MapCleaner Unofficial

• This repository is an unofficial implementation of "MapCleaner", a method for removing moving objects from point cloud maps.
• Please note that I created this software as a hobby and there is no guarantee that the contents of the paper are accurately implemented.

KITTI Odometry 07

Result	Terrain

ERASOR bongeunsa_dataset

Result	Terrain

Update (2025/03/31)

Support for GLIM

• Supported the format of traj_lidar.txt output by GLIM.
• By default, GLIM does not store the individual scan frames required to run the algorithm.
• Use this plugin to save individual scan frames.

Update (2025/03/24)

Algorithm and License Change

• The algorithm used for GroundSegmentation has been changed from TRAVEL to PatchWork++.
• Because this change made it no longer necessary to apply the GPL license, the license of this repository has been changed to BSD.

Docker

• This repository uses ROS1 for visualization, but since ROS1 has become obsolete and it is difficult to prepare an environment, I have prepared a Docker environment.
• Please refer to the README in the docker directory for how to use the Docker environment.

Env

• Ubuntu 20.04 LTS
• ROS Noetic

How to build

• Dependencies

sudo apt install ros-noetic-grid-map ros-noetic-pcl-ros

• Build

mkdir -p catkin_ws/src
cd catkin_ws/src
git clone "this repo" src
catkin_make

How to use

1. Open config/config.yamlorconfig/config_kitti.yaml.
2. Change the following part of the opened yaml.

loader:
  pcds_dir: "[Directory path containing LiDAR scan frames]"
  pose_file: "[File path of pose data]"
  kitti_calibration_file: "[File path of KITTI calibration data]"
  start: [Start frame number]
  end: [End frame number] #If you set a negative value, it will be processed until the end.
  format: "[Dataset format]" #Please specify "kitti" or "erasor"

map_cleaner:
  save_dir: "[Directory path where results are saved]"

3. Run with the following command.

source devel/setup.bash

roslaunch map_cleaner run.launch config:=[config_file_path]

4. ground.pcd, static.pcd, dynamic.pcd, ground_below, other.pcd, terrain.pcd are output to the directory specified by save_dir.

*1: other.pcd contains the unprocessed point clouds that are not on the terrain model. If divide_by_terrain/on_terrain_only is false, all point clouds are processed. Therefore, other.pcd will not be output.

Dataset

• I recommend using SemanticKITTI's Pose data because the Pose data of KITTI Odometry is not accurate.
• In addition to the KITTI Odometry format, it also supports the data format indicated in the "In your own dataset" section of ERASOR. Please refer to ERASOR's README for details.
• My other repository SimpleLoopClosure supports output in ERASOR format.

Difference from paper

• In the paper, Patchwork is used for the initial estimation of ground point clouds, but in this repository, PatchWork++ is used.
• A 3x3 Median Filter has been added after the second BGK Filter.
• The judgment condition of "Fused Result" is partially different from the pseudocode in the paper.

Citation

• Original Paper

@Article{rs14184496,
    AUTHOR = {Fu, Hao and Xue, Hanzhang and Xie, Guanglei},
    TITLE = {MapCleaner: Efficiently Removing Moving Objects from Point Cloud Maps in Autonomous Driving Scenarios},
    JOURNAL = {Remote Sensing},
    VOLUME = {14},
    YEAR = {2022},
    NUMBER = {18},
    ARTICLE-NUMBER = {4496},
    URL = {https://www.mdpi.com/2072-4292/14/18/4496},
    DOI = {10.3390/rs14184496}
    }

• PatchWork++

@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},
    pages={13276-13283}
}

@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 Robot. Autom. Lett.},
    volume={6},
    number={4},
    pages={6458--6465},
    year={2021},
    }

• Nanoflann

@misc{blanco2014nanoflann,
  title        = {nanoflann: a {C}++ header-only fork of {FLANN}, a library for Nearest Neighbor ({NN}) with KD-trees},
  author       = {Blanco, Jose Luis and Rai, Pranjal Kumar},
  howpublished = {\url{https://github.com/jlblancoc/nanoflann}},
  year         = {2014}
}