基于激光雷达的自动驾驶汽车城市道路和人行道实时检测_C++_代码_相关文件_下载

# `urban_road_filter`: a real-time LIDAR-based urban road and sidewalk detection algorithm for autonomous vehicles <img src="img/urban_road_filter_anim01.gif" height=620/> <img src="img/urban_road_filter_static01.png" height=620/> # Dependency - [ROS](http://wiki.ros.org/ROS/Installation) (tested with Kinetic and Melodic) - [PCL](https://pointclouds.org/) # Install Use the following commands to download and compile the package. ``` cd ~/catkin_ws/src git clone https://github.com/jkk-research/urban_road_filter catkin build urban_road_filter ``` # Getting started Issue the following commands to start roscore, download and play sample data, and start the algorithm with visualization. You can also watch this as a [youtube tutorial](https://www.youtube.com/watch?v=HHnj4VcbSy4). In a **new terminal** start roscore: ``` roscore ``` In a **new terminal** go to your bag folder (e.g. `~/Downloads`): ``` cd ~/Downloads ``` Download a sample rosbag (~3,3 GB): ```r wget https://laesze-my.sharepoint.com/:u:/g/personal/herno_o365_sze_hu/EYl_ahy5pgBBhNHt5ZkiBikBoy_j_x95E96rDtTsxueB_A?download=1 -O leaf-2021-04-23-campus.bag ``` Play rosbag: ```r rosbag play -l ~/Downloads/leaf-2021-04-23-campus.bag ``` In a **new terminal** start the `urban_road_filter` node, `rviz` and `rqt_reconfigure` with roslaunch: ``` roslaunch urban_road_filter demo1.launch ``` # Cite & paper If you use any of this code please consider citing the [paper](https://www.mdpi.com/1424-8220/22/1/194): ```bibtex @Article{roadfilt2022horv, title = {Real-Time LIDAR-Based Urban Road and Sidewalk Detection for Autonomous Vehicles}, author = {Horváth, Ernő and Pozna, Claudiu and Unger, Miklós}, journal = {Sensors}, volume = {22}, year = {2022}, number = {1}, url = {https://www.mdpi.com/1424-8220/22/1/194}, issn = {1424-8220}, doi = {10.3390/s22010194} } ``` # Related solutions - [`points_preprocessor`](https://github.com/Autoware-AI/core_perception/tree/master/points_preprocessor) `ray_ground_filter` and `ring_ground_filter` (ROS) - [`linefit_ground_segmentation`](https://github.com/lorenwel/linefit_ground_segmentation) (ROS) - [`curb_detection`](https://github.com/linyliny/curb_detection) (ROS) - [`3DLidar_curb_detection`](https://github.com/SohaibAl-emara/3D_Lidar_Curb_Detection) (ROS) - Many more algorithms without code mentioned in the [paper](https://doi.org/10.3390/s22010194). # Videos and images [<img src="img/yt_demo01.png" width=213/>](https://www.youtube.com/watch?v=T2qi4pldR-E) [<img src="img/yt_tutorial01.png" width=213/>](https://www.youtube.com/watch?v=HHnj4VcbSy4) [<img src="img/yt_demo02.png" width=213/>](https://www.youtube.com/watch?v=9tdzo2AyaHM) [<img src="img/yt_demo03.png" width=213/>](https://www.youtube.com/watch?v=lp6q_QvWA-Y) <img src="img/marker_poly01.png" width=440/> <img src="img/marker_road_high01.png" width=440/> <img src="img/marker_poly02.png" width=440/> # ROS publications / subscriptions ```mermaid flowchart LR P[points] -->|sensor_msgs/PointCloud2| U(urban_road_filt) U --> |sensor_msgs/PointCloud2| A[curb] U --> |sensor_msgs/PointCloud2| B[road] U --> |sensor_msgs/PointCloud2| C[road_probably] U --> |sensor_msgs/PointCloud2| D[roi] U --> |visualization_msgs/MarkerArray| E[road_marker] ```