An ROS implementation of paper "Curved-Voxel Clustering for Accurate Segmentation of 3D LiDAR Point Clouds with Real-Time Performance"
- New Spatial Primitive. curved-voxel, a LiDAR-optimized spatial unit reflecting distinct characteristics of 3D LiDAR point clouds.
- Algorithm. an efficient method for segmenting 3D LiDAR point clouds by utilizing LiDAR-optimized curved-voxels and efficient hashbased data structure.
- ROS dynamic reconfigure, you can tune the parameters easily.
- Curved-Voxel Clustering for Accurate Segmentation of 3D LiDAR Point Clouds with Real-Time Performance. (IROS) 2019
- https://github.com/wangx1996/Lidar-Segementation
- https://github.com/SS47816/lidar_obstacle_detector
TODOs
- imporove the efficiency of algorithm
- imporove the segmentation accuracy
Known Issues
- the segementation result is not very ideal.
- ground cloud filter: https://github.com/HMX2013/patchwork-VLP16
- sudo apt-get install ros-melodic-jsk-rviz-plugins
# clone the repo
mkdir -p catkin_ws/src
cd catkin_ws/src
git clone https://github.com/HMX2013/SemanticKITTI_loader
git clone https://github.com/HMX2013/CVC-ROS
download obsdet_msgs from
"https://drive.google.com/file/d/1ztLk9Slm656CV-WJieUpBJPlz-Iw14Bk/view?usp=share_link"
cd ../
catkin_make
roslaunch semantic_kitti run_semantic.launch
roslaunch cvc_ros run_rviz.launch
You are welcome contributing to the package by opening a pull-request
We are following: Google C++ Style Guide, C++ Core Guidelines, and ROS C++ Style Guide
MIT License