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

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
or
roslaunch map_cleaner run_kitti.launch

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.
*2: TRAVEL parameters are located in thirdparty/travel/config.

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, TRAVEL 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}
    }

• TRAVEL

@ARTICLE{oh2022travel,  
    author={Oh, Minho and Jung, Euigon and Lim, Hyungtae and Song, Wonho and Hu, Sumin and Lee, Eungchang Mason and Park, Junghee and Kim, Jaekyung and Lee, Jangwoo and Myung, Hyun},  
    journal={IEEE Robotics and Automation Letters},   
    title={TRAVEL: Traversable Ground and Above-Ground Object Segmentation Using Graph Representation of 3D LiDAR Scans},   
    volume={7},  
    number={3},  
    pages={7255-7262},  
    year={2022},
    }

@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},
    }

@article{lim2021erasor,
    title={ERASOR: Egocentric Ratio of Pseudo Occupancy-Based Dynamic Object Removal for Static 3D Point Cloud Map Building},
    author={Lim, Hyungtae and Hwang, Sungwon and Myung, Hyun},
    journal={IEEE Robotics and Automation Letters},
    volume={6},
    number={2},
    pages={2272--2279},
    year={2021},
    publisher={IEEE}
    }

• 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}
}