Patchwork

Official page of "Patchwork: Concentric Zone-based Region-wise Ground Segmentation with Ground Likelihood Estimation Using a 3D LiDAR Sensor", which is accepted by RA-L with IROS'21 option

[Video] [Preprint Paper] [Project Wiki]

Patchwork	Concept of our method (CZM & GLE)

It's an overall updated version of R-GPF of ERASOR [Code] [Paper].

NEWS (21.12.27)

• pub\_for\_legoloam node for the pointcloud in kitti bagfile is added.

  • ground_estimate.msg is added

• Bug in xy2theta function is fixed.

• How to run

roslaunch patchwork pub_for_legoloam.launch
rosbag play {YOUR_FILE_PATH}/KITTI_BAG/kitti_sequence_00.bag --clock /kitti/velo/pointcloud:=/velodyne_points

• This README will be updated soon!

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Demo

KITTI 00

Rough Terrain

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Characteristics

• Single hpp file (include/patchwork/patchwork.hpp)

• Robust ground consistency

As shown in the demo videos and below figure, our method shows the most promising robust performance compared with other state-of-the-art methods, especially, our method focuses on the little perturbation of precision/recall as shown in this figure.

Please kindly note that the concept of traversable area and ground is quite different! Please refer to our paper.

Contents

0. Test Env.
1. Requirements
2. How to Run Patchwork
3. Citation

Test Env.

The code is tested successfully at

• Linux 18.04 LTS
• ROS Melodic

Requirements

ROS Setting

• 1. Install ROS on a machine.
• 2. Thereafter, jsk-visualization is required to visualize Ground Likelihood Estimation status.

sudo apt-get install ros-melodic-jsk-recognition
sudo apt-get install ros-melodic-jsk-common-msgs
sudo apt-get install ros-melodic-jsk-rviz-plugins

• 3. Compile compile this package. We use catkin tools,

mkdir -p ~/catkin_ws/src
cd ~/catkin_ws/src
git clone https://github.com/LimHyungTae/patchwork.git
cd .. && catkin build patchwork 

How to Run Patchwork

We provide three examples

• Offline KITTI dataset
• Online (ROS Callback) KITTI dataset
• Own dataset using pcd files

Offline KITTI dataset

1. Download SemanticKITTI Odometry dataset (We also need labels since we also open the evaluation code! :)

2. Set the data_path in launch/offline_kitti.launch for your machine.

The data_path consists of velodyne folder and labels folder as follows:

data_path (e.g. 00, 01, ..., or 10)
_____velodyne
     |___000000.bin
     |___000001.bin
     |___000002.bin
     |...
_____labels
     |___000000.label
     |___000001.label
     |___000002.label
     |...
_____...
   

3. Run launch file

roslaunch patchwork offline_kitti.launch

You can directly feel the speed of Patchwork! 😉

Online (ROS Callback) KITTI dataset

We also provide rosbag example. If you run our patchwork via rosbag, please refer to this example.

1. Download readymade rosbag

wget https://urserver.kaist.ac.kr/publicdata/patchwork/kitti_00_xyzilid.bag

2. After building this package, run the roslaunch as follows:

roslaunch patchwork rosbag_kitti.launch

3. Then play the rosbag file in another command

rosbag play kitti_00_xyzilid.bag

Own dataset using pcd files

Please refer to /nodes/offilne_own_data.cpp.

(Note that in your own data format, there may not exist ground truth labels!)

Be sure to set right params. Otherwise, your results may be wrong as follows:

W/ wrong params	After setting right params

For better understanding of the parameters of Patchwork, please read our wiki, 4. IMPORTANT: Setting Parameters of Patchwork in Your Own Env..

Offline (Using *.pcd or *.bin file)

1. Utilize /nodes/offilne_own_data.cpp

2. Please check the output by following command and corresponding files:

roslaunch patchwork offline_ouster128.launch

Online (via rosbag)

1. Utilize rosbag_kitti.launch.

2. To do so, remap the topic of subscriber, e.g. add remap line as follows:

<remap from="/node" to="$YOUR_LIDAR_TOPIC_NAME$"/>

3. In addition, minor modification of ros_kitti.cpp is necessary by refering to offline_own_data.cpp.

Citation

If you use our code or method in your work, please consider citing the following:

@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}
}

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Description

All explanations of parameters and other experimental results will be uploaded in wiki

Contact

If you have any questions, please let me know:

• Hyungtae Lim {[email protected]}

TODO List

• Add ROS support
• Add preprint paper
• Add demo videos
• Add own dataset examples
• Update wiki

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