare and get parameters
this->declare_parameter<std::string>("cloud_slam_topic", "/odin1/cloud_slam");
this->declare_parameter<std::string>("odometry_topic", "/odin1/odometry");
this->declare_parameter<std::string>("wiwc_topic", "/odin1/wiwc");
this->declare_parameter<std::string>("reprojected_image_topic", "/odin1/reprojected_image");
cloud_slam_topic_ = this->get_parameter("cloud_slam_topic").as_string();
odometry_topic_ = this->get_parameter("odometry_topic").as_string();
wiwc_topic_ = this->get_parameter("wiwc_topic").as_string();
reprojected_image_topic_ = this->get_parameter("reprojected_image_topic").as_string();
// Load camera parameters from calib.yaml file directly
std::string package_path = get_package_source_directory();
std::string calib_file = package_path + "/config/calib.yaml";
YAML::Node calib_config;
try {
calib_config = YAML::LoadFile(calib_file);
} catch (const std::exception& e) {
RCLCPP_ERROR(this->get_logger(), "Failed to load calib.yaml: %s", e.what());
rclcpp::shutdown();
return;
}
CloudReprojector::CameraParams cam_params;
try {
cam_params.image_width = calib_config["cam_0"]["image_width"].as<int>();
cam_params.image_height = calib_config["cam_0"]["image_height"].as<int>();
cam_params.A11 = calib_config["cam_0"]["A11"].as<double>();
cam_params.A12 = calib_config["cam_0"]["A12"].as<double>();
cam_params.A22 = calib_config["cam_0"]["A22"].as<double>();
cam_params.u0 = calib_config["cam_0"]["u0"].as<double>();
cam_params.v0 = calib_config["cam_0"]["v0"].as<double>();
cam_params.k2 = calib_config["cam_0"]["k2"].as<double>();
cam_params.k3 = calib_config["cam_0"]["k3"].as<double>();
cam_params.k4 = calib_config["cam_0"]["k4"].as<double>();
cam_params.k5 = calib_config["cam_0"]["k5"].as<double>();
cam_params.k6 = calib_config["cam_0"]["k6"].as<double>();
cam_params.k7 = calib_config["cam_0"]["k7"].as<double>();
} catch (const std::exception& e) {
RCLCPP_ERROR(this->get_logger(), "Failed to parse camera parameters: %s", e.what());
rclcpp::shutdown();
return;
}
// Load extrinsic parameters
CloudReprojector::ExtrinsicParams ext_params;
try {
auto Tcl_vec = calib_config["Tcl_0"].as<std::vector<double>>();
if (Tcl_vec.size() == 16)
{
for (int i = 0; i < 4; ++i)
for (int j = 0; j < 4; ++j)
ext_params.Tcl(i, j) = Tcl_vec[i * 4 + j];
}
else
{
RCLCPP_ERROR(this->get_logger(), "Tcl_0 has invalid size: %zu (expected 16)", Tcl_vec.size());
rclcpp::shutdown();
return;
}
} catch (const std::exception& e) {
RCLCPP_ERROR(this->get_logger(), "Failed to parse Tcl_0: %s", e.what());
rclcpp::shutdown();
return;
}
ext_params.Til = getFixedTil();
ext_params.Tic = CloudReprojector::calculateTic(ext_params.Tcl, ext_params.Til);
RCLCPP_INFO_STREAM(this->get_logger(), "Loaded Tcl (camera to lidar):\n" << ext_params.Tcl);
RCLCPP_INFO_STREAM(this->get_logger(), "Fixed Til (lidar to imu):\n" << ext_params.Til);
RCLCPP_INFO_STREAM(this->get_logger(), "Calculated Tic:\n" << ext_params.Tic);
RCLCPP_INFO(this->get_logger(), "Camera intrinsics:");
RCLCPP_INFO(this->get_logger(), "Image size: %dx%d", cam_params.image_width, cam_params.image_height);
RCLCPP_INFO(this->get_logger(), "Intrinsics: A11=%f A12=%f A22=%f u0=%f v0=%f",
cam_params.A11, cam_params.A12, cam_params.A22, cam_params.u0, cam_params.v0);
reprojector_ = std::make_unique<CloudReprojector>();
if (!reprojector_->initialize(cam_params, ext_params))
{
RCLCPP_ERROR(this->get_logger(), "Failed to initialize CloudReprojector");
rclcpp::shutdown();
}
}
void CloudReprojectionRosNode::syncCallback(
const PointCloud2::ConstSharedPtr& cloud_msg,
const Odometry::ConstSharedPtr& odom_msg,
const Odometry::ConstSharedPtr& wiwc_msg)
{
// Debug: print that syncCallback is called
static int sync_count = 0;
// RCLCPP_INFO(this->get_logger(), "=== syncCallback called, count: %d ===", ++sync_count);
pcl::PointCloud<pcl::PointXYZRGB> cloud_odom;
pcl::fromROSMsg(*cloud_msg, cloud_odom);
if (cloud_odom.empty())
{
RCLCPP_WARN(this->get_logger(), "Empty cloud_slam received");
return;
}
// Extract real-time extrinsics from WIWC message covariance fields
// pose.covariance contains T_CL (first 16 values), twist.covariance contains T_IL (first 16 values)
Eigen::Matrix4d T_CL = Eigen::Matrix4d::Identity();
Eigen::Matrix4d T_IL = Eigen::Matrix4d::Identity();
for (int i = 0; i < 16; ++i) {
T_CL(i / 4, i % 4) = wiwc_msg->pose.covariance[i];
T_IL(i / 4, i % 4) = wiwc_msg->twist.covariance[i];
}
// Update extrinsics if valid (not identity matrix)
bool T_CL_valid = (T_CL - Eigen::Matrix4d::Identity()).norm() > 1e-6;
bool T_IL_valid = (T_IL - Eigen::Matrix4d::Identity()).norm() > 1e-6;
// // Debug print to compare with host_sdk_sample values
// static int print_count = 0;
// if (print_count++) {
// // Extract rotation (3x3) and translation (3x1) from T_CL
// Eigen::Matrix3d RCL = T_CL.block<3,3>(0,0);
// Eigen::Vector3d TCL = T_CL.block<3,1>(0,3);
// RCLCPP_INFO_STREAM(this->get_logger(), "=== cloud_reprojection RCL (3x3 rotation from T_CL) ===\n" << RCL);
// RCLCPP_INFO_STREAM(this->get_logger(), "=== cloud_reprojection TCL (3x1 translation from T_CL) ===\n" << TCL.transpose());
// // Extract rotation (3x3) and translation (3x1) from T_IL
// Eigen::Matrix3d RIL = T_IL.block<3,3>(0,0);
// Eigen::Vector3d TIL = T_IL.block<3,1>(0,3);
// RCLCPP_INFO_STREAM(this->get_logger(), "=== cloud_reprojection RIL (3x3 rotation from T_IL) ===\n" << RIL);
// RCLCPP_INFO_STREAM(this->get_logger(), "=== cloud_reprojection TIL (3x1 translation from T_IL) ===\n" << TIL.transpose());
// RCLCPP_INFO(this->get_logger(), "T_CL_valid: %d, T_IL_valid: %d", T_CL_valid, T_IL_valid);
// if (T_CL_valid && T_IL_valid) {
// Eigen::Matrix4d Tic = CloudReprojector::calculateTic(T_CL, T_IL);
// RCLCPP_INFO_STREAM(this->get_logger(), "=== cloud_reprojection calculated Tic ===\n" << Tic);
// }
// }
if (T_CL_valid && T_IL_valid) {
reprojector_->updateExtrinsics(T_CL, T_IL);
}
CloudReprojector::OdomPose odom_pose;
odom_pose.orientation = Eigen::Quaterniond(
odom_msg->pose.pose.orientation.w,
odom_msg->pose.pose.orientation.x,
odom_msg->pose.pose.orientation.y,
odom_msg->pose.pose.orientation.z
);
odom_pose.position = Eigen::Vector3d(
odom_msg->pose.pose.position.x,
odom_msg->pose.pose.position.y,
odom_msg->pose.pose.position.z
);
cv::Mat reprojected_img = reprojector_->reprojectCloud(cloud_odom, odom_pose);
auto img_msg = cv_bridge::CvImage(cloud_msg->header, "bgr8", reprojected_img).toImageMsg();
reprojected_image_pub_.publish(*img_msg);
}
// ==================== ROS2 Main ====================
int main(int argc, char** argv)
{
rclcpp::init(argc, argv);
auto temp_node = std::make_shared<rclcpp::Node>("cloud_reprojection_check");
// Check if reprojection is enabled from control_command.yaml
std::string package_path = get_package_source_directory();
std::string config_file = package_path + "/config/control_command.yaml";
try {
YAML::Node config = YAML::LoadFile(config_file);
std::cout << "config: " << config_file << std::endl;
if (!config["register_keys"] || !config["register_keys"]["sendreprojection"]) {
RCLCPP_INFO(temp_node->get_logger(), "sendreprojection parameter not found, cloud reprojection disabled.");
rclcpp::shutdown();
return 0;
}
int sendreprojection = config["register_keys"]["sendreprojection"].as<int>();
if (sendreprojection == 0) {
RCLCPP_INFO(temp_node->get_logger(), "Cloud reprojection will not be published.");
rclcpp::shutdown();
return 0;
}
} catch (const std::exception& e) {
RCLCPP_ERROR(temp_node->get_logger(), "Failed to read config: %s", e.what());
rclcpp::shutdown();
return 1;
}
// Wait for calib.yaml file to be generated by host_sdk_sample
std::string calib_file = package_path + "/config/calib.yaml";
RCLCPP_INFO(temp_node->get_logger(), "Waiting for calib.yaml file at: %s", calib_file.c_str());
int wait_count = 0;
while (rclcpp::ok() && !fileExists(calib_file)) {
if (wait_count % 10 == 0) {
RCLCPP_INFO(temp_node->get_logger(), "Still waiting for calib.yaml file...");
}
std::this_thread::sleep_for(std::chrono::milliseconds(5000));
wait_count++;
// Timeout after 5 seconds
if (wait_count > 10) {
RCLCPP_ERROR(temp_node->get_logger(), "Timeout waiting for calib.yaml file");
rclcpp::shutdown();
return 1;
}
}
if (!rclcpp::ok()) {
RCLCPP_INFO(temp_node->get_logger(), "Node shutdown before calib.yaml file was found.");
return 0;
}
RCLCPP_INFO(temp_node->get_logger(), "Found calib.yaml file! Starting cloud reprojection node...");
auto node = std::make_shared<CloudReprojectionRosNode>();
RCLCPP_INFO(node->get_logger(), "CloudReprojectionRosNode started");
rclcpp::spin(node);
rclcpp::shutdown();
return 0;
}
#else
// ==================== ROS1 Implementation ====================
CloudReprojectionRosNode::CloudReprojectionRosNode(ros::NodeHandle& nh, ros::NodeHandle& pnh)
: nh_(nh), pnh_(pnh)
{
loadParameters();
ROS_INFO_STREAM("\n cloud_slam_topic: " << cloud_slam_topic_
<< "\n odometry_topic: " << odometry_topic_
<< "\n wiwc_topic: " << wiwc_topic_
<< "\n reprojected_image_topic: " << reprojected_image_topic_);
cloud_sub_.subscribe(nh_, cloud_slam_topic_, 1);
odom_sub_.subscribe(nh_, odometry_topic_, 1);
wiwc_sub_.subscribe(nh_, wiwc_topic_, 1);
sync_ = std::make_shared<Sync>(MySyncPolicy(10), cloud_sub_, odom_sub_, wiwc_sub_);
sync_->registerCallback(boost::bind(&CloudReprojectionRosNode::syncCallback, this, _1, _2, _3));
reprojected_image_pub_ = nh_.advertise<sensor_msgs::Image>(reprojected_image_topic_, 1);
ROS_INFO("CloudReprojectionRosNode initialized successfully");
}
void CloudReprojectionRosNode::loadParameters()
{
pnh_.param<std::string>("cloud_slam_topic", cloud_slam_topic_, std::string("/odin1/cloud_slam"));
pnh_.param<std::string>("odometry_topic", odometry_topic_, std::string("/odin1/odometry"));
pnh_.param<std::string>("wiwc_topic", wiwc_topic_, std::string("/odin1/wiwc"));
pnh_.param<std::string>("reprojected_image_topic", reprojected_image_topic_, std::string("/odin1/reprojected_image"));
// Load camera parameters
CloudReprojector::CameraParams cam_params;
pnh_.param<int>("cam_0/image_width", cam_params.image_width, 1600);
pnh_.param<int>("cam_0/image_height", cam_params.image_height, 1296);
pnh_.param<double>("cam_0/A11", cam_params.A11, 0.0);
pnh_.param<double>("cam_0/A12", cam_params.A12, 0.0);
pnh_.param<double>("cam_0/A22", cam_params.A22, 0.0);
pnh_.param<double>("cam_0/u0", cam_params.u0, 0.0);
pnh_.param<double>("cam_0/v0", cam_params.v0, 0.0);
pnh_.param<double>("cam_0/k2", cam_params.k2, 0.0);
pnh_.param<double>("cam_0/k3", cam_params.k3, 0.0);
pnh_.param<double>("cam_0/k4", cam_params.k4, 0.0);
pnh_.param<double>("cam_0/k5", cam_params.k5, 0.0);
pnh_.param<double>("cam_0/k6", cam_params.k6, 0.0);
pnh_.param<double>("cam_0/k7", cam_params.k7, 0.0);
// Load extrinsic parameters
CloudReprojector::ExtrinsicParams ext_params;
std::vector<double> Tcl_vec_param;
if (pnh_.getParam("Tcl_0", Tcl_vec_param) && Tcl_vec_param.size() == 16)
{
for (int i = 0; i < 4; ++i)
for (int j = 0; j < 4; ++j)
ext_params.Tcl(i, j) = Tcl_vec_param[i * 4 + j];
}
else
{
ROS_ERROR("Tcl_0 param missing or invalid.");
ros::shutdown();
return;
}
ext_params.Til = getFixedTil();
ext_params.Tic = CloudReprojector::calculateTic(ext_params.Tcl, ext_params.Til);
ROS_INFO_STREAM("Loaded Tcl (camera to lidar):\n" << ext_params.Tcl);
ROS_INFO_STREAM("Fixed Til (lidar to imu):\n" << ext_params.Til);
ROS_INFO_STREAM("Calculated Tic:\n" << ext_params.Tic);
ROS_INFO("Camera intrinsics:");
ROS_INFO("Image size: %dx%d", cam_params.image_width, cam_params.image_height);
ROS_INFO("Intrinsics: A11=%f A12=%f A22=%f u0=%f v0=%f",
cam_params.A11, cam_params.A12, cam_params.A22, cam_params.u0, cam_params.v0);
reprojector_ = std::make_unique<CloudReprojector>();
if (!reprojector_->initialize(cam_params, ext_params))
{
ROS_ERROR("Failed to initialize CloudReprojector");
ros::shutdown();
}
}
void CloudReprojectionRosNode::syncCallback(
const sensor_msgs::PointCloud2ConstPtr& cloud_msg,
const nav_msgs::OdometryConstPtr& odom_msg,
const nav_msgs::OdometryConstPtr& wiwc_msg)
{
pcl::PointCloud<pcl::PointXYZRGB> cloud_odom;
pcl::fromROSMsg(*cloud_msg, cloud_odom);
if (cloud_odom.empty())
{
ROS_WARN("Empty cloud_slam received");
return;
}
// Extract real-time extrinsics from WIWC message covariance fields
// pose.covariance contains T_CL (first 16 values), twist.covariance contains T_IL (first 16 values)
Eigen::Matrix4d T_CL = Eigen::Matrix4d::Identity();
Eigen::Matrix4d T_IL = Eigen::Matrix4d::Identity();
for (int i = 0; i < 16; ++i) {
T_CL(i / 4, i % 4) = wiwc_msg->pose.covariance[i];
T_IL(i / 4, i % 4) = wiwc_msg->twist.covariance[i];
}
// Update extrinsics if valid (not identity matrix)
bool T_CL_valid = (T_CL - Eigen::Matrix4d::Identity()).norm() > 1e-6;
bool T_IL_valid = (T_IL - Eigen::Matrix4d::Identity()).norm() > 1e-6;
if (T_CL_valid && T_IL_valid) {
reprojector_->updateExtrinsics(T_CL, T_IL);
}
CloudReprojector::OdomPose odom_pose;
odom_pose.orientation = Eigen::Quaterniond(
odom_msg->pose.pose.orientation.w,
odom_msg->pose.pose.orientation.x,
odom_msg->pose.pose.orientation.y,
odom_msg->pose.pose.orientation.z
);
odom_pose.position = Eigen::Vector3d(
odom_msg->pose.pose.position.x,
odom_msg->pose.pose.position.y,
odom_msg->pose.pose.position.z
);
cv::Mat reprojected_img = reprojector_->reprojectCloud(cloud_odom, odom_pose);
sensor_msgs::ImagePtr img_msg = cv_bridge::CvImage(cloud_msg->header, "bgr8", reprojected_img).toImageMsg();
reprojected_image_pub_.publish(img_msg);
}
// ==================== ROS1 Main ====================
int main(int argc, char **argv)
{
ros::init(argc, argv, "cloud_reprojection");
ros::NodeHandle nh;
ros::NodeHandle pnh("~");
// Check if reprojection is enabled
int sendreprojection = 0;
pnh.param("register_keys/sendreprojection", sendreprojection, 0);
if(sendreprojection == 0)
{
ROS_INFO("Cloud reprojection will not be published.");
return 0;
}
std::string calib_file_path;
pnh.param<std::string>("calib_file_path", calib_file_path, "");
ROS_INFO("Waiting for calib.yaml file at: %s", calib_file_path.c_str());
while(ros::ok() && !fileExists(calib_file_path))
{
ROS_INFO_THROTTLE(5, "Still waiting for calib.yaml file...");
ros::Duration(0.5).sleep();
ros::spinOnce();
}
if(!ros::ok())
{
ROS_INFO("Node shutdown before calib.yaml file was found.");
return 0;
}
ROS_INFO("Found calib.yaml file! Loading parameters...");
std::string node_name = ros::this_node::getName();
std::string rosparam_command = "rosparam load " + calib_file_path + " " + node_name;
int result = system(rosparam_command.c_str());
if(result == 0)
{
ROS_INFO("Successfully loaded parameters from calib.yaml to namespace: %s", node_name.c_str());
}
else
{
ROS_ERROR("Failed to load parameters from calib.yaml");
return 1;
}
CloudReprojectionRosNode reprojection_node(nh, pnh);
ros::spin();
return 0;
}
#endif
@@ -0,0 +1,128 @@
/*
Copyright 2025 Manifold Tech Ltd.(www.manifoldtech.com.co)
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
*/
#include "cloud_reprojector.hpp"
#include <cmath>
CloudReprojector::CloudReprojector()
{
}
bool CloudReprojector::initialize(const CameraParams& cam_params, const ExtrinsicParams& ext_params)
{
camera_params_ = cam_params;
extrinsic_params_ = ext_params;
if (camera_params_.A11 < 1e-6 || camera_params_.A22 < 1e-6 ||
camera_params_.u0 < 1e-6 || camera_params_.v0 < 1e-6)
{
return false;
}
camera_model_ = std::make_unique<mini_vikit::PolynomialCamera>(
camera_params_.image_width, camera_params_.image_height,
camera_params_.A11, camera_params_.A22,
camera_params_.u0, camera_params_.v0,
camera_params_.A12,
camera_params_.k2, camera_params_.k3, camera_params_.k4,
camera_params_.k5, camera_params_.k6, camera_params_.k7
);
initialized_ = true;
return true;
}
Eigen::Matrix4d CloudReprojector::calculateTic(const Eigen::Matrix4d& Tcl, const Eigen::Matrix4d& Til)
{
// Tic = Til * Tlc = Til * Tcl.inverse()
Eigen::Matrix4d Tlc = Tcl.inverse();
return Til * Tlc;
}
Eigen::Matrix4d CloudReprojector::odomPoseToMatrix(const OdomPose& odom) const
{
Eigen::Matrix4d T = Eigen::Matrix4d::Identity();
T.block<3, 3>(0, 0) = odom.orientation.toRotationMatrix();
T(0, 3) = odom.position.x();
T(1, 3) = odom.position.y();
T(2, 3) = odom.position.z();
return T;
}
cv::Mat CloudReprojector::reprojectCloud(const pcl::PointCloud<pcl::PointXYZRGB>& cloud_odom,
const OdomPose& odom_pose)
{
if (!initialized_)
{
return cv::Mat();
}
// T_odom_imu: imu pose in odom frame
Eigen::Matrix4d T_odom_imu = odomPoseToMatrix(odom_pose);
// T_imu_odom: transforms points from odom frame to imu frame
Eigen::Matrix4d T_imu_odom = T_odom_imu.inverse();
// T_cam_imu = Tic.inverse(): transforms from imu to camera
Eigen::Matrix4d T_cam_imu = extrinsic_params_.Tic.inverse();
// T_cam_odom: transforms points from odom frame to camera frame
Eigen::Matrix4d T_cam_odom = T_cam_imu * T_imu_odom;
pcl::PointCloud<pcl::PointXYZRGB> cloud_in_cam;
pcl::transformPointCloud(cloud_odom, cloud_in_cam, T_cam_odom);
return projectCloudToImage(cloud_in_cam);
}
cv::Mat CloudReprojector::projectCloudToImage(const pcl::PointCloud<pcl::PointXYZRGB>& cloud_in_cam) const
{
cv::Mat img = cv::Mat::zeros(camera_params_.image_height, camera_params_.image_width, CV_8UC3);
img.setTo(cv::Scalar(255, 255, 255));
const double fx = camera_model_->fx();
const double fy = camera_model_->fy();
const double cx = camera_model_->cx();
const double cy = camera_model_->cy();
for (const auto& pt : cloud_in_cam)
{
if (pt.z <= 0.01)
continue;
int u_int, v_int;
if (1)
{
// Pinhole projection (undistorted image)
u_int = static_cast<int>(std::round(fx * pt.x / pt.z + cx));
v_int = static_cast<int>(std::round(fy * pt.y / pt.z + cy));
}
else
{
// Distorted projection (original image)
Eigen::Vector3d pt_cam(pt.x, pt.y, pt.z);
Eigen::Vector2d uv = camera_model_->world2cam(pt_cam);
u_int = static_cast<int>(std::round(uv[0]));
v_int = static_cast<int>(std::round(uv[1]));
}
if (u_int >= 0 && u_int < camera_params_.image_width &&
v_int >= 0 && v_int < camera_params_.image_height)
{
cv::circle(img, cv::Point(u_int, v_int), point_radius_,
cv::Scalar(pt.b, pt.g, pt.r), -1);
}
}
return img;
}
@@ -0,0 +1,172 @@
/*
Copyright 2025 Manifold Tech Ltd.(www.manifoldtech.com.co)
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
*/
#include "depth_image_ros2_node.hpp"
#include <functional>
DepthImageRos2Node::DepthImageRos2Node(const rclcpp::NodeOptions & options)
: Node("depth_image_ros2_node", options)
{
PointCloudToDepthConverter::CameraParams camera_params = loadCameraParams();
depth_converter_ = std::make_unique<PointCloudToDepthConverter>(camera_params);
cloud_raw_topic_ = this->declare_parameter<std::string>("cloud_raw_topic", "/odin1/cloud_raw");
color_compressed_topic_ = this->declare_parameter<std::string>("color_compressed_topic", "/odin1/image/compressed");
color_raw_topic_ = this->declare_parameter<std::string>("color_raw_topic", "/odin1/image");
depth_image_topic_ = this->declare_parameter<std::string>("depth_image_topic", "/odin1/depth_img_competetion");
depth_cloud_topic_ = this->declare_parameter<std::string>("depth_cloud_topic", "/odin1/depth_img_competetion_cloud");
RCLCPP_INFO_STREAM(this->get_logger(),
"\n cloud_raw_topic: " << cloud_raw_topic_
<< "\n color_compressed_topic: " << color_compressed_topic_
<< "\n color_raw_topic: " << color_raw_topic_
<< "\n depth_image_topic: " << depth_image_topic_
<< "\n depth_cloud_topic: " << depth_cloud_topic_);
}
void DepthImageRos2Node::initialize()
{
cloud_sub_.subscribe(this, cloud_raw_topic_);
color_compressed_sub_.subscribe(this, color_compressed_topic_);
color_sub_.subscribe(this, color_raw_topic_);
sync_ = std::make_shared<Sync>(MySyncPolicy(10), cloud_sub_, color_sub_);
sync_->registerCallback(std::bind(&DepthImageRos2Node::syncCallback, this,
std::placeholders::_1, std::placeholders::_2));
it_ = std::make_shared<image_transport::ImageTransport>(shared_from_this());
depth_image_pub_ = it_->advertise(depth_image_topic_, 1);
depth_cloud_pub_ = this->create_publisher<sensor_msgs::msg::PointCloud2>(depth_cloud_topic_, 1);
RCLCPP_INFO(this->get_logger(), "DepthImageRos2Node initialized successfully");
}
PointCloudToDepthConverter::CameraParams DepthImageRos2Node::loadCameraParams()
{
PointCloudToDepthConverter::CameraParams params;
params.image_width = this->declare_parameter<int>("cam_0.image_width", 1600);
params.image_height = this->declare_parameter<int>("cam_0.image_height", 1296);
params.A11 = this->declare_parameter<double>("cam_0.A11", 0.0);
params.A12 = this->declare_parameter<double>("cam_0.A12", 0.0);
params.A22 = this->declare_parameter<double>("cam_0.A22", 0.0);
params.u0 = this->declare_parameter<double>("cam_0.u0", 0.0);
params.v0 = this->declare_parameter<double>("cam_0.v0", 0.0);
params.k2 = this->declare_parameter<double>("cam_0.k2", 0.0);
params.k3 = this->declare_parameter<double>("cam_0.k3", 0.0);
params.k4 = this->declare_parameter<double>("cam_0.k4", 0.0);
params.k5 = this->declare_parameter<double>("cam_0.k5", 0.0);
params.k6 = this->declare_parameter<double>("cam_0.k6", 0.0);
params.k7 = this->declare_parameter<double>("cam_0.k7", 0.0);
params.scale = this->declare_parameter<double>("scale", 7.0);
params.point_sampling_rate = this->declare_parameter<int>("point_sampling_rate", 5);
std::vector<double> Tcl_vec_param = this->declare_parameter<std::vector<double>>("Tcl_0", std::vector<double>(16, 0.0));
if (Tcl_vec_param.size() == 16)
{
for (int i = 0; i < 4; ++i)
{
for (int j = 0; j < 4; ++j)
{
params.Tcl(i, j) = Tcl_vec_param[i * 4 + j];
}
}
}
else
{
RCLCPP_ERROR(this->get_logger(), "Tcl_0 param missing or invalid, colored reproject cloud disabled.");
rclcpp::shutdown();
}
if (params.A11 < 1e-6 || params.A22 < 1e-6 || params.u0 < 1e-6 || params.v0 < 1e-6)
{
RCLCPP_ERROR(this->get_logger(), "Invalid camera intrinsics A11 or A22");
rclcpp::shutdown();
}
RCLCPP_INFO(this->get_logger(), "Camera intrinsics:");
RCLCPP_INFO(this->get_logger(), "Image size: %dx%d", params.image_width, params.image_height);
RCLCPP_INFO(this->get_logger(), "Intrinsics: A11=%f A12=%f A22=%f u0=%f v0=%f",
params.A11, params.A12, params.A22, params.u0, params.v0);
RCLCPP_INFO(this->get_logger(), "Distortions: k2=%f k3=%f k4=%f k5=%f k6=%f k7=%f",
params.k2, params.k3, params.k4, params.k5, params.k6, params.k7);
RCLCPP_INFO(this->get_logger(), "Scale: %f, Point sampling rate: %d", params.scale, params.point_sampling_rate);
RCLCPP_INFO_STREAM(this->get_logger(), "Extrinsics (Tcl):\n" << params.Tcl);
return params;
}
void DepthImageRos2Node::syncCallback(const sensor_msgs::msg::PointCloud2::ConstSharedPtr cloud_msg,
// const sensor_msgs::msg::CompressedImage::ConstSharedPtr image_msg,
const sensor_msgs::msg::Image::ConstSharedPtr color_msg)
{
pcl::PointCloud<pcl::PointXYZ> cloud;
pcl::fromROSMsg(*cloud_msg, cloud);
if (cloud.empty())
{
RCLCPP_WARN(this->get_logger(), "Empty point cloud received");
return;
}
cv::Mat img_raw;
try
{
// img_raw = cv::imdecode(cv::Mat(image_msg->data), cv::IMREAD_COLOR);
cv_bridge::CvImageConstPtr cv_ptr = cv_bridge::toCvShare(color_msg, "bgr8");
img_raw = cv_ptr->image;
if (img_raw.empty())
{
RCLCPP_WARN(this->get_logger(), "Failed to decode compressed image");
return;
}
}
catch (const cv_bridge::Exception &e)
{
RCLCPP_ERROR(this->get_logger(), "cv_bridge: %s", e.what());
return;
}
auto result = depth_converter_->processCloudAndImage(cloud, img_raw);
if (!result.success)
{
RCLCPP_WARN(this->get_logger(), "Data processing failed: %s", result.error_message.c_str());
return;
}
publishDepthImage(result.depth_image, cloud_msg->header);
publishDepthCloud(result.colored_cloud, cloud_msg->header);
}
void DepthImageRos2Node::publishDepthImage(const cv::Mat &img,
const std_msgs::msg::Header &header,
const std::string &encoding)
{
sensor_msgs::msg::Image::SharedPtr depth_msg = cv_bridge::CvImage(header, encoding, img).toImageMsg();
depth_image_pub_.publish(*depth_msg);
}
void DepthImageRos2Node::publishDepthCloud(const pcl::PointCloud<pcl::PointXYZRGB> &colored_cloud,
const std_msgs::msg::Header &header)
{
if (!colored_cloud.points.empty())
{
sensor_msgs::msg::PointCloud2 cloud_msg;
pcl::toROSMsg(colored_cloud, cloud_msg);
cloud_msg.header = header;
depth_cloud_pub_->publish(cloud_msg);
}
}
@@ -0,0 +1,165 @@
/*
Copyright 2025 Manifold Tech Ltd.(www.manifoldtech.com.co)
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
*/
#include "depth_image_ros_node.hpp"
#include <boost/bind.hpp>
DepthImageRosNode::DepthImageRosNode(ros::NodeHandle &nh, ros::NodeHandle &pnh)
: nh_(nh), pnh_(pnh), it_(nh_)
{
PointCloudToDepthConverter::CameraParams camera_params = loadCameraParams();
depth_converter_ = std::make_unique<PointCloudToDepthConverter>(camera_params);
pnh_.param<std::string>("cloud_raw_topic", cloud_raw_topic_, std::string("/odin1/cloud_raw"));
pnh_.param<std::string>("color_raw_topic", color_raw_topic_, std::string("/odin1/image"));
pnh_.param<std::string>("color_compressed_topic_", color_compressed_topic_, std::string("/odin1/image/compressed"));
pnh_.param<std::string>("depth_image_topic", depth_image_topic_, std::string("/odin1/depth_img_competetion"));
pnh_.param<std::string>("depth_cloud_topic", depth_cloud_topic_, std::string("/odin1/depth_img_competetion_cloud"));
ROS_INFO_STREAM("\n cloud_raw_topic: " << cloud_raw_topic_
<< "\n color_raw_topic: " << color_raw_topic_
<< "\n color_compressed_topic: " << color_compressed_topic_
<< "\n depth_image_topic: " << depth_image_topic_
<< "\n depth_cloud_topic: " << depth_cloud_topic_);
cloud_sub_.subscribe(nh_, cloud_raw_topic_, 1);
color_sub_.subscribe(nh_, color_raw_topic_, 1);
color_compressed_sub_.subscribe(nh_, color_compressed_topic_, 1);
sync_ = std::make_shared<Sync>(MySyncPolicy(10), cloud_sub_, color_sub_);
sync_->registerCallback(boost::bind(&DepthImageRosNode::syncCallback, this, _1, _2));
depth_image_pub_ = it_.advertise(depth_image_topic_, 1);
depth_cloud_pub_ = nh_.advertise<sensor_msgs::PointCloud2>(depth_cloud_topic_, 1);
ROS_INFO("DepthImageRosNode initialized successfully");
}
PointCloudToDepthConverter::CameraParams DepthImageRosNode::loadCameraParams()
{
PointCloudToDepthConverter::CameraParams params;
pnh_.param<int>("cam_0/image_width", params.image_width, 1600);
pnh_.param<int>("cam_0/image_height", params.image_height, 1296);
pnh_.param<double>("cam_0/A11", params.A11, 0.0);
pnh_.param<double>("cam_0/A12", params.A12, 0.0);
pnh_.param<double>("cam_0/A22", params.A22, 0.0);
pnh_.param<double>("cam_0/u0", params.u0, 0.0);
pnh_.param<double>("cam_0/v0", params.v0, 0.0);
pnh_.param<double>("cam_0/k2", params.k2, 0.0);
pnh_.param<double>("cam_0/k3", params.k3, 0.0);
pnh_.param<double>("cam_0/k4", params.k4, 0.0);
pnh_.param<double>("cam_0/k5", params.k5, 0.0);
pnh_.param<double>("cam_0/k6", params.k6, 0.0);
pnh_.param<double>("cam_0/k7", params.k7, 0.0);
pnh_.param<double>("scale", params.scale, 7.0);
pnh_.param<int>("point_sampling_rate", params.point_sampling_rate, 5);
std::vector<double> Tcl_vec_param;
if (pnh_.getParam("Tcl_0", Tcl_vec_param) && Tcl_vec_param.size() == 16)
{
for (int i = 0; i < 4; ++i)
{
for (int j = 0; j < 4; ++j)
{
params.Tcl(i, j) = Tcl_vec_param[i * 4 + j];
}
}
}
else
{
ROS_ERROR("Tcl_0 param missing or invalid, colored reproject cloud disabled.");
ros::shutdown();
}
if (params.A11 < 1e-6 || params.A22 < 1e-6 || params.u0 < 1e-6 || params.v0 < 1e-6)
{
ROS_ERROR("Invalid camera intrinsics A11 or A22");
ros::shutdown();
}
ROS_INFO("Camera intrinsics:");
ROS_INFO("Image size: %dx%d", params.image_width, params.image_height);
ROS_INFO("Intrinsics: A11=%f A12=%f A22=%f u0=%f v0=%f",
params.A11, params.A12, params.A22, params.u0, params.v0);
ROS_INFO("Distortions: k2=%f k3=%f k4=%f k5=%f k6=%f k7=%f",
params.k2, params.k3, params.k4, params.k5, params.k6, params.k7);
ROS_INFO("Scale: %f, Point sampling rate: %d", params.scale, params.point_sampling_rate);
ROS_INFO_STREAM("Extrinsics (Tcl):\n"
<< params.Tcl);
return params;
}
void DepthImageRosNode::syncCallback(const sensor_msgs::PointCloud2ConstPtr &cloud_msg,
const sensor_msgs::ImageConstPtr &image_msg)
{
pcl::PointCloud<pcl::PointXYZ> cloud;
pcl::fromROSMsg(*cloud_msg, cloud);
if (cloud.empty())
{
ROS_WARN("Empty point cloud received");
return;
}
cv::Mat img_raw;
try
{
// img_raw = cv::imdecode(cv::Mat(image_msg->data), cv::IMREAD_COLOR);
cv_bridge::CvImageConstPtr cv_ptr = cv_bridge::toCvShare(image_msg, "bgr8");
img_raw = cv_ptr->image;
if (img_raw.empty())
{
ROS_WARN("Failed to decode compressed image");
return;
}
}
catch (const cv_bridge::Exception &e)
{
ROS_ERROR("cv_bridge: %s", e.what());
return;
}
auto result = depth_converter_->processCloudAndImage(cloud, img_raw);
if (!result.success)
{
ROS_WARN("Data processing failed: %s", result.error_message.c_str());
return;
}
publishDepthImage(result.depth_image, cloud_msg->header);
publishDepthCloud(result.colored_cloud, cloud_msg->header);
}
void DepthImageRosNode::publishDepthImage(const cv::Mat &img,
const std_msgs::Header &header,
const std::string &encoding)
{
sensor_msgs::ImagePtr depth_msg = cv_bridge::CvImage(header, encoding, img).toImageMsg();
depth_image_pub_.publish(depth_msg);
}
void DepthImageRosNode::publishDepthCloud(const pcl::PointCloud<pcl::PointXYZRGB> &colored_cloud,
const std_msgs::Header &header)
{
if (!colored_cloud.points.empty())
{
sensor_msgs::PointCloud2 cloud_msg;
pcl::toROSMsg(colored_cloud, cloud_msg);
cloud_msg.header = header;
depth_cloud_pub_.publish(cloud_msg);
}
}
File diff suppressed because it is too large Load Diff
@@ -0,0 +1,254 @@
/*
Copyright 2025 Manifold Tech Ltd.(www.manifoldtech.com.co)
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
*/
#include "image_overlay_node.hpp"
#ifdef ROS2
// ==================== ROS2 Implementation ====================
ImageOverlayNode::ImageOverlayNode(const rclcpp::NodeOptions& options)
: Node("image_overlay_node", options)
{
// Read from register_keys (same structure as control_command.yaml)
this->declare_parameter<std::string>("register_keys.overlay_reprojected_topic", "/odin1/reprojected_image");
this->declare_parameter<std::string>("register_keys.overlay_camera_topic", "/odin1/image/undistorted");
this->declare_parameter<std::string>("register_keys.overlay_output_topic", "/odin1/overlay_image");
this->declare_parameter<double>("register_keys.overlay_alpha", 0.6);
reprojected_topic_ = this->get_parameter("register_keys.overlay_reprojected_topic").as_string();
camera_topic_ = this->get_parameter("register_keys.overlay_camera_topic").as_string();
overlay_topic_ = this->get_parameter("register_keys.overlay_output_topic").as_string();
alpha_ = this->get_parameter("register_keys.overlay_alpha").as_double();
RCLCPP_INFO(this->get_logger(), "Subscribing to: %s and %s",
reprojected_topic_.c_str(), camera_topic_.c_str());
RCLCPP_INFO(this->get_logger(), "Publishing to: %s (alpha=%.2f)", overlay_topic_.c_str(), alpha_);
// Independent subscriptions - no synchronization needed
reproj_sub_ = this->create_subscription<Image>(
reprojected_topic_, 10,
std::bind(&ImageOverlayNode::reprojCallback, this, std::placeholders::_1));
camera_sub_ = this->create_subscription<Image>(
camera_topic_, 10,
std::bind(&ImageOverlayNode::cameraCallback, this, std::placeholders::_1));
overlay_pub_ = this->create_publisher<sensor_msgs::msg::Image>(overlay_topic_, 10);
RCLCPP_INFO(this->get_logger(), "ImageOverlayNode initialized (no-sync mode)");
}
void ImageOverlayNode::reprojCallback(Image::ConstSharedPtr msg)
{
try {
cv_bridge::CvImagePtr cv_ptr = cv_bridge::toCvCopy(msg, "bgr8");
{
std::lock_guard<std::mutex> lock(mutex_);
latest_reproj_img_ = cv_ptr->image.clone();
latest_header_ = msg->header;
}
} catch (cv_bridge::Exception& e) {
RCLCPP_ERROR(this->get_logger(), "cv_bridge exception (reproj): %s", e.what());
return;
}
publishOverlay();
}
void ImageOverlayNode::cameraCallback(Image::ConstSharedPtr msg)
{
try {
cv_bridge::CvImagePtr cv_ptr = cv_bridge::toCvCopy(msg, "bgr8");
{
std::lock_guard<std::mutex> lock(mutex_);
latest_camera_img_ = cv_ptr->image.clone();
}
} catch (cv_bridge::Exception& e) {
RCLCPP_ERROR(this->get_logger(), "cv_bridge exception (camera): %s", e.what());
return;
}
publishOverlay();
}
void ImageOverlayNode::publishOverlay()
{
cv::Mat reproj_copy, camera_copy;
std_msgs::msg::Header header_copy;
{
std::lock_guard<std::mutex> lock(mutex_);
if (latest_reproj_img_.empty() || latest_camera_img_.empty()) {
return;
}
reproj_copy = latest_reproj_img_.clone();
camera_copy = latest_camera_img_.clone();
header_copy = latest_header_;
}
if (reproj_copy.size() != camera_copy.size()) {
RCLCPP_WARN(this->get_logger(),
"Image sizes don't match: reproj(%dx%d) vs camera(%dx%d)",
reproj_copy.cols, reproj_copy.rows,
camera_copy.cols, camera_copy.rows);
return;
}
// Create overlay using alpha blending
// Replace white background in reproj with camera image, keep colored points
cv::Mat overlay = camera_copy.clone();
// Blend: where reproj has color (non-white), show reproj color semi-transparently
// where reproj is white (background), show camera image
for (int y = 0; y < reproj_copy.rows; ++y) {
for (int x = 0; x < reproj_copy.cols; ++x) {
cv::Vec3b reproj_pixel = reproj_copy.at<cv::Vec3b>(y, x);
// Check if pixel is not white (has point cloud color)
if (reproj_pixel[0] < 250 || reproj_pixel[1] < 250 || reproj_pixel[2] < 250) {
// Blend reproj color with camera color
cv::Vec3b cam_pixel = camera_copy.at<cv::Vec3b>(y, x);
overlay.at<cv::Vec3b>(y, x) = cv::Vec3b(
static_cast<uchar>(alpha_ * reproj_pixel[0] + (1 - alpha_) * cam_pixel[0]),
static_cast<uchar>(alpha_ * reproj_pixel[1] + (1 - alpha_) * cam_pixel[1]),
static_cast<uchar>(alpha_ * reproj_pixel[2] + (1 - alpha_) * cam_pixel[2])
);
}
// else: keep camera image (already in overlay)
}
}
auto overlay_msg = cv_bridge::CvImage(header_copy, "bgr8", overlay).toImageMsg();
overlay_pub_->publish(*overlay_msg);
}
// ==================== ROS2 Main ====================
int main(int argc, char **argv)
{
rclcpp::init(argc, argv);
auto node = std::make_shared<ImageOverlayNode>();
rclcpp::spin(node);
rclcpp::shutdown();
return 0;
}
#else
// ==================== ROS1 Implementation ====================
ImageOverlayNode::ImageOverlayNode(ros::NodeHandle& nh, ros::NodeHandle& pnh)
: nh_(nh), pnh_(pnh)
{
// Read from register_keys (same structure as control_command.yaml)
pnh_.param<std::string>("register_keys/overlay_reprojected_topic", reprojected_topic_, "/odin1/reprojected_image");
pnh_.param<std::string>("register_keys/overlay_camera_topic", camera_topic_, "/odin1/image/undistorted");
pnh_.param<std::string>("register_keys/overlay_output_topic", overlay_topic_, "/odin1/overlay_image");
pnh_.param<double>("register_keys/overlay_alpha", alpha_, 0.6);
ROS_INFO("Subscribing to: %s and %s", reprojected_topic_.c_str(), camera_topic_.c_str());
ROS_INFO("Publishing to: %s (alpha=%.2f)", overlay_topic_.c_str(), alpha_);
// Independent subscriptions - no synchronization needed
reproj_sub_ = nh_.subscribe(reprojected_topic_, 10, &ImageOverlayNode::reprojCallback, this);
camera_sub_ = nh_.subscribe(camera_topic_, 10, &ImageOverlayNode::cameraCallback, this);
overlay_pub_ = nh_.advertise<sensor_msgs::Image>(overlay_topic_, 10);
ROS_INFO("ImageOverlayNode initialized (no-sync mode)");
}
void ImageOverlayNode::reprojCallback(const sensor_msgs::ImageConstPtr& msg)
{
try {
cv_bridge::CvImagePtr cv_ptr = cv_bridge::toCvCopy(msg, "bgr8");
{
std::lock_guard<std::mutex> lock(mutex_);
latest_reproj_img_ = cv_ptr->image.clone();
latest_header_ = msg->header;
}
} catch (cv_bridge::Exception& e) {
ROS_ERROR("cv_bridge exception (reproj): %s", e.what());
return;
}
publishOverlay();
}
void ImageOverlayNode::cameraCallback(const sensor_msgs::ImageConstPtr& msg)
{
try {
cv_bridge::CvImagePtr cv_ptr = cv_bridge::toCvCopy(msg, "bgr8");
{
std::lock_guard<std::mutex> lock(mutex_);
latest_camera_img_ = cv_ptr->image.clone();
}
} catch (cv_bridge::Exception& e) {
ROS_ERROR("cv_bridge exception (camera): %s", e.what());
return;
}
publishOverlay();
}
void ImageOverlayNode::publishOverlay()
{
cv::Mat reproj_copy, camera_copy;
std_msgs::Header header_copy;
{
std::lock_guard<std::mutex> lock(mutex_);
if (latest_reproj_img_.empty() || latest_camera_img_.empty()) {
return;
}
reproj_copy = latest_reproj_img_.clone();
camera_copy = latest_camera_img_.clone();
header_copy = latest_header_;
}
if (reproj_copy.size() != camera_copy.size()) {
ROS_WARN_THROTTLE(2, "Image sizes don't match: reproj(%dx%d) vs camera(%dx%d)",
reproj_copy.cols, reproj_copy.rows, camera_copy.cols, camera_copy.rows);
return;
}
// Create overlay using alpha blending
cv::Mat overlay = camera_copy.clone();
for (int y = 0; y < reproj_copy.rows; ++y) {
for (int x = 0; x < reproj_copy.cols; ++x) {
cv::Vec3b reproj_pixel = reproj_copy.at<cv::Vec3b>(y, x);
if (reproj_pixel[0] < 250 || reproj_pixel[1] < 250 || reproj_pixel[2] < 250) {
cv::Vec3b cam_pixel = camera_copy.at<cv::Vec3b>(y, x);
overlay.at<cv::Vec3b>(y, x) = cv::Vec3b(
static_cast<uchar>(alpha_ * reproj_pixel[0] + (1 - alpha_) * cam_pixel[0]),
static_cast<uchar>(alpha_ * reproj_pixel[1] + (1 - alpha_) * cam_pixel[1]),
static_cast<uchar>(alpha_ * reproj_pixel[2] + (1 - alpha_) * cam_pixel[2])
);
}
}
}
sensor_msgs::ImagePtr overlay_msg = cv_bridge::CvImage(header_copy, "bgr8", overlay).toImageMsg();
overlay_pub_.publish(overlay_msg);
}
// ==================== ROS1 Main ====================
int main(int argc, char **argv)
{
ros::init(argc, argv, "image_overlay_node");
ros::NodeHandle nh;
ros::NodeHandle pnh("~");
ImageOverlayNode node(nh, pnh);
ros::spin();
return 0;
}
#endif
@@ -0,0 +1,74 @@
/*
Copyright 2025 Manifold Tech Ltd.(www.manifoldtech.com.co)
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
*/
#include <ros/ros.h>
#include <fstream>
#include <sys/stat.h>
#include "depth_image_ros_node.hpp"
bool fileExists(const std::string& filename) {
struct stat buffer;
return (stat(filename.c_str(), &buffer) == 0);
}
int main(int argc, char **argv)
{
ros::init(argc, argv, "depth_projector");
ros::NodeHandle nh;
ros::NodeHandle pnh("~");
int senddepth=0;
pnh.param("register_keys/senddepth", senddepth, 0);
if(senddepth==0)
{
ROS_INFO("Depth image will not be published.");
return 0;
}
std::string calib_file_path;
pnh.param<std::string>("calib_file_path", calib_file_path, "");
ROS_INFO("Waiting for calib.yaml file at: %s", calib_file_path.c_str());
while(ros::ok() && !fileExists(calib_file_path))
{
ROS_INFO_THROTTLE(5, "Still waiting for calib.yaml file...");
ros::Duration(0.5).sleep();
ros::spinOnce();
}
if(!ros::ok())
{
ROS_INFO("Node shutdown before calib.yaml file was found.");
return 0;
}
ROS_INFO("Found calib.yaml file! Loading parameters...");
std::string node_name = ros::this_node::getName();
std::string rosparam_command = "rosparam load " + calib_file_path + " " + node_name;
int result = system(rosparam_command.c_str());
if(result == 0)
{
ROS_INFO("Successfully loaded parameters from calib.yaml to namespace: %s", node_name.c_str());
}
else
{
ROS_ERROR("Failed to load parameters from calib.yaml");
return 1;
}
DepthImageRosNode projector(nh, pnh);
ros::spin();
return 0;
}
@@ -0,0 +1,238 @@
/*
Copyright 2025 Manifold Tech Ltd.(www.manifoldtech.com.co)
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
*/
#include <rclcpp/rclcpp.hpp>
#include <fstream>
#include <sys/stat.h>
#include <yaml-cpp/yaml.h>
#include <iostream>
#include <fstream>
#include <string>
#include "depth_image_ros2_node.hpp"
#include <rcpputils/filesystem_helper.hpp>
bool fileExists(const std::string& filename) {
struct stat buffer;
return (stat(filename.c_str(), &buffer) == 0);
}
bool loadCalibParameters(std::shared_ptr<rclcpp::Node> node, const std::string& calib_file_path) {
try {
RCLCPP_INFO(node->get_logger(), "Loading parameters from calib.yaml file: %s", calib_file_path.c_str());
YAML::Node config = YAML::LoadFile(calib_file_path);
if (config["cam_num"]) {
int cam_num = config["cam_num"].as<int>();
node->declare_parameter("cam_num", cam_num);
RCLCPP_INFO(node->get_logger(), "Loaded parameter: cam_num = %d", cam_num);
}
if (config["img_topic_0"]) {
std::string img_topic = config["img_topic_0"].as<std::string>();
node->declare_parameter("img_topic_0", img_topic);
RCLCPP_INFO(node->get_logger(), "Loaded parameter: img_topic_0 = %s", img_topic.c_str());
}
if (config["Tcl_0"]) {
std::vector<double> tcl_matrix = config["Tcl_0"].as<std::vector<double>>();
node->declare_parameter("Tcl_0", tcl_matrix);
RCLCPP_INFO(node->get_logger(), "Loaded parameter: Tcl_0 (transform matrix with %zu elements)", tcl_matrix.size());
}
if (config["cam_0"]) {
YAML::Node cam_0 = config["cam_0"];
if (cam_0["cam_model"]) {
std::string cam_model = cam_0["cam_model"].as<std::string>();
node->declare_parameter("cam_0.cam_model", cam_model);
RCLCPP_INFO(node->get_logger(), "Loaded parameter: cam_0.cam_model = %s", cam_model.c_str());
}
if (cam_0["image_width"]) {
int image_width = cam_0["image_width"].as<int>();
node->declare_parameter("cam_0.image_width", image_width);
RCLCPP_INFO(node->get_logger(), "Loaded parameter: cam_0.image_width = %d", image_width);
}
if (cam_0["image_height"]) {
int image_height = cam_0["image_height"].as<int>();
node->declare_parameter("cam_0.image_height", image_height);
RCLCPP_INFO(node->get_logger(), "Loaded parameter: cam_0.image_height = %d", image_height);
}
std::vector<std::string> distortion_params = {"k2", "k3", "k4", "k5", "k6", "k7", "p1", "p2"};
for (const auto& param : distortion_params) {
if (cam_0[param]) {
double value = cam_0[param].as<double>();
node->declare_parameter("cam_0." + param, value);
RCLCPP_INFO(node->get_logger(), "Loaded parameter: cam_0.%s = %f", param.c_str(), value);
}
}
std::vector<std::string> intrinsic_params = {"A11", "A12", "A22", "u0", "v0"};
for (const auto& param : intrinsic_params) {
if (cam_0[param]) {
double value = cam_0[param].as<double>();
node->declare_parameter("cam_0." + param, value);
RCLCPP_INFO(node->get_logger(), "Loaded parameter: cam_0.%s = %f", param.c_str(), value);
}
}
if (cam_0["isFast"]) {
int is_fast = cam_0["isFast"].as<int>();
node->declare_parameter("cam_0.isFast", is_fast);
RCLCPP_INFO(node->get_logger(), "Loaded parameter: cam_0.isFast = %d", is_fast);
}
if (cam_0["numDiff"]) {
int num_diff = cam_0["numDiff"].as<int>();
node->declare_parameter("cam_0.numDiff", num_diff);
RCLCPP_INFO(node->get_logger(), "Loaded parameter: cam_0.numDiff = %d", num_diff);
}
if (cam_0["maxIncidentAngle"]) {
int max_incident_angle = cam_0["maxIncidentAngle"].as<int>();
node->declare_parameter("cam_0.maxIncidentAngle", max_incident_angle);
RCLCPP_INFO(node->get_logger(), "Loaded parameter: cam_0.maxIncidentAngle = %d", max_incident_angle);
}
}
RCLCPP_INFO(node->get_logger(), "Successfully loaded all parameters from calib.yaml");
return true;
} catch (const YAML::Exception& e) {
RCLCPP_ERROR(node->get_logger(), "YAML parsing error: %s", e.what());
return false;
} catch (const std::exception& e) {
RCLCPP_ERROR(node->get_logger(), "Error loading calib parameters: %s", e.what());
return false;
}
}
std::string get_package_source_directory() {
// 使用 rcpputils::fs::path 替代 std::filesystem::path
rcpputils::fs::path current_file(__FILE__);
// 回溯到包根目录
auto path = current_file.parent_path();
// 使用 rcpputils::fs::exists 替代 std::filesystem::exists
while (!path.empty() && !rcpputils::fs::exists(path / "package.xml")) {
path = path.parent_path();
}
if (path.empty()) {
throw std::runtime_error("Failed to locate package root directory");
}
return path.string();
}
int main(int argc, char **argv)
{
rclcpp::init(argc, argv);
auto node = std::make_shared<rclcpp::Node>("pcd2depth_node");
RCLCPP_INFO(node->get_logger(), "Node created");
std::string package_path = get_package_source_directory();
RCLCPP_INFO(node->get_logger(), "Package path: %s", package_path.c_str());
std::string config_file = package_path + "/config/control_command.yaml";
RCLCPP_INFO(node->get_logger(), "Loading config from: %s", config_file.c_str());
YAML::Node config = YAML::LoadFile(config_file);
if (!config["register_keys"]) {
throw std::runtime_error("Missing 'register_keys' section");
}
if (!config["register_keys"]["senddepth"]) {
throw std::runtime_error("Missing 'senddepth' parameter");
}
int senddepth = config["register_keys"]["senddepth"].as<int>();
std::cout << "senddepth: " << senddepth << std::endl;
if(senddepth == 0)
{
RCLCPP_INFO(node->get_logger(), "Depth image will not be published.");
return 0;
}
std::string calib_file_path = node->declare_parameter<std::string>("calib_file_path", "");
RCLCPP_INFO(node->get_logger(), "Waiting for calib.yaml file at: %s", calib_file_path.c_str());
while(rclcpp::ok() && !fileExists(calib_file_path))
{
RCLCPP_INFO_THROTTLE(node->get_logger(), *node->get_clock(), 5000, "Still waiting for calib.yaml file...");
rclcpp::sleep_for(std::chrono::milliseconds(500)); // 等待0.5秒后再检查
rclcpp::spin_some(node);
}
if(!rclcpp::ok())
{
RCLCPP_INFO(node->get_logger(), "Node shutdown before calib.yaml file was found.");
return 0;
}
RCLCPP_INFO(node->get_logger(), "Found calib.yaml file! Loading parameters...");
if (!loadCalibParameters(node, calib_file_path)) {
RCLCPP_ERROR(node->get_logger(), "Failed to load parameters from calib.yaml file");
return 1;
}
rclcpp::NodeOptions depth_node_options;
std::vector<rclcpp::Parameter> params_override;
try {
params_override.push_back(rclcpp::Parameter("cam_0.image_width", node->get_parameter("cam_0.image_width").as_int()));
params_override.push_back(rclcpp::Parameter("cam_0.image_height", node->get_parameter("cam_0.image_height").as_int()));
params_override.push_back(rclcpp::Parameter("cam_0.A11", node->get_parameter("cam_0.A11").as_double()));
params_override.push_back(rclcpp::Parameter("cam_0.A12", node->get_parameter("cam_0.A12").as_double()));
params_override.push_back(rclcpp::Parameter("cam_0.A22", node->get_parameter("cam_0.A22").as_double()));
params_override.push_back(rclcpp::Parameter("cam_0.u0", node->get_parameter("cam_0.u0").as_double()));
params_override.push_back(rclcpp::Parameter("cam_0.v0", node->get_parameter("cam_0.v0").as_double()));
params_override.push_back(rclcpp::Parameter("cam_0.k2", node->get_parameter("cam_0.k2").as_double()));
params_override.push_back(rclcpp::Parameter("cam_0.k3", node->get_parameter("cam_0.k3").as_double()));
params_override.push_back(rclcpp::Parameter("cam_0.k4", node->get_parameter("cam_0.k4").as_double()));
params_override.push_back(rclcpp::Parameter("cam_0.k5", node->get_parameter("cam_0.k5").as_double()));
params_override.push_back(rclcpp::Parameter("cam_0.k6", node->get_parameter("cam_0.k6").as_double()));
params_override.push_back(rclcpp::Parameter("cam_0.k7", node->get_parameter("cam_0.k7").as_double()));
params_override.push_back(rclcpp::Parameter("Tcl_0", node->get_parameter("Tcl_0").as_double_array()));
depth_node_options.parameter_overrides(params_override);
RCLCPP_INFO(node->get_logger(), "Parameters successfully prepared for DepthImageRos2Node");
}
catch (const std::exception& e) {
RCLCPP_ERROR(node->get_logger(), "Error preparing parameters for DepthImageRos2Node: %s", e.what());
return 1;
}
try
{
auto depth_node = std::make_shared<DepthImageRos2Node>(depth_node_options);
depth_node->initialize();
rclcpp::executors::MultiThreadedExecutor executor;
executor.add_node(depth_node);
executor.spin();
}
catch (const std::exception& e)
{
RCLCPP_ERROR(node->get_logger(), "Error creating depth node: %s", e.what());
return 1;
}
rclcpp::shutdown();
return 0;
}
@@ -0,0 +1,376 @@
/*
Copyright 2025 Manifold Tech Ltd.(www.manifoldtech.com.co)
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
*/
#include "pointcloud_depth_converter.hpp"
#include <cmath>
#include <iostream>
PointCloudToDepthConverter::PointCloudToDepthConverter(const CameraParams &params)
: params_(params)
{
initializeInternalParams();
createDistortionMaps();
}
void PointCloudToDepthConverter::initializeInternalParams()
{
scaled_width_ = static_cast<int>(params_.image_width / params_.scale);
scaled_height_ = static_cast<int>(params_.image_height / params_.scale);
K_ = Eigen::Matrix3d::Identity();
K_(0, 0) = params_.A11;
K_(0, 1) = params_.A12;
K_(0, 2) = params_.u0;
K_(1, 1) = params_.A22;
K_(1, 2) = params_.v0;
Kl_ = Eigen::Matrix3d::Identity();
Kl_(0, 0) = params_.A11 / params_.scale;
Kl_(0, 1) = 0.0;
Kl_(0, 2) = params_.u0 / params_.scale;
Kl_(1, 1) = params_.A22 / params_.scale;
Kl_(1, 2) = params_.v0 / params_.scale;
K_4x4_ = Eigen::Matrix4d::Identity();
K_4x4_.block<3, 3>(0, 0) = Kl_;
Kcl_ = K_4x4_ * params_.Tcl;
}
void PointCloudToDepthConverter::createDistortionMaps()
{
map_x_ = cv::Mat::zeros(params_.image_height, params_.image_width, CV_32FC1);
map_y_ = cv::Mat::zeros(params_.image_height, params_.image_width, CV_32FC1);
for (int u = 0; u < params_.image_width; ++u)
{
for (int v = 0; v < params_.image_height; ++v)
{
double y = (v - params_.v0) / params_.A22;
double x = (u - params_.u0 - params_.A12 * y) / params_.A11;
double r = sqrt(x * x + y * y);
double theta = atan(r);
double theta_d = theta + params_.k2 * pow(theta, 2) + params_.k3 * pow(theta, 3) +
params_.k4 * pow(theta, 4) + params_.k5 * pow(theta, 5) +
params_.k6 * pow(theta, 6) + params_.k7 * pow(theta, 7);
double x_distorted = x * (r / theta_d);
double y_distorted = y * (r / theta_d);
map_x_.at<float>(v, u) = static_cast<float>(x_distorted * params_.A11 + params_.A12 * y_distorted + params_.u0);
map_y_.at<float>(v, u) = static_cast<float>(y_distorted * params_.A22 + params_.v0);
}
}
inv_map_x_ = cv::Mat::zeros(params_.image_height, params_.image_width, CV_32FC1);
inv_map_y_ = cv::Mat::zeros(params_.image_height, params_.image_width, CV_32FC1);
for (int u = 0; u < params_.image_width; ++u)
{
for (int v = 0; v < params_.image_height; ++v)
{
double y = (v - params_.v0) / params_.A22;
double x = (u - params_.u0 - params_.A12 * y) / params_.A11;
double r = sqrt(x * x + y * y);
double theta = atan(r);
double theta_d = theta + params_.k2 * pow(theta, 2) + params_.k3 * pow(theta, 3) +
params_.k4 * pow(theta, 4) + params_.k5 * pow(theta, 5) +
params_.k6 * pow(theta, 6) + params_.k7 * pow(theta, 7);
double x_distorted = x * (theta_d / r);
double y_distorted = y * (theta_d / r);
inv_map_x_.at<float>(v, u) = static_cast<float>(x_distorted * params_.A11 + params_.A12 * y_distorted + params_.u0);
inv_map_y_.at<float>(v, u) = static_cast<float>(y_distorted * params_.A22 + params_.v0);
}
}
}
PointCloudToDepthConverter::ProcessResult PointCloudToDepthConverter::processCloudAndImage(
const pcl::PointCloud<pcl::PointXYZ> &cloud,
const cv::Mat &image)
{
ProcessResult result;
result.success = false;
auto validation_result = validateInputs(cloud, image);
if (!validation_result.first)
{
result.error_message = validation_result.second;
return result;
}
try
{
pcl::PointCloud<pcl::PointXYZ> cloud_in_cam;
pcl::transformPointCloud(cloud, cloud_in_cam, Kcl_);
cv::Mat depth_img = projectCloudToDepth(cloud_in_cam);
cv::Mat processed_depth = postProcessDepthImage(depth_img);
pcl::PointCloud<pcl::PointXYZRGB> colored_cloud = generateColoredCloud(processed_depth, image);
result.depth_image = processed_depth;
result.colored_cloud = colored_cloud;
result.success = true;
}
catch (const std::exception &e)
{
result.error_message = std::string("Processing error: ") + e.what();
}
return result;
}
cv::Mat PointCloudToDepthConverter::projectCloudToDepth(const pcl::PointCloud<pcl::PointXYZ> &cloud_in_cam)
{
cv::Mat depth_img = cv::Mat::zeros(scaled_height_, scaled_width_, CV_32FC1);
for (const auto &camera_point : cloud_in_cam)
{
if (camera_point.z <= 0)
continue;
int u = static_cast<int>(std::round(camera_point.x / camera_point.z));
int v = static_cast<int>(std::round(camera_point.y / camera_point.z));
if (u >= 0 && u < scaled_width_ && v >= 0 && v < scaled_height_)
{
depth_img.at<float>(v, u) = static_cast<float>(camera_point.z);
for (int du = -1; du <= 1; ++du)
{
for (int dv = -1; dv <= 1; ++dv)
{
int nu = u + du;
int nv = v + dv;
if (nu >= 0 && nu < scaled_width_ && nv >= 0 && nv < scaled_height_)
{
if (depth_img.at<float>(nv, nu) == 0.0f)
{
depth_img.at<float>(nv, nu) = static_cast<float>(camera_point.z);
}
}
}
}
}
}
return depth_img;
}
cv::Mat PointCloudToDepthConverter::postProcessDepthImage(const cv::Mat &depth_img) {
if (depth_img.empty()) {
std::cerr << "ERROR: Input depth image is empty!" << std::endl;
return cv::Mat();
}
if (depth_img.data == nullptr) {
std::cerr << "ERROR: Input depth image has null data pointer!" << std::endl;
return cv::Mat();
}
if (depth_img.rows <= 0 || depth_img.cols <= 0) {
std::cerr << "ERROR: Invalid input dimensions: "
<< depth_img.rows << "x" << depth_img.cols << std::endl;
return cv::Mat();
}
if (params_.image_width <= 0 || params_.image_height <= 0) {
std::cerr << "ERROR: Invalid target size: "
<< params_.image_width << "x" << params_.image_height << std::endl;
return cv::Mat();
}
cv::Mat safe_input = depth_img.clone();
if (safe_input.empty()) {
std::cerr << "ERROR: Failed to create safe copy of input image!" << std::endl;
return cv::Mat();
}
cv::Mat depth_img_upsampled;
try {
depth_img_upsampled = customResize(safe_input, cv::Size(1600, 1296));
} catch (const std::exception& e) {
std::cerr << "ERROR: Custom resize failed: " << e.what() << std::endl;
return cv::Mat();
}
if (depth_img_upsampled.empty()) {
std::cerr << "ERROR: Resized image is empty!" << std::endl;
return cv::Mat();
}
if (depth_img_upsampled.rows != 1296 || depth_img_upsampled.cols != 1600) {
std::cerr << "ERROR: Resized image has wrong dimensions: "
<< depth_img_upsampled.cols << "x" << depth_img_upsampled.rows
<< " (expected " << 1600 << "x" << 1296 << ")" << std::endl;
return cv::Mat();
}
cv::Mat grad_x, grad_y, grad_magnitude;
try {
cv::Sobel(depth_img_upsampled, grad_x, CV_32F, 1, 0, 3);
cv::Sobel(depth_img_upsampled, grad_y, CV_32F, 0, 1, 3);
cv::magnitude(grad_x, grad_y, grad_magnitude);
} catch (const cv::Exception& e) {
std::cerr << "ERROR: Sobel/magnitude failed: " << e.what() << std::endl;
return cv::Mat();
}
if (grad_magnitude.type() != CV_32F) {
std::cerr << "ERROR: grad_magnitude has wrong type: "
<< grad_magnitude.type() << " (expected CV_32F)" << std::endl;
return cv::Mat();
}
cv::Mat threshold_mask;
try {
cv::threshold(grad_magnitude, threshold_mask, 0.75, 1, cv::THRESH_BINARY);
threshold_mask.convertTo(threshold_mask, CV_8U);
depth_img_upsampled.setTo(0, threshold_mask);
} catch (const cv::Exception& e) {
std::cerr << "ERROR: Threshold mask failed: " << e.what() << std::endl;
return cv::Mat();
}
return depth_img_upsampled;
}
cv::Mat PointCloudToDepthConverter::customResize(const cv::Mat& src, const cv::Size& size) {
if (src.empty()) {
throw std::runtime_error("Source image is empty");
}
if (size.width <= 0 || size.height <= 0) {
throw std::runtime_error("Invalid target size");
}
cv::Mat dst(size.height, size.width, src.type());
float scale_x = src.cols / static_cast<float>(size.width);
float scale_y = src.rows / static_cast<float>(size.height);
if (src.channels() != 1 || src.type() != CV_32F) {
throw std::runtime_error("Unsupported image type - expected single channel float");
}
for (int y = 0; y < dst.rows; y++) {
int src_y = static_cast<int>(y * scale_y);
src_y = std::min(src_y, src.rows - 1);
for (int x = 0; x < dst.cols; x++) {
int src_x = static_cast<int>(x * scale_x);
src_x = std::min(src_x, src.cols - 1);
dst.at<float>(y, x) = src.at<float>(src_y, src_x);
}
}
return dst;
}
pcl::PointCloud<pcl::PointXYZRGB> PointCloudToDepthConverter::generateColoredCloud(
const cv::Mat &depth_img, const cv::Mat &color_img)
{
cv::Mat depth_undistorted, color_undistorted;
depth_undistorted = depth_img.clone();
cv::remap(color_img, color_undistorted, inv_map_x_, inv_map_y_, cv::INTER_LINEAR);
pcl::PointCloud<pcl::PointXYZRGB> cloud_colored;
Eigen::Matrix4d Tlc = params_.Tcl.inverse();
for (int v = 0; v < depth_undistorted.rows; v += params_.point_sampling_rate)
{
for (int u = 0; u < depth_undistorted.cols; u += params_.point_sampling_rate)
{
float depth = depth_undistorted.at<float>(v, u);
if (depth > 0.1f && depth < 100.0f)
{
double y_cam = (v - params_.v0) * depth / params_.A22;
double x_cam = ((u - params_.u0) * depth - params_.A12 * y_cam)/ params_.A11;
double z_cam = depth;
Eigen::Vector4d point_cam(x_cam, y_cam, z_cam, 1.0);
Eigen::Vector4d point_lidar = Tlc * point_cam;
pcl::PointXYZRGB point;
point.x = static_cast<float>(point_lidar[0]);
point.y = static_cast<float>(point_lidar[1]);
point.z = static_cast<float>(point_lidar[2]);
if (u < color_undistorted.cols && v < color_undistorted.rows)
{
cv::Vec3b color = color_undistorted.at<cv::Vec3b>(v, u);
point.b = color[0];
point.g = color[1];
point.r = color[2];
}
else
{
point.r = point.g = point.b = 255;
}
cloud_colored.points.push_back(point);
}
}
}
cloud_colored.width = cloud_colored.points.size();
cloud_colored.height = 1;
cloud_colored.is_dense = false;
return cloud_colored;
}
std::pair<bool, std::string> PointCloudToDepthConverter::validateInputs(
const pcl::PointCloud<pcl::PointXYZ> &cloud, const cv::Mat &image)
{
if (cloud.empty())
{
return {false, "Empty point cloud"};
}
if (image.empty())
{
return {false, "Empty image"};
}
if (params_.A11 < 1e-6 || params_.A22 < 1e-6)
{
return {false, "Invalid camera intrinsics"};
}
return {true, ""};
}
void PointCloudToDepthConverter::updateCameraParams(const CameraParams &params)
{
params_ = params;
initializeInternalParams();
createDistortionMaps();
}
@@ -0,0 +1,299 @@
/*
Copyright 2025 Manifold Tech Ltd.(www.manifoldtech.com.co)
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
*/
#include "rawCloudRender.h"
#include <yaml-cpp/yaml.h>
#include <iostream>
#include <vector>
#include <Eigen/Dense>
#include <algorithm>
#include <iostream>
#include <cmath>
#include <array>
#include <algorithm>
struct ValidPointInfo {
float x;
float y;
float z;
int u;
int v;
};
namespace GlobalCameraParams {
float g_fx = 0.0f;
float g_fy = 0.0f;
float g_cx = 0.0f;
float g_cy = 0.0f;
float g_skew = 0.0f;
float g_k2 = 0.0f;
float g_k3 = 0.0f;
float g_k4 = 0.0f;
float g_k5 = 0.0f;
float g_k6 = 0.0f;
float g_k7 = 0.0f;
Eigen::Matrix4f g_T_camera_lidar = Eigen::Matrix4f::Identity();
}
bool raw_debug=0;
bool rawCloudRender::init(const std::string& yamlFilePath) {
YAML::Node config;
try {
config = YAML::LoadFile(yamlFilePath);
} catch (const YAML::BadFile& e) {
std::cerr << "Error: Could not open file '" << yamlFilePath << "' - " << e.what() << std::endl;
return false;
} catch (const YAML::ParserException& e) {
std::cerr << "Error: YAML parsing failed - " << e.what() << std::endl;
return false;
} catch (const std::exception& e) {
std::cerr << "Unexpected error: " << e.what() << std::endl;
return false;
}
// Intrinsic parameters node (cam_0)
const std::string cam_node_name = "cam_0";
if (!config[cam_node_name]) {
std::cerr << "Error: Missing camera node '" << cam_node_name << "'" << std::endl;
return false;
}
YAML::Node cam_node = config[cam_node_name];
// Directly access Tcl_0 node
const std::string tcl_node_name = "Tcl_0";
if (!config[tcl_node_name]) {
std::cerr << "Error: Missing transformation matrix node '" << tcl_node_name << "'" << std::endl;
return false;
}
YAML::Node tclNode = config[tcl_node_name];
if (tclNode.size() != 16) {
std::cerr << "Error: Transformation matrix must be 4x4 (16 elements)" << std::endl;
return false;
}
// === Read intrinsic parameters ===
GlobalCameraParams::g_k2 = cam_node["k2"].as<float>();
GlobalCameraParams::g_k3 = cam_node["k3"].as<float>();
GlobalCameraParams::g_k4 = cam_node["k4"].as<float>();
GlobalCameraParams::g_k5 = cam_node["k5"].as<float>();
GlobalCameraParams::g_k6 = cam_node["k6"].as<float>();
GlobalCameraParams::g_k7 = cam_node["k7"].as<float>();
GlobalCameraParams::g_fx = cam_node["A11"].as<float>();
GlobalCameraParams::g_skew = cam_node["A12"].as<float>();
GlobalCameraParams::g_fy = cam_node["A22"].as<float>();
GlobalCameraParams::g_cx = cam_node["u0"].as<float>();
GlobalCameraParams::g_cy = cam_node["v0"].as<float>();
// === Read extrinsic transformation matrix ===
GlobalCameraParams::g_T_camera_lidar <<
tclNode[0].as<float>(), tclNode[1].as<float>(), tclNode[2].as<float>(), tclNode[3].as<float>(),
tclNode[4].as<float>(), tclNode[5].as<float>(), tclNode[6].as<float>(), tclNode[7].as<float>(),
tclNode[8].as<float>(), tclNode[9].as<float>(), tclNode[10].as<float>(), tclNode[11].as<float>(),
tclNode[12].as<float>(), tclNode[13].as<float>(), tclNode[14].as<float>(), tclNode[15].as<float>();
// === Display key parameters concisely ===
std::cout << "=== Camera Calibration Parameters ===" << std::endl;
std::cout << "Intrinsics:" << std::endl;
std::cout << " fx: " << GlobalCameraParams::g_fx
<< ", fy: " << GlobalCameraParams::g_fy
<< ", cx: " << GlobalCameraParams::g_cx
<< ", cy: " << GlobalCameraParams::g_cy << std::endl;
std::cout << "Distortion: k2=" << GlobalCameraParams::g_k2
<< ", k3=" << GlobalCameraParams::g_k3 << std::endl;
Eigen::Vector3f translation = GlobalCameraParams::g_T_camera_lidar.block<3,1>(0,3);
Eigen::Matrix3f rotation = GlobalCameraParams::g_T_camera_lidar.block<3,3>(0,0);
std::cout << "Extrinsics:" << std::endl;
std::cout << " Translation: [" << translation.x() << ", "
<< translation.y() << ", " << translation.z() << "]" << std::endl;
std::cout << " Rotation (euler angles): "
<< rotation.eulerAngles(0,1,2).transpose() * 180/M_PI << "°" << std::endl;
return true;
}
void rawCloudRender::render(std::vector<std::vector<float>>& rgb_image,
capture_Image_List_t* pcd_stream,
int pcdIdx,
std::vector<float>& rgbCloud_flat)
{
// Initialize constants
constexpr float inv_1000 = 0.001f;
const float fx = GlobalCameraParams::g_fx;
const float fy = GlobalCameraParams::g_fy;
const float cx = GlobalCameraParams::g_cx;
const float cy = GlobalCameraParams::g_cy;
const float skew = GlobalCameraParams::g_skew;
const float k2 = GlobalCameraParams::g_k2;
const float k3 = GlobalCameraParams::g_k3;
const float k4 = GlobalCameraParams::g_k4;
const float k5 = GlobalCameraParams::g_k5;
const float k6 = GlobalCameraParams::g_k6;
const float k7 = GlobalCameraParams::g_k7;
const Eigen::Matrix4f& T = GlobalCameraParams::g_T_camera_lidar;
// Precompute matrix elements
const float T00 = T(0,0), T01 = T(0,1), T02 = T(0,2), T03 = T(0,3);
const float T10 = T(1,0), T11 = T(1,1), T12 = T(1,2), T13 = T(1,3);
const float T20 = T(2,0), T21 = T(2,1), T22 = T(2,2), T23 = T(2,3);
// Initialize lookup table
static std::array<float, 10000> dist_table;
static bool table_init = [&](){
for (size_t i=0; i<dist_table.size(); ++i) {
float theta = i * (M_PI/2) / dist_table.size();
dist_table[i] = theta*(1 + theta*(k2 + theta*(k3 + theta*(k4 + theta*(k5 + theta*(k6 + theta*k7))))));
}
return true;
}();
// Get point cloud data (direct access)
if (!pcd_stream || pcdIdx < 0 || pcdIdx >= 10) {
std::cerr << "ERROR: Invalid pcd_stream or index in render function" << std::endl;
return;
}
buffer_List_t& pcd_buffer = pcd_stream->imageList[pcdIdx];
const int total_points = pcd_buffer.height * pcd_buffer.width;
if (!pcd_buffer.pAddr) {
std::cerr << "ERROR: Null point cloud data pointer in render function" << std::endl;
return;
}
float* data = static_cast<float*>(pcd_buffer.pAddr);
// Prepare output
rgbCloud_flat.clear();
rgbCloud_flat.resize(total_points * 4); // Preallocate maximum space
float* output_ptr = rgbCloud_flat.data();
// Get image dimensions
const int img_height = 1296;
const int img_width = 1600;
// Process point cloud
int valid_count = 0;
for (int idx = 0; idx < total_points; ++idx)
{
float* pf = data + idx*4;
// Quick check for invalid points
if (std::abs(pf[0]) < 1e-5f && std::abs(pf[1]) < 1e-5f && std::abs(pf[2]) < 1e-5f) {
continue;
}
// Coordinate transformation
const float x = pf[2] * inv_1000;
const float y = -pf[0] * inv_1000;
const float z = pf[1] * inv_1000;
// Manual matrix transformation
const float x1 = T00*x + T01*y + T02*z + T03;
const float y1 = T10*x + T11*y + T12*z + T13;
const float z1 = T20*x + T21*y + T22*z + T23;
// Check for points behind camera
if (z1 <= 0.0f) continue;
// Calculate projection
const float x1_sq = x1*x1;
const float y1_sq = y1*y1;
const float z1_sq = z1*z1;
const float norm = std::sqrt(x1_sq + y1_sq + z1_sq);
if (norm < 1e-7f) continue;
const float r = std::sqrt(x1_sq + y1_sq);
if (r < 1e-7f) continue;
const float cost = z1 / norm;
const float theta = std::acos(cost);
// Safe table lookup
const size_t table_idx = static_cast<size_t>(theta * (2.0f/M_PI) * dist_table.size());
const size_t safe_idx = std::min(table_idx, dist_table.size()-1);
const float thetad = dist_table[safe_idx];
const float scaling = thetad / r;
const float xd = x1 * scaling;
const float yd = y1 * scaling;
const float pd_2d_x = xd * fx + yd * skew + cx;
const float pd_2d_y = yd * fy + cy;
// Quick boundary check
const int u = static_cast<int>(pd_2d_x);
const int v = static_cast<int>(pd_2d_y);
// Strict boundary check
if (u < 0 || u >= img_width || v < 0 || v >= img_height) {
continue;
}
// Safe image data access
if (v < static_cast<int>(rgb_image.size()) && u < static_cast<int>(rgb_image[v].size())) {
*output_ptr++ = x;
*output_ptr++ = y;
*output_ptr++ = z;
*output_ptr++ = rgb_image[v][u];
valid_count++;
} else {
// Handle invalid coordinates
static bool warned = false;
if (!warned) {
if(raw_debug)
{
std::cerr << "WARNING: Invalid image coordinates: u=" << u << ", v=" << v
<< " (image size: " << rgb_image.size() << "x"
<< (rgb_image.empty() ? 0 : rgb_image[0].size()) << ")" << std::endl;
}
warned = true;
}
}
}
// Resize output
rgbCloud_flat.resize(output_ptr - rgbCloud_flat.data());
if(raw_debug)
{
std::cout << "Render completed: " << total_points << " points processed, "
<< valid_count << " valid points ("
<< (100.0 * valid_count / total_points) << "%)" << std::endl;
}
}
void rawCloudRender::print_camera_calib() {
std::cout << model_type_ << std::endl;
std::cout << image_width_ << std::endl;
std::cout << image_height_ << std::endl;
std::cout << "T_camera_lidar" << std::endl;
std::cout << T_camera_lidar_(0,0) << " " << T_camera_lidar_(0,1) << " " << T_camera_lidar_(0,2) << " " << T_camera_lidar_(0,3) << std::endl;
std::cout << T_camera_lidar_(1,0) << " " << T_camera_lidar_(1,1) << " " << T_camera_lidar_(1,2) << " " << T_camera_lidar_(1,3) << std::endl;
std::cout << T_camera_lidar_(2,0) << " " << T_camera_lidar_(2,1) << " " << T_camera_lidar_(2,2) << " " << T_camera_lidar_(2,3) << std::endl;
std::cout << T_camera_lidar_(3,0) << " " << T_camera_lidar_(3,1) << " " << T_camera_lidar_(3,2) << " " << T_camera_lidar_(3,3) << std::endl;
std::cout << "cam" << std::endl;
std::cout << k2_ << std::endl;
std::cout << k3_ << std::endl;
std::cout << k4_ << std::endl;
std::cout << k5_ << std::endl;
std::cout << k6_ << std::endl;
std::cout << k7_ << std::endl;
std::cout << A11_fx_ << std::endl;
std::cout << A12_skew_ << std::endl;
std::cout << A22_fy_ << std::endl;
std::cout << u0_cx_ << std::endl;
std::cout << v0_cy_ << std::endl;
}
@@ -0,0 +1,246 @@
/*
Copyright 2025 Manifold Tech Ltd.(www.manifoldtech.com.co)
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
*/
#include "yaml_parser.h"
#include <fstream>
#include <filesystem>
#include <iostream>
#include <algorithm>
#include <iomanip>
#include <cstring>
namespace odin_ros_driver {
YamlParser::YamlParser(const std::string& config_file)
: config_file_(config_file) {}
bool YamlParser::loadConfig() {
try {
std::cerr << "Loading config file: " << config_file_ << std::endl;
// Check if file exists
if (!std::filesystem::exists(config_file_)) {
std::cerr << "Config file not found: " << config_file_ << std::endl;
return false;
}
// Print file contents
std::ifstream file(config_file_);
std::string content((std::istreambuf_iterator<char>(file)),
std::istreambuf_iterator<char>());
std::cerr << "Config file content:\n" << content << "\n--- End of file ---" << std::endl;
// Load YAML
YAML::Node config = YAML::LoadFile(config_file_);
// Check if 'register_keys' node exists
if (!config["register_keys"]) {
std::cerr << "Missing 'register_keys' section in config file" << std::endl;
return false;
}
YAML::Node register_keys = config["register_keys"];
register_keys_.clear();
register_keys_str_val_.clear();
custom_parameters_.clear();
// Print number of key-value pairs found
std::cerr << "Found " << register_keys.size() << " keys in config" << std::endl;
for (YAML::const_iterator it = register_keys.begin(); it != register_keys.end(); ++it) {
std::string key = it->first.as<std::string>();
const YAML::Node& value_node = it->second;
// Convert key to lowercase
std::transform(key.begin(), key.end(), key.begin(),
[](unsigned char c){ return std::tolower(c); });
// Check if this is a custom parameter
if (key.substr(0, 7) == "custom_") {
std::string param_name = key.substr(7);
// Handle different value types
if (value_node.IsScalar()) {
// Single scalar value (int or float)
try {
int int_value = value_node.as<int>();
ParameterValue param_value;
param_value.type = DataType::INT_TYPE;
param_value.setData(int_value);
custom_parameters_[param_name] = param_value;
std::cerr << "Loaded custom parameter (int): " << param_name << " = " << int_value << std::endl;
} catch (...) {
try {
double float_value = value_node.as<double>();
ParameterValue param_value;
param_value.type = DataType::FLOAT_ARRAY_TYPE;
std::vector<float> float_array = {static_cast<float>(float_value)};
param_value.setArray(float_array);
custom_parameters_[param_name] = param_value;
std::cerr << "Loaded custom parameter (float): " << param_name << " = " << float_value << std::endl;
} catch (const std::exception& e) {
std::cerr << "Failed to parse custom parameter " << param_name << ": " << e.what() << std::endl;
}
}
} else if (value_node.IsSequence()) {
// Array of values
size_t array_size = value_node.size();
if (array_size == 0) {
std::cerr << "Empty array for custom parameter: " << param_name << std::endl;
continue;
}
// Try to detect if it's a float or int array based on first element
try {
// Try to parse as float array first
std::vector<float> float_array;
for (size_t i = 0; i < array_size; ++i) {
float_array.push_back(value_node[i].as<float>());
}
ParameterValue param_value;
param_value.type = DataType::FLOAT_ARRAY_TYPE;
param_value.setArray(float_array);
custom_parameters_[param_name] = param_value;
std::cerr << "Loaded custom parameter (float array): " << param_name << " = [";
for (size_t i = 0; i < float_array.size(); ++i) {
if (i > 0) std::cerr << ", ";
std::cerr << std::fixed << std::setprecision(4) << float_array[i];
}
std::cerr << "]" << std::endl;
} catch (const std::exception& e) {
std::cerr << "Failed to parse custom parameter array " << param_name << ": " << e.what() << std::endl;
}
}
} else if (allowed_key_w_str_val.find(key) != allowed_key_w_str_val.end()) {
try {
std::string value = value_node.as<std::string>();
register_keys_str_val_[key] = value;
std::cerr << "Loaded key: " << key << " = " << value << std::endl;
} catch (const std::exception& e) {
std::cerr << "Failed to parse key " << key << ": " << e.what() << std::endl;
}
} else if (allowed_key_w_float_val.find(key) != allowed_key_w_float_val.end()) {
try {
double value = value_node.as<double>();
register_keys_float_val_[key] = value;
std::cerr << "Loaded key: " << key << " = " << value << std::endl;
} catch (const std::exception& e) {
std::cerr << "Failed to parse key " << key << ": " << e.what() << std::endl;
}
} else {
// Regular (non-custom) integer parameter
try {
int value = value_node.as<int>();
register_keys_[key] = value;
std::cerr << "Loaded key: " << key << " = " << value << std::endl;
} catch (const std::exception& e) {
std::cerr << "Failed to parse key " << key << ": " << e.what() << std::endl;
}
}
}
return true;
} catch (const YAML::Exception& e) {
std::cerr << "YAML exception: " << e.what() << std::endl;
return false;
} catch (const std::exception& e) {
std::cerr << "Exception: " << e.what() << std::endl;
return false;
}
}
const std::map<std::string, int>& YamlParser::getRegisterKeys() const {
return register_keys_;
}
const std::map<std::string, ParameterValue>& YamlParser::getCustomParameters() const {
return custom_parameters_;
}
const std::map<std::string, std::string>& YamlParser::getRegisterKeysStrVal() const {
return register_keys_str_val_;
}
void YamlParser::printConfig() const {
std::cerr << "Configuration Keys:" << std::endl;
if (register_keys_.empty()) {
std::cerr << " (int val empty)" << std::endl;
} else {
for (const auto& [key, value] : register_keys_) {
std::cerr << " " << key << ": " << value << std::endl;
}
}
if (register_keys_str_val_.empty()) {
std::cerr << " (str_val empty)" << std::endl;
} else {
for (const auto& [key, value] : register_keys_str_val_) {
std::cerr << " " << key << ": " << value << std::endl;
}
}
std::cerr << "Custom Parameters:" << std::endl;
if (custom_parameters_.empty()) {
std::cerr << " (custom param empty)" << std::endl;
} else {
for (const auto& [key, param_val] : custom_parameters_) {
std::cerr << " " << key << ": (size=" << param_val.getSize() << " bytes)";
if (param_val.type == DataType::INT_TYPE && param_val.getSize() == sizeof(int)) {
int int_val = *reinterpret_cast<const int*>(param_val.getData());
std::cerr << " = " << int_val;
} else if (param_val.type == DataType::FLOAT_ARRAY_TYPE && param_val.getSize() % sizeof(float) == 0) {
size_t count = param_val.getSize() / sizeof(float);
const float* float_arr = reinterpret_cast<const float*>(param_val.getData());
std::cerr << " = [";
for (size_t i = 0; i < count; ++i) {
if (i > 0) std::cerr << ", ";
std::cerr << std::fixed << std::setprecision(4) << float_arr[i];
}
std::cerr << "]";
}
std::cerr << std::endl;
}
}
}
bool YamlParser::applyCustomParameters(device_handle device) {
bool success = true;
for (const auto& [param_name, param_value] : custom_parameters_) {
std::cerr << "Setting custom parameter: " << param_name << " (size=" << param_value.getSize() << " bytes)" << std::endl;
int result = lidar_set_custom_parameter(device, param_name.c_str(), param_value.getData(), param_value.getSize());
if (result != 0) {
std::cerr << "Failed to set custom parameter " << param_name << ": error code " << result << std::endl;
success = false;
} else {
std::cerr << "Successfully set custom parameter " << param_name << std::endl;
}
}
return success;
}
int YamlParser::getCustomParameterInt(const std::string& param_name, int default_value) const {
auto it = custom_parameters_.find(param_name);
if (it != custom_parameters_.end()) {
const ParameterValue& param_value = it->second;
if (param_value.type == DataType::INT_TYPE && param_value.getSize() == sizeof(int)) {
return *reinterpret_cast<const int*>(param_value.getData());
}
}
return default_value;
}
}
@@ -0,0 +1,11 @@
cmake_minimum_required(VERSION 3.8)
project(sim2real_bringup)
find_package(ament_cmake REQUIRED)
install(
DIRECTORY launch config
DESTINATION share/${PROJECT_NAME}
)
ament_package()
@@ -0,0 +1,103 @@
# =============================================================================
# 部署契约参考文件(仅供参考,C++ 代码不读取此文件)
# =============================================================================
#
# ⚠️ 注意:所有部署参数(电机映射、动作缩放、默认姿态等)均硬编码在
# sim2real_common/include/sim2real_common/deployment_contract.hpp 中。
# 本 YAML 文件仅作为可读参考,修改此文件不会影响运行时行为!
# 如需修改部署参数,请同步更新 .hpp 文件和本文件。
#
# =============================================================================
model:
path: "policies/model_rough.onnx"
source_pt: "policies/model_rough.pt"
backend: "onnxruntime"
obs_dim: 53
action_dim: 16
clip_obs: 100.0
observation:
terms:
- {name: base_ang_vel, dim: 3, scale: 0.25}
- {name: projected_gravity, dim: 3}
- {name: command, dim: 3}
- {name: joint_pos_rel, dim: 12}
- {name: joint_vel_rel, dim: 12, scale: 0.05}
- {name: wheel_vel, dim: 4, scale: 0.05}
- {name: last_actions, dim: 16}
action:
joint_order:
- fl_hip_abduction
- fl_hip_pitch
- fl_knee
- fr_hip_abduction
- fr_hip_pitch
- fr_knee
- rl_hip_abduction
- rl_hip_pitch
- rl_knee
- rr_hip_abduction
- rr_hip_pitch
- rr_knee
- fl_wheel
- fr_wheel
- rl_wheel
- rr_wheel
wheel_indices: [12, 13, 14, 15]
scale: [0.125, 0.25, 0.25, 0.125, 0.25, 0.25, 0.125, 0.25, 0.25, 0.125, 0.25, 0.25, 5.0, 5.0, 5.0, 5.0]
default_dof_pos: [0.0, 0.550, -1.125, 0.0, 0.550, -1.125, 0.0, 0.550, -1.125, 0.0, 0.550, -1.125, 0.0, 0.0, 0.0, 0.0]
motor_mapping:
can_id_map:
fl_hip_abduction: [1, 1]
fl_hip_pitch: [1, 2]
fl_knee: [1, 3]
fl_wheel: [1, 4]
fr_hip_abduction: [1, 5]
fr_hip_pitch: [1, 6]
fr_knee: [1, 7]
fr_wheel: [1, 8]
rl_hip_abduction: [2, 1]
rl_hip_pitch: [2, 2]
rl_knee: [2, 3]
rl_wheel: [2, 4]
rr_hip_abduction: [2, 5]
rr_hip_pitch: [2, 6]
rr_knee: [2, 7]
rr_wheel: [2, 8]
direction_map:
fl_hip_abduction: -1
fl_hip_pitch: -1
fl_knee: -1
fl_wheel: -1
fr_hip_abduction: -1
fr_hip_pitch: 1
fr_knee: 1
fr_wheel: 1
rl_hip_abduction: 1
rl_hip_pitch: -1
rl_knee: -1
rl_wheel: -1
rr_hip_abduction: 1
rr_hip_pitch: 1
rr_knee: 1
rr_wheel: 1
zero_offset_map:
fl_hip_abduction: 0.003
fl_hip_pitch: 0.030
fl_knee: 0.028
fl_wheel: 0.0
fr_hip_abduction: 0.004
fr_hip_pitch: 0.038
fr_knee: 0.011
fr_wheel: 0.0
rl_hip_abduction: 0.019
rl_hip_pitch: -0.034
rl_knee: 0.025
rl_wheel: 0.0
rr_hip_abduction: -0.001
rr_hip_pitch: 0.039
rr_knee: 0.018
rr_wheel: 0.0
@@ -0,0 +1,196 @@
/**:
ros__parameters:
policy_hz: 50.0
motor_hz: 200.0
status_hz: 10.0
target_timeout_ms: 150.0
event_log_dir: "logs_v2_web"
model_engine_path: policies/model_9600_fp16.engine
prefer_tensorrt: true
model_path: policies/model_9600.onnx
rough_model_engine_path: policies/model_9600_fp16.engine
crawl_model_path: policies/model_crawl.onnx
crawl_model_engine_path: ""
rough_default_dof_pos: [0.0, 0.550, -1.125, 0.0, 0.550, -1.125, 0.0, 0.550, -1.125, 0.0, 0.550, -1.125, 0.0, 0.0, 0.0, 0.0]
crawl_backend: "ik"
crawl_default_dof_pos: [0.2, 1.697, -2.650, -0.2, 1.697, -2.650, 0.2, 1.697, -2.650, -0.2, 1.697, -2.650, 0.0, 0.0, 0.0, 0.0]
crawl_ik_wheel_linear_gain: 6.25
crawl_ik_wheel_yaw_gain: 4.0
crawl_ik_max_wheel_speed: 6.0
crawl_ik_abduction_clip: 0.45
model_switch_transition_s: 1.5
model_switch_to_stand_transition_scale: 2.0
model_switch_to_model_transition_scale: 2.5
model_switch_stand_hold_s: 0.8
model_switch_stand_max_err: 0.18
model_switch_stand_max_vel: 0.8
model_switch_release_scale: 2.0
debug_trace_enabled: true
debug_trace_decimation: 1
use_cuda: true # 启用 CUDA Execution ProviderOrin Nano GPU 加速)
contract_file: deployment_contract.yaml
dry_run: false
can0_name: "can0"
can1_name: "can1"
imu_topic: "/odin1/imu"
odom_topic: "/odom"
# Remote UART / SBUS parameters, aligned with the Python deployment
remote_enabled: true
remote_port: "/dev/ttyACM0"
remote_baudrate: 100000
remote_timeout: 0.02
remote_axis_deadzone: 40
remote_active_threshold: 40
remote_axis_full_scale: 660.0
remote_max_vx: 0.85
remote_max_vy: 0.3
remote_max_yaw_rate: 0.5
remote_invert_vx: true
remote_invert_vy: false
remote_invert_yaw: true
remote_publish_inactive_zero: true
remote_estop_latch: true
remote_poll_hz: 50.0
remote_model_switch_enabled: true
remote_model_switch_channel: 10
remote_model_switch_debounce_frames: 3
remote_model_switch_rough_level: "low"
remote_model_switch_ik_level: "high"
# Command mux parameters
cmd_mux_default_mode: "NAV"
cmd_mux_output_hz: 50.0
cmd_mux_remote_timeout_ms: 250.0
cmd_mux_web_timeout_ms: 300.0
cmd_mux_nav_timeout_ms: 500.0
cmd_mux_max_vx: 0.8
cmd_mux_max_vy: 0.3
cmd_mux_max_yaw_rate: 0.5
cmd_mux_max_vx_acc: 1.0
cmd_mux_max_vy_acc: 1.0
cmd_mux_max_yaw_acc: 1.5
# Windows/Nano Web UDP bridge parameters
web_bridge_enabled: true
web_http_host: "0.0.0.0"
web_http_port: 18080
web_static_dir: "tools/win_web_debug/static"
web_udp_listen_host: "0.0.0.0"
web_udp_listen_port: 15000
web_udp_remote_host: ""
web_udp_remote_port: 15001
web_udp_state_hz: 20.0
web_udp_cmd_timeout_ms: 300.0
web_udp_max_packet_bytes: 8192
web_udp_max_vx: 0.8
web_udp_max_vy: 0.3
web_udp_max_yaw_rate: 0.5
web_udp_estop_on_timeout: false
# Safety parameters
safety_enabled: true
max_target_offset: 0.6
model_switch_max_target_offset: 1.8
hard_target_offset: 2.0
max_ang_vel: 24.0
max_tilt_z: -0.3
clip_to_brake: 0
imu_age_warn_ms: 60.0
imu_age_stop_ms: 200.0
wheel_no_effect_command_threshold: 1.0
wheel_no_effect_min_response_ratio: 0.20
wheel_no_effect_velocity_epsilon: 0.25
wheel_no_effect_max_temperature_c: 90.0
wheel_no_effect_min_bus_voltage_v: 18.0
wheel_no_effect_command_warmup_cycles: 12
wheel_no_effect_trigger_cycles: 30
wheel_no_effect_attempt_limit: 2
wheel_no_effect_cooldown_ms: 1200
wheel_recovery_verify_timeout_ms: 180
wheel_no_effect_diag_freshness_ms: 350
wheel_no_effect_diag_request_period_ms: 80
leg_no_effect_position_error_threshold: 0.18
leg_no_effect_velocity_epsilon: 0.12
leg_no_effect_max_estimated_current_arms: 4.0
leg_no_effect_max_abs_torque_nm: 5.0
leg_no_effect_max_temperature_c: 100.0
leg_no_effect_min_bus_voltage_v: 18.0
leg_no_effect_command_warmup_cycles: 40
leg_no_effect_trigger_cycles: 25
leg_no_effect_attempt_limit: 2
leg_no_effect_cooldown_ms: 1200
leg_recovery_verify_timeout_ms: 220
# Policy alignment with the Python deployment
command_release_s: 0.35
release_command_hold_s: 0.12
release_posture_max_err: 0.35
release_target_blend_s: 0.30
model_switch_release_scale: 2.0
clip_obs: 100.0
hold_zero_command_pose: true
enable_zero_cmd_suppression: true
require_active_command_to_release: true
zero_cmd_use_yaw_rate: true
# Simple navigation parameters
nav_map_frame: "map"
nav_odom_frame: "odom"
nav_base_frame: "base_link"
nav_control_hz: 20.0
nav_goal_tolerance: 0.20
nav_yaw_stop_threshold: 0.80
nav_max_vx: 0.45
nav_max_wz: 0.80
nav_kp_dist: 0.80
nav_kp_yaw: 1.80
nav_goal_exit_tolerance_margin: 0.08
nav_goal_complete_stable_cycles: 4
nav_final_align_kp_yaw_scale: 0.60
nav_final_align_max_wz: 0.45
nav_final_align_creep_speed: 0.05
nav_goal_yaw_tolerance_deg: 12.0
nav_astar_enabled: true
nav_astar_resolution: 0.10
nav_astar_pcd_sample_step: 5
nav_astar_allow_diagonal: true
nav_astar_smooth_enabled: true
nav_astar_corner_blend_dist: 0.20
nav_astar_waypoint_reach_dist: 0.18
nav_astar_lookahead_dist: 0.35
nav_astar_snap_radius: 0.60
nav_astar_max_expansions: 120000
nav_turn_in_place_enabled: true
nav_turn_in_place_enter_yaw_deg: 70.0
nav_turn_in_place_exit_yaw_deg: 18.0
nav_turn_in_place_max_wz: 0.80
nav_pre_dock_enabled: true
nav_pre_dock_distance: 0.35
nav_pre_dock_tolerance: 0.18
nav_pre_dock_skip_within_goal_dist: 0.45
nav_goals_file: src/sim2real_nav2/config/maps/factory_a/goals.yaml
nav_missions_file: src/sim2real_nav2/config/maps/factory_a/missions.yaml
# Keep the legacy YAML route for reference; active task is selected by nav_route_task_file below.
nav_route_file: src/sim2real_nav2/config/maps/a_min/test_route.yaml
# Task switch entry: change this path to another .json/.yaml route file, then relaunch or reload the nav nodes.
# The web "odom" button uses the first waypoint of this route as the fixed odom fallback start pose.
nav_route_task_file: map/routes/A_min/A_min_route.json
nav_route_auto_align_enabled: false
nav_route_rotation_offset_deg: 0.0
nav_route_align_max_angle_deg: 6.0
nav_route_align_angle_step_deg: 0.5
nav_route_align_search_radius: 0.35
odom_fallback_require_odom_fresh: true
odom_fallback_max_odom_age_ms: 500.0
odom_fallback_block_existing_map_odom_tf: true
odom_fallback_tf_conflict_window_s: 1.0
odom_fallback_tf_conflict_xy_tolerance: 0.05
odom_fallback_tf_conflict_yaw_tolerance_deg: 2.0
# Keep odom fallback running if Odin relocalizes mid-task; hand off after mission end or Exit odom.
odom_fallback_stop_on_external_tf: false
pcd_nav_file: map/A_min.pcd
pcd_floor_z_min: -1.6
pcd_floor_z_max: 0.4
pcd_sample_step: 25
pcd_robot_radius: 0.18
@@ -0,0 +1,152 @@
from launch import LaunchDescription
from launch.actions import DeclareLaunchArgument, IncludeLaunchDescription
from launch.launch_description_sources import PythonLaunchDescriptionSource
from launch.substitutions import LaunchConfiguration, PathJoinSubstitution, PythonExpression
from launch.conditions import IfCondition
from launch_ros.actions import Node
from launch_ros.parameter_descriptions import ParameterFile
from launch_ros.substitutions import FindPackageShare
def generate_launch_description():
runtime_params = ParameterFile(
PathJoinSubstitution([
FindPackageShare("sim2real_bringup"),
"config",
"runtime.yaml",
]),
allow_substs=True,
)
# Declare launch configurations
launch_driver_arg = DeclareLaunchArgument(
'launch_driver',
default_value='true',
description='Whether to launch the odin_ros_driver sensor node'
)
launch_nav2_arg = DeclareLaunchArgument(
'launch_nav2',
default_value='false',
description='Whether to launch the Nav2 navigation stack'
)
launch_remote_arg = DeclareLaunchArgument(
'launch_remote',
default_value='true',
description='Whether to launch the SBUS UART remote control node'
)
launch_web_bridge_arg = DeclareLaunchArgument(
'launch_web_bridge',
default_value='true',
description='Whether to launch the Windows/Nano UDP web debug bridge'
)
launch_simple_nav_arg = DeclareLaunchArgument(
'launch_simple_nav',
default_value='true',
description='Whether to launch the simple waypoint navigation node'
)
event_log_dir_arg = DeclareLaunchArgument(
'event_log_dir',
default_value=PythonExpression([
"'logs_v2_web/run_' + __import__('datetime').datetime.now().strftime('%Y-%m-%d_%H-%M-%S_%f')[:-3]"
]),
description='Per-run event log directory'
)
# Include odin_ros_driver launch
driver_launch = IncludeLaunchDescription(
PythonLaunchDescriptionSource(
PathJoinSubstitution([
FindPackageShare('odin_ros_driver'),
'launch',
'odin1_ros2.launch.py'
])
),
launch_arguments={'launch_rviz': 'false'}.items(),
condition=IfCondition(LaunchConfiguration('launch_driver'))
)
# Include sim2real_nav2 launch
nav2_launch = IncludeLaunchDescription(
PythonLaunchDescriptionSource(
PathJoinSubstitution([
FindPackageShare('sim2real_nav2'),
'launch',
'nav2.launch.py'
])
),
condition=IfCondition(LaunchConfiguration('launch_nav2'))
)
return LaunchDescription([
launch_driver_arg,
launch_nav2_arg,
launch_remote_arg,
launch_web_bridge_arg,
launch_simple_nav_arg,
event_log_dir_arg,
Node(
package="sim2real_hw",
executable="sim2real_hw_node",
name="sim2real_hw_node",
output="screen",
parameters=[runtime_params, {"event_log_dir": LaunchConfiguration("event_log_dir")}],
),
Node(
package="sim2real_runtime",
executable="sim2real_runtime_node",
name="sim2real_runtime_node",
output="screen",
parameters=[runtime_params, {"event_log_dir": LaunchConfiguration("event_log_dir")}],
),
Node(
package="sim2real_runtime",
executable="cmd_mux_node.py",
name="sim2real_cmd_mux_node",
output="screen",
parameters=[runtime_params],
),
Node(
package="sim2real_runtime",
executable="web_udp_bridge_node.py",
name="sim2real_web_udp_bridge_node",
output="screen",
parameters=[runtime_params],
condition=IfCondition(LaunchConfiguration('launch_web_bridge')),
),
Node(
package="sim2real_runtime",
executable="remote_uart_node.py",
name="sim2real_remote_uart_node",
output="screen",
parameters=[runtime_params],
condition=IfCondition(LaunchConfiguration('launch_remote')),
),
Node(
package="sim2real_runtime",
executable="simple_nav_node.py",
name="sim2real_simple_nav_node",
output="screen",
parameters=[runtime_params],
condition=IfCondition(LaunchConfiguration('launch_simple_nav')),
),
Node(
package="sim2real_runtime",
executable="odom_relay_node",
name="odom_relay_node",
output="screen",
parameters=[{
"odom_input_topic": "/odin1/odometry",
"odom_output_topic": "/odom",
"base_frame": "base_link",
"publish_tf": True,
}],
condition=IfCondition(LaunchConfiguration('launch_driver')),
),
driver_launch,
nav2_launch,
])
@@ -0,0 +1,24 @@
<?xml version="1.0"?>
<package format="3">
<name>sim2real_bringup</name>
<version>0.1.0</version>
<description>Launch and configuration package for sim2real_ros2.</description>
<maintainer email="todo@example.com">todo</maintainer>
<license>Proprietary</license>
<buildtool_depend>ament_cmake</buildtool_depend>
<exec_depend>launch</exec_depend>
<exec_depend>launch_ros</exec_depend>
<exec_depend>sim2real_common</exec_depend>
<exec_depend>sim2real_hw</exec_depend>
<exec_depend>sim2real_interfaces</exec_depend>
<exec_depend>sim2real_runtime</exec_depend>
<exec_depend>sim2real_nav2</exec_depend>
<exec_depend>odin_ros_driver</exec_depend>
<exec_depend>tf2_ros</exec_depend>
<export>
<build_type>ament_cmake</build_type>
</export>
</package>
@@ -0,0 +1,24 @@
cmake_minimum_required(VERSION 3.8)
project(sim2real_common)
find_package(ament_cmake REQUIRED)
add_library(${PROJECT_NAME} INTERFACE)
target_include_directories(${PROJECT_NAME} INTERFACE
$<BUILD_INTERFACE:${CMAKE_CURRENT_SOURCE_DIR}/include>
$<INSTALL_INTERFACE:include>
)
target_compile_features(${PROJECT_NAME} INTERFACE cxx_std_17)
install(
DIRECTORY include/
DESTINATION include
)
install(
TARGETS ${PROJECT_NAME}
EXPORT export_${PROJECT_NAME}
)
ament_export_targets(export_${PROJECT_NAME} HAS_LIBRARY_TARGET)
ament_package()
@@ -0,0 +1,94 @@
#pragma once
#include <array>
#include <cstddef>
namespace sim2real_common
{
struct DeploymentContract
{
static constexpr std::size_t kObsDim = 53;
static constexpr std::size_t kActionDim = 16;
static constexpr std::size_t kLegJointCount = 12;
static constexpr std::size_t kWheelCount = 4;
static constexpr double kPolicyHz = 50.0;
static constexpr double kMotorHz = 200.0;
static constexpr double kStatusHz = 10.0;
static constexpr std::array<int, 4> kWheelIndices = {12, 13, 14, 15};
static constexpr float kLegKp = 50.0f;
static constexpr float kLegKd = 1.5f;
static constexpr float kLegHoldKp = 80.0f;
static constexpr float kLegHoldKd = 4.0f;
static constexpr float kWheelKd = 1.0f;
static constexpr std::array<int, 16> kCanBusMap = {
1, 1, 1, // fl legs
1, 1, 1, // fr legs
2, 2, 2, // rl legs
2, 2, 2, // rr legs
1, 1, 2, 2 // wheels: fl, fr, rl, rr
};
static constexpr std::array<int, 16> kCanIdMap = {
1, 2, 3, // fl legs
5, 6, 7, // fr legs
1, 2, 3, // rl legs
5, 6, 7, // rr legs
4, 8, 4, 8 // wheels: fl, fr, rl, rr
};
static constexpr std::array<float, 16> kDirectionMap = {
-1.0f, -1.0f, -1.0f, // fl
-1.0f, 1.0f, 1.0f, // fr
1.0f, -1.0f, -1.0f, // rl
1.0f, 1.0f, 1.0f, // rr
-1.0f, 1.0f, -1.0f, 1.0f // wheels
};
static constexpr std::array<float, 16> kZeroOffsetMap = {
0.003f, 0.030f, 0.028f, // fl
0.004f, 0.038f, 0.011f, // fr
0.019f, -0.034f, 0.025f, // rl
-0.001f, 0.039f, 0.018f, // rr
0.000f, 0.000f, 0.000f, 0.000f // wheels
};
static constexpr std::array<float, 16> kActionScale = {
0.125f, 0.25f, 0.25f,
0.125f, 0.25f, 0.25f,
0.125f, 0.25f, 0.25f,
0.125f, 0.25f, 0.25f,
5.0f, 5.0f, 5.0f, 5.0f
};
static constexpr std::array<float, 16> kDefaultDofPos = {
0.0f, 0.550f, -1.125f,
0.0f, 0.550f, -1.125f,
0.0f, 0.550f, -1.125f,
0.0f, 0.550f, -1.125f,
0.0f, 0.0f, 0.0f, 0.0f
};
};
static constexpr std::array<const char *, 16> kJointLabels = {
"fl_hip_abduction",
"fl_hip_pitch",
"fl_knee",
"fr_hip_abduction",
"fr_hip_pitch",
"fr_knee",
"rl_hip_abduction",
"rl_hip_pitch",
"rl_knee",
"rr_hip_abduction",
"rr_hip_pitch",
"rr_knee",
"fl_wheel",
"fr_wheel",
"rl_wheel",
"rr_wheel"
};
} // namespace sim2real_common
@@ -0,0 +1,167 @@
#pragma once
#include <chrono>
#include <ctime>
#include <filesystem>
#include <fstream>
#include <iomanip>
#include <mutex>
#include <sstream>
#include <string>
#include <system_error>
namespace sim2real_common
{
class EventLogger
{
public:
static std::string makeRunId()
{
const auto now = std::chrono::system_clock::now();
const auto millis = std::chrono::duration_cast<std::chrono::milliseconds>(
now.time_since_epoch()).count() % 1000;
const std::time_t now_time = std::chrono::system_clock::to_time_t(now);
const std::tm tm_value = toLocalTm(now_time);
std::ostringstream oss;
oss << "run_"
<< std::put_time(&tm_value, "%Y-%m-%d_%H-%M-%S")
<< "_"
<< std::setw(3) << std::setfill('0') << millis;
return oss.str();
}
void configure(const std::string & log_dir, const std::string & file_stem)
{
std::scoped_lock<std::mutex> lock(mutex_);
log_dir_ = log_dir;
file_stem_ = file_stem;
}
std::string componentLogPath() const
{
std::scoped_lock<std::mutex> lock(mutex_);
return componentLogPathUnlocked();
}
std::string timelineLogPath() const
{
std::scoped_lock<std::mutex> lock(mutex_);
return timelineLogPathUnlocked();
}
std::string summaryLogPath() const
{
std::scoped_lock<std::mutex> lock(mutex_);
return summaryLogPathUnlocked();
}
void log(
const std::string & level,
const std::string & component,
const std::string & event,
const std::string & message)
{
std::scoped_lock<std::mutex> lock(mutex_);
if (!isConfiguredUnlocked()) {
return;
}
const std::string line = buildLogLine(level, component, event, message);
appendLineUnlocked(componentLogPathUnlocked(), line);
appendLineUnlocked(timelineLogPathUnlocked(), line);
}
void logSummary(const std::string & component, const std::string & message)
{
std::scoped_lock<std::mutex> lock(mutex_);
if (!isConfiguredUnlocked()) {
return;
}
const std::string line = buildLogLine("SUMMARY", component, "session_summary", message);
appendLineUnlocked(summaryLogPathUnlocked(), line);
appendLineUnlocked(timelineLogPathUnlocked(), line);
}
private:
static std::tm toLocalTm(std::time_t now_time)
{
std::tm tm_value{};
#ifdef _WIN32
localtime_s(&tm_value, &now_time);
#else
localtime_r(&now_time, &tm_value);
#endif
return tm_value;
}
static std::string formatTimestamp()
{
const auto now = std::chrono::system_clock::now();
const auto millis = std::chrono::duration_cast<std::chrono::milliseconds>(
now.time_since_epoch()).count() % 1000;
const std::time_t now_time = std::chrono::system_clock::to_time_t(now);
const std::tm tm_value = toLocalTm(now_time);
std::ostringstream oss;
oss << std::put_time(&tm_value, "%Y-%m-%d %H:%M:%S")
<< '.'
<< std::setw(3) << std::setfill('0') << millis;
return oss.str();
}
static std::string buildLogLine(
const std::string & level,
const std::string & component,
const std::string & event,
const std::string & message)
{
std::ostringstream oss;
oss << "[" << formatTimestamp() << "]"
<< "[" << level << "]"
<< "[" << component << "]"
<< "[" << event << "] "
<< message;
return oss.str();
}
bool isConfiguredUnlocked() const
{
return !log_dir_.empty() && !file_stem_.empty();
}
void appendLineUnlocked(const std::string & path, const std::string & line) const
{
std::error_code ec;
std::filesystem::create_directories(log_dir_, ec);
std::ofstream stream(path, std::ios::app);
if (!stream.is_open()) {
return;
}
stream << line << '\n';
}
std::string componentLogPathUnlocked() const
{
return (std::filesystem::path(log_dir_) / (file_stem_ + ".log")).string();
}
std::string timelineLogPathUnlocked() const
{
return (std::filesystem::path(log_dir_) / "timeline.log").string();
}
std::string summaryLogPathUnlocked() const
{
return (std::filesystem::path(log_dir_) / "summary.log").string();
}
mutable std::mutex mutex_;
std::string log_dir_;
std::string file_stem_;
};
} // namespace sim2real_common
@@ -0,0 +1,55 @@
#pragma once
#include <vector>
#include <cmath>
#include <algorithm>
#ifndef M_PI
#define M_PI 3.14159265358979323846
#endif
namespace sim2real_common
{
class LowPassFilter
{
public:
LowPassFilter(double cutoff_freq, double dt, std::size_t dim)
: dim_(dim), initialized_(false)
{
alpha_ = static_cast<float>(1.0 - std::exp(-2.0 * M_PI * cutoff_freq * dt));
y_prev_.resize(dim, 0.0f);
}
void filter(const float* x, float* y)
{
if (!initialized_) {
for (std::size_t i = 0; i < dim_; ++i) {
y_prev_[i] = x[i];
}
initialized_ = true;
}
for (std::size_t i = 0; i < dim_; ++i) {
y[i] = alpha_ * x[i] + (1.0f - alpha_) * y_prev_[i];
y_prev_[i] = y[i];
}
}
void filter(const std::vector<float>& x, std::vector<float>& y)
{
filter(x.data(), y.data());
}
void reset()
{
initialized_ = false;
}
private:
float alpha_;
std::size_t dim_;
bool initialized_;
std::vector<float> y_prev_;
};
} // namespace sim2real_common
@@ -0,0 +1,152 @@
#pragma once
#include <array>
#include <cmath>
#include <algorithm>
namespace sim2real_common
{
// Helper to calculate gravity orientation from quaternion [w, x, y, z]
inline std::array<float, 3> get_gravity_orientation(const std::array<float, 4>& quat_wxyz)
{
float qw = quat_wxyz[0];
float qx = quat_wxyz[1];
float qy = quat_wxyz[2];
float qz = quat_wxyz[3];
float gx = 2.0f * (-qz * qx + qw * qy);
float gy = -2.0f * (qz * qy + qw * qx);
float gz = 1.0f - 2.0f * (qw * qw + qz * qz);
return {gx, gy, gz};
}
// Helper to create quaternion from acceleration vector
inline std::array<float, 4> quat_from_accel(const std::array<float, 3>& accel)
{
float norm_a = std::sqrt(accel[0]*accel[0] + accel[1]*accel[1] + accel[2]*accel[2]);
if (norm_a < 1e-9f) {
return {1.0f, 0.0f, 0.0f, 0.0f};
}
float ax = accel[0] / norm_a;
float ay = accel[1] / norm_a;
float az = accel[2] / norm_a;
// Ref gravity vector is [0.0, 0.0, 1.0]
float cross_x = -ay;
float cross_y = ax;
float cross_z = 0.0f;
float dot = az;
if (dot < -0.999999f) {
return {0.0f, 1.0f, 0.0f, 0.0f};
}
float s = std::sqrt((1.0f + dot) * 2.0f);
std::array<float, 4> q = {
s * 0.5f,
cross_x / s,
cross_y / s,
cross_z / s
};
float norm_q = std::sqrt(q[0]*q[0] + q[1]*q[1] + q[2]*q[2] + q[3]*q[3]);
if (norm_q < 1e-9f) {
return {1.0f, 0.0f, 0.0f, 0.0f};
}
q[0] /= norm_q;
q[1] /= norm_q;
q[2] /= norm_q;
q[3] /= norm_q;
return q;
}
class MahonyFilter
{
public:
MahonyFilter(float kp = 2.0f, float ki = 0.0f)
: kp_(kp), ki_(ki)
{
q_ = {1.0f, 0.0f, 0.0f, 0.0f};
e_int_ = {0.0f, 0.0f, 0.0f};
}
void reset_with_accel(const std::array<float, 3>& accel)
{
q_ = quat_from_accel(accel);
e_int_ = {0.0f, 0.0f, 0.0f};
}
std::array<float, 4> update(const std::array<float, 3>& accel, const std::array<float, 3>& gyro, float dt)
{
float norm_a = std::sqrt(accel[0]*accel[0] + accel[1]*accel[1] + accel[2]*accel[2]);
std::array<float, 3> gyro_corr = gyro;
if (norm_a > 1e-6f) {
float ax = accel[0] / norm_a;
float ay = accel[1] / norm_a;
float az = accel[2] / norm_a;
float qw = q_[0];
float qx = q_[1];
float qy = q_[2];
float qz = q_[3];
float vx = 2.0f * (qx * qz - qw * qy);
float vy = 2.0f * (qw * qx + qy * qz);
float vz = qw * qw - qx * qx - qy * qy + qz * qz;
// Error = cross(a, v)
float ex = ay * vz - az * vy;
float ey = az * vx - ax * vz;
float ez = ax * vy - ay * vx;
if (ki_ > 0.0f) {
e_int_[0] += ex * dt;
e_int_[1] += ey * dt;
e_int_[2] += ez * dt;
} else {
e_int_ = {0.0f, 0.0f, 0.0f};
}
gyro_corr[0] += kp_ * ex + ki_ * e_int_[0];
gyro_corr[1] += kp_ * ey + ki_ * e_int_[1];
gyro_corr[2] += kp_ * ez + ki_ * e_int_[2];
}
float qw = q_[0];
float qx = q_[1];
float qy = q_[2];
float qz = q_[3];
float q_dot_w = 0.5f * (-qx * gyro_corr[0] - qy * gyro_corr[1] - qz * gyro_corr[2]);
float q_dot_x = 0.5f * ( qw * gyro_corr[0] + qy * gyro_corr[2] - qz * gyro_corr[1]);
float q_dot_y = 0.5f * ( qw * gyro_corr[1] - qx * gyro_corr[2] + qz * gyro_corr[0]);
float q_dot_z = 0.5f * ( qw * gyro_corr[2] + qx * gyro_corr[1] - qy * gyro_corr[0]);
q_[0] += q_dot_w * dt;
q_[1] += q_dot_x * dt;
q_[2] += q_dot_y * dt;
q_[3] += q_dot_z * dt;
float norm_q = std::sqrt(q_[0]*q_[0] + q_[1]*q_[1] + q_[2]*q_[2] + q_[3]*q_[3]) + 1e-9f;
q_[0] /= norm_q;
q_[1] /= norm_q;
q_[2] /= norm_q;
q_[3] /= norm_q;
return q_;
}
const std::array<float, 4>& get_q() const { return q_; }
private:
float kp_;
float ki_;
std::array<float, 4> q_;
std::array<float, 3> e_int_;
};
} // namespace sim2real_common
@@ -0,0 +1,114 @@
#pragma once
#include <array>
#include <string>
#include <vector>
#include <cmath>
namespace sim2real_common
{
enum class GuardLevel : int {
OK = 0,
WARN = 1,
STOP = 2
};
struct GuardDecision {
GuardLevel level{GuardLevel::OK};
std::string reason;
};
class RuntimeGuard {
public:
RuntimeGuard(
float max_ang_vel = 12.0f,
float max_tilt_z = -0.30f,
float imu_age_warn_ms = 60.0f,
float imu_age_stop_ms = 200.0f)
: max_ang_vel_(max_ang_vel),
max_tilt_z_(max_tilt_z),
imu_age_warn_ms_(imu_age_warn_ms),
imu_age_stop_ms_(imu_age_stop_ms)
{}
GuardDecision check(
const std::array<float, 3>& imu_gyro,
const std::array<float, 3>& projected_gravity,
float imu_age_ms,
bool estop_triggered,
const std::vector<float>& extra_vals = {})
{
GuardDecision decision;
// 1) user E-stop
if (estop_triggered) {
decision.level = GuardLevel::STOP;
decision.reason = "user E-stop";
return decision;
}
// 2) NaN/Inf check
for (float v : imu_gyro) {
if (std::isnan(v) || std::isinf(v)) {
decision.level = GuardLevel::STOP;
decision.reason = "NaN/Inf detected in imu_gyro";
return decision;
}
}
for (float v : projected_gravity) {
if (std::isnan(v) || std::isinf(v)) {
decision.level = GuardLevel::STOP;
decision.reason = "NaN/Inf detected in projected_gravity";
return decision;
}
}
for (float v : extra_vals) {
if (std::isnan(v) || std::isinf(v)) {
decision.level = GuardLevel::STOP;
decision.reason = "NaN/Inf detected in checked values";
return decision;
}
}
// 3) IMU stale
if (imu_age_ms > imu_age_stop_ms_) {
decision.level = GuardLevel::STOP;
decision.reason = "IMU stale " + std::to_string(imu_age_ms) + "ms";
return decision;
}
bool warned_imu = (imu_age_ms > imu_age_warn_ms_);
// 4) Tilt check
if (projected_gravity[2] > max_tilt_z_) {
decision.level = GuardLevel::STOP;
decision.reason = "tilt: g_z=" + std::to_string(projected_gravity[2]);
return decision;
}
// 5) Angular velocity check
float ang_norm = std::sqrt(imu_gyro[0] * imu_gyro[0] + imu_gyro[1] * imu_gyro[1] + imu_gyro[2] * imu_gyro[2]);
if (ang_norm > max_ang_vel_) {
decision.level = GuardLevel::STOP;
decision.reason = "ang_vel overflow: |w|=" + std::to_string(ang_norm);
return decision;
}
if (warned_imu) {
decision.level = GuardLevel::WARN;
decision.reason = "IMU age " + std::to_string(imu_age_ms) + "ms";
return decision;
}
decision.level = GuardLevel::OK;
return decision;
}
private:
float max_ang_vel_;
float max_tilt_z_;
float imu_age_warn_ms_;
float imu_age_stop_ms_;
};
} // namespace sim2real_common
@@ -0,0 +1,119 @@
#pragma once
#include <array>
#include <string>
#include <cmath>
#include <algorithm>
namespace sim2real_common
{
enum class SafetyLevel : int {
NORMAL = 0,
CLIP = 1,
BRAKE = 2,
ESTOP = 3
};
struct SafetyDecision {
SafetyLevel level{SafetyLevel::NORMAL};
std::string message;
std::array<float, 16> clipped_target{};
};
class SafetyMonitor {
public:
SafetyMonitor(
float max_target_offset = 0.6f,
float max_ang_vel = 10.0f,
float max_tilt_z = -0.3f,
int clip_to_brake = 0,
float hard_target_offset = 1.2f)
: max_target_offset_(max_target_offset),
max_ang_vel_(max_ang_vel),
max_tilt_z_(max_tilt_z),
clip_to_brake_(clip_to_brake),
hard_target_offset_(hard_target_offset),
consecutive_clips_(0)
{}
SafetyDecision check(
const std::array<float, 16>& target_pose,
const std::array<float, 16>& default_pose,
const std::array<float, 3>& imu_gyro,
const std::array<float, 3>& projected_gravity,
bool estop_triggered)
{
SafetyDecision decision;
decision.clipped_target = target_pose;
if (estop_triggered) {
decision.level = SafetyLevel::ESTOP;
decision.message = "user E-stop";
return decision;
}
// Tilt check (g_z should be ~ -1.0, if it is > max_tilt_z e.g. -0.3, it is tilted)
if (projected_gravity[2] > max_tilt_z_) {
decision.level = SafetyLevel::BRAKE;
decision.message = "tilt detected: g_z=" + std::to_string(projected_gravity[2]);
return decision;
}
// Angular velocity norm check
float ang_vel_norm = std::sqrt(imu_gyro[0] * imu_gyro[0] + imu_gyro[1] * imu_gyro[1] + imu_gyro[2] * imu_gyro[2]);
if (ang_vel_norm > max_ang_vel_) {
decision.level = SafetyLevel::BRAKE;
decision.message = "angular velocity overflow: |w|=" + std::to_string(ang_vel_norm);
return decision;
}
// Offset check
bool needs_clip = false;
float max_offset = 0.0f;
for (std::size_t i = 0; i < 12; ++i) { // check leg joint offsets from default pose
float offset = target_pose[i] - default_pose[i];
max_offset = std::max(max_offset, std::abs(offset));
if (std::abs(offset) > max_target_offset_) {
needs_clip = true;
float clipped_val = std::clamp(offset, -max_target_offset_, max_target_offset_);
decision.clipped_target[i] = default_pose[i] + clipped_val;
}
}
if (needs_clip) {
consecutive_clips_++;
if (hard_target_offset_ > 0.0f && max_offset > hard_target_offset_) {
decision.level = SafetyLevel::BRAKE;
decision.message = "target leg offset exceeds hard limit: " + std::to_string(max_offset);
return decision;
}
if (clip_to_brake_ > 0 && consecutive_clips_ >= clip_to_brake_) {
decision.level = SafetyLevel::BRAKE;
decision.message = "clipped " + std::to_string(consecutive_clips_) + " frames in a row";
return decision;
}
decision.level = SafetyLevel::CLIP;
decision.message = "target leg offset out of range";
return decision;
}
consecutive_clips_ = 0;
decision.level = SafetyLevel::NORMAL;
return decision;
}
void reset() {
consecutive_clips_ = 0;
}
private:
float max_target_offset_;
float max_ang_vel_;
float max_tilt_z_;
int clip_to_brake_;
float hard_target_offset_;
int consecutive_clips_;
};
} // namespace sim2real_common
@@ -0,0 +1,107 @@
#pragma once
#include <array>
#include <cmath>
#include <algorithm>
#include "sim2real_common/deployment_contract.hpp"
#ifndef M_PI
#define M_PI 3.14159265358979323846
#endif
namespace sim2real_common
{
class StandBalanceController
{
public:
StandBalanceController(double control_dt = 0.02)
: control_dt_(control_dt)
{
reset();
}
void setNominalLegPose(float hip_pitch, float knee)
{
nominal_hip_pitch_ = hip_pitch;
nominal_knee_ = knee;
}
void reset()
{
stable_time_ = 0.0f;
}
std::array<float, 16> computeTarget(
const std::array<float, 3>& projected_gravity,
const std::array<float, 3>& imu_gyro,
const std::array<float, 3>& cmd)
{
const float hip_base = nominal_hip_pitch_;
const float knee_base = nominal_knee_;
float roll = 0.0f;
float pitch = 0.0f;
estimateRollPitch(projected_gravity, roll, pitch);
float roll_rate = imu_gyro[0];
float pitch_rate = imu_gyro[1];
float roll_corr = -kp_roll_ * roll - kd_roll_rate_ * roll_rate;
float lateral_lean = lateral_lean_gain_ * cmd[1];
std::array<float, 16> target{};
for (int leg_idx = 0; leg_idx < 4; ++leg_idx) {
float side = (leg_idx == 0 || leg_idx == 2) ? 1.0f : -1.0f;
target[leg_idx * 3 + 0] = std::clamp(side * roll_corr + lateral_lean, -hip_abduction_clip_, hip_abduction_clip_);
target[leg_idx * 3 + 1] = std::clamp(hip_base, hip_pitch_clip_[0], hip_pitch_clip_[1]);
target[leg_idx * 3 + 2] = std::clamp(knee_base, knee_clip_[0], knee_clip_[1]);
}
// wheels 0
target[12] = target[13] = target[14] = target[15] = 0.0f;
bool stable = (std::abs(roll * 180.0f / static_cast<float>(M_PI)) <= stable_roll_deg_) &&
(std::abs(pitch * 180.0f / static_cast<float>(M_PI)) <= stable_pitch_deg_) &&
(std::max(std::abs(roll_rate * 180.0f / static_cast<float>(M_PI)), std::abs(pitch_rate * 180.0f / static_cast<float>(M_PI))) <= stable_gyro_deg_s_);
stable_time_ = stable ? (stable_time_ + static_cast<float>(control_dt_)) : 0.0f;
return target;
}
bool isStable() const
{
return stable_time_ >= enter_hold_s_;
}
private:
void estimateRollPitch(const std::array<float, 3>& projected_gravity, float& roll, float& pitch)
{
float gx = projected_gravity[0];
float gy = projected_gravity[1];
float gz = projected_gravity[2];
roll = std::atan2(-gy, std::max(1e-6f, -gz));
pitch = std::atan2(gx, std::sqrt(std::max(1e-6f, gy * gy + gz * gz)));
}
double control_dt_;
float nominal_hip_pitch_{DeploymentContract::kDefaultDofPos[1]};
float nominal_knee_{DeploymentContract::kDefaultDofPos[2]};
float kp_roll_{0.85f};
float kd_roll_rate_{0.03f};
float lateral_lean_gain_{0.0f};
float hip_abduction_clip_{0.45f};
std::array<float, 2> hip_pitch_clip_{-1.0f, 2.5f};
std::array<float, 2> knee_clip_{-2.6f, -0.3f};
float stable_roll_deg_{6.0f};
float stable_pitch_deg_{8.0f};
float stable_gyro_deg_s_{45.0f};
float enter_hold_s_{1.0f};
float stable_time_{0.0f};
};
} // namespace sim2real_common
@@ -0,0 +1,10 @@
<?xml version="1.0"?>
<package format="3">
<name>sim2real_common</name>
<version>0.1.0</version>
<description>Shared constants and deployment contract helpers for sim2real_ros2.</description>
<maintainer email="todo@example.com">todo</maintainer>
<license>Proprietary</license>
<buildtool_depend>ament_cmake</buildtool_depend>
</package>
@@ -0,0 +1,37 @@
cmake_minimum_required(VERSION 3.8)
project(sim2real_hw)
find_package(ament_cmake REQUIRED)
find_package(rclcpp REQUIRED)
find_package(sensor_msgs REQUIRED)
find_package(std_msgs REQUIRED)
find_package(nav_msgs REQUIRED)
find_package(sim2real_common REQUIRED)
find_package(sim2real_interfaces REQUIRED)
add_executable(sim2real_hw_node
src/hardware_bridge_node.cpp
)
target_include_directories(sim2real_hw_node PRIVATE include)
target_compile_features(sim2real_hw_node PRIVATE cxx_std_17)
ament_target_dependencies(sim2real_hw_node
rclcpp
sensor_msgs
std_msgs
nav_msgs
sim2real_common
sim2real_interfaces
)
install(
DIRECTORY include/
DESTINATION include
)
install(
TARGETS sim2real_hw_node
DESTINATION lib/${PROJECT_NAME}
)
ament_package()
@@ -0,0 +1,318 @@
#pragma once
#include <array>
#include <mutex>
#include <memory>
#include <string>
#include <vector>
#include <atomic>
#include <chrono>
#include "rclcpp/rclcpp.hpp"
#include "sensor_msgs/msg/imu.hpp"
#include "nav_msgs/msg/odometry.hpp"
#include "std_msgs/msg/bool.hpp"
#include "std_msgs/msg/string.hpp"
#include "sim2real_interfaces/msg/runtime_state.hpp"
#include "sim2real_interfaces/msg/runtime_target.hpp"
#include "sim2real_common/event_logger.hpp"
#include "sim2real_common/low_pass_filter.hpp"
#include "sim2real_common/mahony_filter.hpp"
#include "sim2real_common/safety_monitor.hpp"
#include "sim2real_common/runtime_guard.hpp"
struct can_frame;
namespace sim2real_hw
{
enum class RecoveryKind
{
None,
Stale,
NoEffect
};
enum class RecoveryStage
{
Idle,
AwaitInitFeedback,
AwaitEffectVerification
};
enum class ActiveModelMode
{
Rough,
Crawl
};
struct MotorConfig
{
int bus; // 1 or 2
int id; // motor CAN id
float direction;
float offset;
};
struct MotorStateInternal
{
float position{0.0f};
float velocity{0.0f};
float torque{0.0f};
float temperature{0.0f};
float bus_voltage{0.0f};
float estimated_current_arms{0.0f};
float last_command_sim{0.0f};
std::uint32_t update_count{0};
std::uint32_t stale_count{0};
std::uint32_t command_active_count{0};
std::uint32_t no_effect_count{0};
std::uint16_t fault_code{0};
std::uint16_t fault_detail_1{0};
std::uint16_t fault_detail_2{0};
// Hold-over state
float last_valid_pos{0.0f};
float last_valid_vel{0.0f};
float last_valid_torque{0.0f};
std::uint32_t prev_update_count{0};
bool has_valid_data{false};
bool has_bus_voltage{false};
bool has_fault_snapshot{false};
bool stale_reported{false};
bool recovered_reported{false};
bool disable_reported{false};
bool command_effect_monitoring_active{false};
bool no_effect_reported{false};
bool high_temp_reported{false};
bool high_current_reported{false};
bool high_voltage_reported{false};
bool low_voltage_reported{false};
bool fault_code_reported{false};
bool init_confirmed{false};
std::uint32_t init_attempt_count{0};
std::uint32_t recovery_attempt_count{0};
std::uint32_t no_effect_recovery_attempt_count{0};
std::chrono::steady_clock::time_point last_recovery_attempt_time_{};
std::chrono::steady_clock::time_point last_no_effect_recovery_attempt_time_{};
std::chrono::steady_clock::time_point last_diag_snapshot_time_{};
std::chrono::steady_clock::time_point last_diag_request_time_{};
std::chrono::steady_clock::time_point recovery_stage_deadline_{};
std::uint32_t recovery_start_update_count{0};
std::uint32_t recovery_active_attempt_number{0};
RecoveryKind recovery_kind{RecoveryKind::None};
RecoveryStage recovery_stage{RecoveryStage::Idle};
std::string recovery_trigger;
std::string last_power_event_reason;
};
class HardwareBridgeNode : public rclcpp::Node
{
public:
HardwareBridgeNode();
~HardwareBridgeNode();
private:
void onTarget(const sim2real_interfaces::msg::RuntimeTarget::SharedPtr msg);
void onModelStatus(const std_msgs::msg::String::SharedPtr msg);
void onReadLoop();
void onWriteLoop();
void onImu(const sensor_msgs::msg::Imu::SharedPtr msg);
void onOdom(const nav_msgs::msg::Odometry::SharedPtr msg);
bool initCan(const std::string& ifname, int& fd);
bool sendCanFrame(int fd, std::uint32_t can_id, const std::uint8_t* data, std::uint8_t dlc);
bool readCanFrame(int fd, void* frame, int timeout_us);
bool enableMotor(int fd, int motor_id);
bool disableMotor(int fd, int motor_id, bool clear_fault = false);
bool writeParameterInt(int fd, int motor_id, std::uint16_t param_id, std::uint32_t value);
bool setModeRaw(int fd, int motor_id, std::int8_t mode);
bool readParameter(int fd, int motor_id, std::uint16_t param_id);
bool writeLimit(int fd, int motor_id, std::uint16_t param_id, float limit);
bool writeOperationFrame(int fd, int motor_id, double pos, double vel, double kp, double kd, double torque);
bool initializeMotor(std::size_t index, const std::string & reason, int max_attempts = 3);
bool initializeMotorsOnBus(int bus_id, const std::string & reason);
bool waitForMotorFeedback(std::size_t index, std::chrono::milliseconds timeout);
void processCanFrame(const struct can_frame & frame, int bus_id);
void drainCanFrames(int fd, int bus_id, int timeout_us);
bool isLegMotor(std::size_t index) const;
bool isWheelMotor(std::size_t index) const;
bool motorHasBlockingFault(std::size_t index) const;
bool isNoEffectConditionPresent(std::size_t index) const;
std::uint32_t noEffectCommandWarmupCycles(std::size_t index) const;
std::uint32_t noEffectTriggerCycles(std::size_t index) const;
std::uint32_t noEffectAttemptLimit(std::size_t index) const;
std::uint32_t noEffectCooldownMs(std::size_t index) const;
std::uint32_t noEffectVerifyTimeoutMs(std::size_t index) const;
bool hasFreshNoEffectDiagnostics(std::size_t index) const;
void requestMotorDiagnostics(std::size_t index);
std::string classifyNoEffectSuspect(std::size_t index) const;
std::string buildNoEffectSummary(std::size_t index) const;
void updateMotorCommandTracking(std::size_t index, float sim_command, const std::string & target_source);
void updateNoEffectDetection(std::size_t index);
bool startMotorRecoverySequence(
std::size_t index,
const std::string & trigger,
RecoveryKind kind,
std::uint32_t attempt_number);
void processMotorRecoverySequence(std::size_t index);
void clearMotorRecoverySequence(std::size_t index);
bool shouldAttemptMotorRecovery(std::size_t index) const;
bool attemptMotorRecovery(std::size_t index, const std::string & trigger);
bool shouldAttemptNoEffectRecovery(std::size_t index) const;
bool attemptNoEffectRecovery(std::size_t index, const std::string & trigger);
const char * jointName(std::size_t index) const;
std::string motorTag(std::size_t index) const;
float estimateCurrentArms(float torque_nm) const;
std::string decodeFaultCode(std::uint16_t fault_code) const;
std::string decodeFaultDetailRegister(std::uint16_t register_value, int register_index) const;
std::string buildMotorFaultSummary(std::size_t index) const;
std::string formatProtectionReason(const std::string & trigger, const std::string & reason) const;
void logProtectionEvent(const std::string & trigger, const std::string & reason, const std::string & action);
void logMotorPowerEvent(std::size_t index, const std::string & state, const std::string & reason);
void logMotorDiagnosticEvent(std::size_t index, const std::string & event, const std::string & reason, const char * level = "WARN");
void updateMotorTelemetry(std::size_t index, float pos_sim, float vel_sim, float torque_sim, float temperature_c);
void handleParameterResponse(const struct can_frame & frame, int bus_id);
void updateMotorDiagnostics(std::size_t index);
void pollMotorDiagnostics();
void finalizeRunSummary();
rclcpp::Publisher<sim2real_interfaces::msg::RuntimeState>::SharedPtr state_pub_;
rclcpp::Subscription<sim2real_interfaces::msg::RuntimeTarget>::SharedPtr target_sub_;
rclcpp::Subscription<std_msgs::msg::String>::SharedPtr model_status_sub_;
rclcpp::Subscription<sensor_msgs::msg::Imu>::SharedPtr imu_sub_;
rclcpp::Subscription<std_msgs::msg::Bool>::SharedPtr estop_sub_;
rclcpp::Subscription<nav_msgs::msg::Odometry>::SharedPtr odom_sub_;
rclcpp::TimerBase::SharedPtr read_timer_;
rclcpp::TimerBase::SharedPtr write_timer_;
std::mutex target_mutex_;
std::array<float, 16> latest_target_{};
std::array<float, 16> latest_raw_action_{};
std::string latest_target_source_{"boot_hold"};
rclcpp::Time latest_target_stamp_{0, 0, RCL_ROS_TIME};
std::array<float, 16> rough_default_dof_pos_{};
std::array<float, 16> crawl_default_dof_pos_{};
std::array<float, 16> active_default_dof_pos_{};
ActiveModelMode active_model_mode_{ActiveModelMode::Rough};
bool model_switch_active_{false};
std::uint32_t target_sequence_{0};
std::uint32_t state_sequence_{0};
double target_timeout_ms_{150.0};
// SocketCAN file descriptors
int can0_fd_{-1};
int can1_fd_{-1};
std::string can0_name_{"can0"};
std::string can1_name_{"can1"};
// CAN error recovery
static constexpr int kCanErrorThreshold = 50; // consecutive errors before reinit
int can0_error_count_{0};
int can1_error_count_{0};
bool reinitCan(const std::string& ifname, int& fd, int& error_count);
// Hold-over constants
static constexpr std::uint32_t kHoldoverThreshold = 2;
static constexpr std::uint32_t kMotorDropReportThreshold = 40;
// Motor configurations and states
std::array<MotorConfig, 16> motors_;
std::array<MotorStateInternal, 16> motor_states_;
// IMU state
std::mutex imu_mutex_;
std::array<float, 3> imu_gyro_{};
std::array<float, 3> imu_accel_{};
std::array<float, 3> projected_gravity_{0.0f, 0.0f, -1.0f};
bool imu_fresh_{false};
rclcpp::Time last_imu_stamp_{0, 0, RCL_ROS_TIME};
std::chrono::steady_clock::time_point last_imu_recv_time_{};
bool has_received_imu_{false};
std::array<float, 3> imu_gravity_sum_{0.0f, 0.0f, 0.0f};
std::uint32_t imu_gravity_sample_count_{0};
static constexpr std::uint32_t kImuGravityAlignSamples = 50;
// Odom state
std::mutex odom_mutex_;
rclcpp::Time last_odom_stamp_{0, 0, RCL_ROS_TIME};
std::array<float, 3> odom_pos_{};
std::array<float, 4> odom_quat_wxyz_{1.0f, 0.0f, 0.0f, 0.0f};
std::array<float, 3> odom_linear_vel_{};
std::array<float, 3> odom_angular_vel_{};
bool odom_fresh_{false};
// Filters and Estimators
std::unique_ptr<sim2real_common::LowPassFilter> lpf_legs_;
std::unique_ptr<sim2real_common::LowPassFilter> lpf_wheels_;
std::unique_ptr<sim2real_common::MahonyFilter> mahony_filter_;
std::unique_ptr<sim2real_common::SafetyMonitor> safety_monitor_;
std::unique_ptr<sim2real_common::SafetyMonitor> model_switch_safety_monitor_;
std::unique_ptr<sim2real_common::RuntimeGuard> runtime_guard_;
bool mahony_initialized_{false};
rclcpp::Time last_read_time_{0, 0, RCL_ROS_TIME};
rclcpp::Time startup_soft_hold_start_time_{0, 0, RCL_ROS_TIME};
rclcpp::Time last_diag_poll_time_{0, 0, RCL_ROS_TIME};
std::size_t diag_poll_motor_index_{0};
// Telemetry
std::uint32_t fresh_count_{0};
std::uint32_t holdover_count_{0};
std::uint32_t stale_max_{0};
std::uint32_t holdover_events_total_{0};
std::uint32_t protection_trigger_count_{0};
std::uint32_t motor_drop_event_count_{0};
std::uint32_t motor_recover_event_count_{0};
std::uint32_t motor_fault_event_count_{0};
bool timeout_hold_logged_{false};
bool clip_active_logged_{false};
bool dry_run_{false};
std::atomic<bool> estop_triggered_{false};
std::atomic<bool> safety_enabled_{true};
std::atomic<bool> safety_triggered_{false};
std::string safety_reason_{""};
sim2real_common::EventLogger event_logger_;
std::string run_log_dir_;
float motor_temp_warn_c_{100.0f};
float motor_temp_fault_c_{135.0f};
float motor_bus_overvoltage_v_{60.0f};
float motor_bus_undervoltage_v_{12.0f};
float motor_current_warn_arms_{10.5f};
float motor_current_peak_arms_{14.0f};
float motor_torque_warn_nm_{13.0f};
float motor_torque_peak_nm_{17.0f};
double diag_poll_period_s_{0.10};
float wheel_no_effect_command_threshold_{1.0f};
float wheel_no_effect_min_response_ratio_{0.20f};
float wheel_no_effect_velocity_epsilon_{0.25f};
float wheel_no_effect_max_temperature_c_{90.0f};
float wheel_no_effect_min_bus_voltage_v_{18.0f};
std::uint32_t wheel_no_effect_command_warmup_cycles_{12};
std::uint32_t wheel_no_effect_trigger_cycles_{30};
std::uint32_t wheel_no_effect_attempt_limit_{2};
std::uint32_t wheel_no_effect_cooldown_ms_{1200};
std::uint32_t wheel_recovery_verify_timeout_ms_{180};
std::uint32_t wheel_no_effect_diag_freshness_ms_{350};
std::uint32_t wheel_no_effect_diag_request_period_ms_{80};
float leg_no_effect_position_error_threshold_{0.18f};
float leg_no_effect_velocity_epsilon_{0.12f};
float leg_no_effect_max_estimated_current_arms_{4.0f};
float leg_no_effect_max_abs_torque_nm_{5.0f};
float leg_no_effect_max_temperature_c_{100.0f};
float leg_no_effect_min_bus_voltage_v_{18.0f};
std::uint32_t leg_no_effect_command_warmup_cycles_{40};
std::uint32_t leg_no_effect_trigger_cycles_{25};
std::uint32_t leg_no_effect_attempt_limit_{2};
std::uint32_t leg_no_effect_cooldown_ms_{1200};
std::uint32_t leg_recovery_verify_timeout_ms_{220};
void onEstop(const std_msgs::msg::Bool::SharedPtr msg);
void logEvent(
const std::string & level,
const std::string & event,
const std::string & message);
};
} // namespace sim2real_hw
@@ -0,0 +1,17 @@
<?xml version="1.0"?>
<package format="3">
<name>sim2real_hw</name>
<version>0.1.0</version>
<description>Hardware bridge and safety boundary for sim2real_ros2.</description>
<maintainer email="todo@example.com">todo</maintainer>
<license>Proprietary</license>
<buildtool_depend>ament_cmake</buildtool_depend>
<depend>rclcpp</depend>
<depend>sensor_msgs</depend>
<depend>std_msgs</depend>
<depend>nav_msgs</depend>
<depend>sim2real_common</depend>
<depend>sim2real_interfaces</depend>
</package>

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