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]
IMPORTANT: (Aug. 18th, 2024) I employ TBB, so its FPS is increased from 50 Hz to 100 Hz! If you want to use the paper version of Patchwork for SOTA comparison purpose, Please use this ground seg. benchmark code.
| Patchwork | Concept of our method (CZM & GLE) |
|---|---|
 |
 |
It's an overall updated version of R-GPF of ERASOR [Code] [Paper].
-
Single hpp file (
include/patchwork/patchwork.hpp) -
Robust ground consistency
As shown in the demo videos, 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.
The code is tested successfully at
- Linux 18.04 LTS
- ROS Melodic
-
- Install ROS on a machine.
-
- Thereafter, jsk-visualization is required to visualize Ground Likelihood Estimation status.
(if you use ubuntu 20.04)
sudo apt-get install ros-noetic-jsk-recognition
sudo apt-get install ros-noetic-jsk-common-msgs
sudo apt-get install ros-noetic-jsk-rviz-plugins
(if you use ubuntu 18.04)
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-
- 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 We provide four examples:
-
How to run Patchwork in SemanticKITTI dataset
- Offline KITTI dataset
- Online (ROS Callback) KITTI dataset
-
How to run Patchwork in your own dataset
- Offline by loading pcd files
- Online (ROS Callback) using your ROS bag file
-
Download SemanticKITTI Odometry dataset (We also need labels since we also open the evaluation code! :)
-
Set the
data_pathinlaunch/offline_kitti.launchfor 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
|...
_____...
- Run launch file
roslaunch patchwork offline_kitti.launch
You can directly feel the speed of Patchwork! 😉
We also provide rosbag example. If you run our patchwork via rosbag, please refer to this example.
- After building this package, run the roslaunch as follows:
roslaunch patchwork run_patchwork.launch is_kitti:=true
Then you can see the below message:
-
Set the
data_pathinlaunch/kitti_publisher.launchfor your machine, which is same with the aforementioned parameter in "Offline KITTI dataset" part. -
Then, run ros player (please refer to
nodes/ros_kitti_publisher.cpp) by following command at another terminal window:
roslaunch patchwork kitti_publisher.launch
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..
-
Utilize
/nodes/offilne_own_data.cpp -
Please check the output by following command and corresponding files:
-
Set appropriate absolute file directory, i.e.
file_dir, inoffline_ouster128.launch
roslaunch patchwork offline_ouster128.launch
It is easy by re-using run_patchwork.launch.
- Remap the topic of subscriber, i.g. modify remap line as follows:
<remap from="/patchwork/cloud" to="$YOUR_LIDAR_TOPIC_NAME$"/>
Note that the type subscribed data is sensor_msgs::PointCloud2.
- Next, launch the roslaunch file as follows:
roslaunch patchwork run_patchwork.launch is_kitti:=false
Note that is_kitti=false is important! Because it decides which rviz is opened. The rviz shows only estimated ground and non-ground because your own dataset may have no point-wise labels.
- Then play your bag file!
rosbag play $YOUR_BAG_FILE_NAME$.bag
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}
}
All explanations of parameters and other experimental results will be uploaded in wiki
If you have any questions, please let me know:
- Hyungtae Lim (
shapelim at kaist dot ac dot kr)
- Merry christmas eve XD!
include/label_generatoris added to make the.labelfile, following the SemanticKITTI format. - The
.labelfiles can be directly used in 3DUIS benchmark - Thank Lucas Nunes and Xieyuanli Chen for providing code snippets to save a
.labelfile.
- Pybinding + more advanced version is now available on Patchwork++ as a preprocessing step for deep learning users (i.e., python users can also use our robust ground segmentation)!
- For increasing convenience of use, the examples and codes are extensively revised by reflecting issue #12.
- The meaning of
elevation_thresholdsis changed to increase the usability. The meaning is explained in wiki. - A novel height estimator, called All-Terrain Automatic heighT estimator (ATAT) is added within the patchwork code, which auto-calibrates the sensor height using the ground points in the vicinity of the vehicle/mobile robot.
- Please refer to the function
consensus_set_based_height_estimation().
- Please refer to the function
-
pub_for_legoloamnode for the pointcloud in kitti bagfile is added.ground_estimate.msgis 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 about this LiDAR odometry is still incomplete. It will be updated soon!
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