Created by Alex Su 08/05/2020
This is a point cloud clustering segmentation algorithm, including the removal of ground point clouds and the segmentation of point clouds.
You can use the following commands to download and compile the package.
cd ~/catkin_ws/src
https://github.com/supengufo/depth_clustre_ros
cd ..
caktin_make
run the launch file:
cd ~/catkin_ws
source devel/setup.bash(or setup.zsh)
roslaunch depth_cluster_ros depth_cluster_ros.launch
First, convert all the 3D points coordinates into 2D depth image;
Then,use Breadth-First-Search(BFS) to extraction point cloud clusters. The algorithm flow chart is as follows:
The main idea of point cloud segmentation is based on depth_cluster, in which the filtering threshold condition and neighborhood search are modified;
The segmentation of ground point clouds is based on Zermas' paper, although principal component analysis is already a very common method to extract ground.
Thank them for their work!
All these two papers can be found in the doc folder.
@Article{bogoslavskyi17pfg,
title = {Efficient Online Segmentation for Sparse 3D Laser Scans},
author = {I. Bogoslavskyi and C. Stachniss},
journal = {PFG -- Journal of Photogrammetry, Remote Sensing and Geoinformation Science},
year = {2017},
pages = {1--12},
url = {https://link.springer.com/article/10.1007%2Fs41064-016-0003-y},
}
@inproceedings{Zermas2017Fast,
title={Fast segmentation of 3D point clouds: A paradigm on LiDAR data for autonomous vehicle applications},
author={Zermas, Dimitris and Izzat, Izzat and Papanikolopoulos, Nikolaos},
booktitle={IEEE International Conference on Robotics and Automation},
year={2017},
}
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