YOPO/Simulator/src/readme.md

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# 真实环境点云/深度图仿真(支持CUDA)
### 1 依赖
CUDA; ROS; OpenCV; PCL; (如果已经安装ROS依赖基本都满足) yaml-cpp
```angular2html
sudo apt-get install libyaml-cpp-dev
```
### 2 编译
```angular2html
catkin build
```
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在CmakeList中采用`cuda_select_nvcc_arch_flags`自动检测如果遇到编译错误你需要手动设置CUDA架构请将CmakeList 25行起
```
cuda_select_nvcc_arch_flags(ARCH_FLAGS)
...
endif()
```
替换为(5060 GPU是120, 需要根据自己设备设置)
```
set(ARCH_FLAGS "-gencode arch=compute_120,code=sm_120")
```
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### 3 运行
```angular2html
source devel/setup.bash
# GPU版本 (推荐, RTX 3060 深度输出 > 1000fps)
rosrun sensor_simulator sensor_simulator_cuda
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# CPU版本 (资源占用高仅供GPU编译失败无法解决时测试)
rosrun sensor_simulator sensor_simulator
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```
传感器参数以及点云环境修改见[config](config/config.yaml),重要参数说明:
```
# 一些话题
odom_topic: "/sim/odom"
depth_topic: "/depth_image"
lidar_topic: "/lidar_points"
# 使用预先构建的点云地图还是随机地图
random_map: true
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# 随机地图配置
maze_type: 5 # 1: 溶洞 2: 柱子 3:迷宫 5:森林 6:房间
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```
### 4 仿真位置发布与简单可视化(可选)
```angular2html
cd src/sensor_simulator
python sim_odom.py
cd src/sensor_simulator
rviz -d rviz.rviz
```
### 5 实时性与资源占用
cpu 版本 (i7-9700)
深度图0.02s, 点云0.01s
gpu 版本 (RTX 3060)
深度图0.001s, 点云0.001s
GPU版资源占用(开30HZ)
![Demo GIF](img/resource.png)
### 6 示例场景
<table>
<tr>
<td align="center">
<img src="img/forest.png" alt="Image 1" style="width:100%;"/>
<p>1. realworld forest</p>
</td>
<td align="center">
<img src="img/building.png" alt="Image 2" style="width:82%;"/>
<p>2. realworld building</p>
</td>
</tr>
<tr>
<td align="center">
<img src="img/perlin3D.png" alt="Image 3" style="width:100%;"/>
<p>3. 3D perlin</p>
</td>
<td align="center">
<img src="img/random_forest.png" alt="Image 4"style="width:100%;"/>
<p>4. random forest</p>
</td>
</tr>
<tr>
<td align="center">
<img src="img/random_room.png" alt="Image 5" style="width:100%;"/>
<p>5. random room</p>
</td>
<td align="center">
<img src="img/random_maze.png" alt="Image 6" style="width:100%;"/>
<p>6. random maze</p>
</td>
</tr>
</table>
**注释:**
1. GPU版本地图无边界可无限延伸; CPU版本地图有边界可选择复制地图几份已弃用
### acknowledgment
Some maps (3D Perlin, random maze) are generated based on: https://github.com/HKUST-Aerial-Robotics/mockamap, thanks for their excellent work!