This is ROS package of Patchwork++ (@ IROS'22), which is a fast and robust ground segmentation method.

If you are not familiar with ROS, please visit the original repository.

If you follow the repository, you can run Patchwork++ in Python and C++ easily.

• ROS based Patchwork source code (patchworkpp.hpp)
• Demo launch file (demo.launch) with sample rosbag file. You can execute Patchwork++ simply!

You may need to install ROS, PCL, Eigen, ...

To build Patchwork++, you can follow below codes.

$ mkdir -p ~/catkin_ws/src
$ cd ~/catkin_ws
$ catkin build # or catkin_make

There is a demo which executes Patchwork++ with sample rosbag file. You can download a sample file with the following command.

For the sample rosbag data, I utilizes semantickitti2bag package.

$ wget https://urserver.kaist.ac.kr/publicdata/patchwork++/kitti_00_sample.bag

If you have any trouble to download the file by the above command, please click here to download the file directly.

The rosbag file is based on the KITTI dataset. The bin files are merged into the rosbag file format.

The sample file contains LiDAR sensor data only.

Then, you can run demo as follows.

# Start Patchwork++
$ roslaunch patchworkpp demo.launch
# Start the bag file
$ rosbag play kitti_00_sample.bag

• Update additional demo codes processing data with .bin file format
• Generalize point type in the source code
• Add visualization result of demo codes in readme

If you use our codes, please cite our paper.

In addition, you can also check the paper of our baseline(Patchwork) here.

@inproceedings{lee2022patchworkpp,
    title={{Patchwork++: Fast and robust ground segmentation solving partial under-segmentation using 3D point cloud}},
    author={Lee, Seungjae and Lim, Hyungtae and Myung, Hyun},
    booktitle={Proc. IEEE/RSJ Int. Conf. Intell. Robots Syst.},
    year={2022},
    note={{Submitted}} 
}

@article{lim2021patchwork,
    title={Patchwork: Concentric Zone-based Region-wise Ground Segmentation with Ground Likelihood Estimation Using a 3D LiDAR Sensor},
    author={Lim, Hyungtae and Minho, Oh and Myung, Hyun},
    journal={IEEE Robotics and Automation Letters},
    year={2021}
}

If you have any question, don't be hesitate let us know!

• Seungjae Lee ✉️ (sj98lee at kaist.ac.kr)
• Hyungtae Lim ✉️ (shapelim at kaist.ac.kr)