ERASOR (RA-L’21 with ICRA Option)

Official page of “ERASOR: Egocentric Ratio of Pseudo Occupancy-based Dynamic Object Removal for Static 3D Point Cloud Map Building”, which is accepted by RA-L with ICRA’21 option [Demo Video].


We provide all contents including

  • Source code of ERASOR
  • All outputs of the State-of-the-arts
  • Visualization
  • Calculation code of Preservation Rate/Rejection Rate

So enjoy our codes! 🙂

Contact: Hyungtae Lim ([email protected])

Advisor: Hyun Myung ([email protected])


  1. Test Env.
  2. Requirements
  3. How to Run ERASOR
  4. Calculate PR/RR
  5. Benchmark
  6. Run Your Own Code
  7. Visualization of All the State-of-the-arts
  8. Citation

Test Env.

The code is tested successfully at

  • Linux 18.04 LTS
  • ROS Melodic


ROS Setting

  • Install ROS on a machine.
  • Also, jsk-visualization is required to visualize Scan Ratio Test (SRT) 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

Buildg Our Package

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

Python Setting

  • Our metric calculation for PR/RR code is implemented by python2.7
  • To run the python code, following pakages are necessary: pypcd, tqdm, scikit-learn, and tabulate

pip install pypcd
pip install tqdm	
pip install scikit-learn
pip install tabulate

Prepared dataset

  • Download the preprocessed KITTI data encoded into rosbag.
  • The downloading process might take five minutes or so. All rosbags requires total 2.3G of storage space

wget https://urserver.kaist.ac.kr/publicdata/erasor/rosbag/00_4390_to_4530_w_interval_2_node.bag
wget https://urserver.kaist.ac.kr/publicdata/erasor/rosbag/01_150_to_250_w_interval_1_node.bag
wget https://urserver.kaist.ac.kr/publicdata/erasor/rosbag/02_860_to_950_w_interval_2_node.bag
wget https://urserver.kaist.ac.kr/publicdata/erasor/rosbag/05_2350_to_2670_w_interval_2_node.bag
wget https://urserver.kaist.ac.kr/publicdata/erasor/rosbag/07_630_to_820_w_interval_2_node.bag

Description of Preprocessed Rosbag Files

  • Please note that the rosbag consists of node. Refer to msg/node.msg.
  • Note that each label of the point is assigned in intensity for the sake of convenience.
  • And we set the following classes are dynamic classes:
# 252: "moving-car"
# 253: "moving-bicyclist"
# 254: "moving-person"
# 255: "moving-motorcyclist"
# 256: "moving-on-rails"
# 257: "moving-bus"
# 258: "moving-truck"
# 259: "moving-other-vehicle"
  • Please refer to std::vector

    in our code :).

How to Run ERASOR

We will explain how to run our code on seq 05 of the KITTI dataset as an example.

Step 1. Build naive map


  • Set the following parameters in launch/mapgen.launch.
    • target_rosbag: The name of target rosbag, e.g. 05_2350_to_2670_w_interval_2_node.bag
    • save_path: The path where the naively accumulated map is saved.
  • Launch mapgen.launch and play corresponding rosbag on the other bash as follows:

roscore # (Optional)
roslaunch erasor mapgen.launch
rosbag play 05_2350_to_2670_w_interval_2_node.bag
  • Then, dense map and voxelized map are auto-saved at the save path. Note that the dense map is used to fill corresponding labels (HERE). The voxelized map will be an input of step 2 as a naively accumulated map.

Step 2. Run ERASOR erasor

  • Set the following parameters in config/seq_05.yaml.

    • initial_map_path: The path of naively accumulated map
    • save_path: The path where the filtered static map is saved.
  • Run the following command for each bash.

roscore # (Optional)
roslaunch erasor run_erasor.launch target_seq:="05"
rosbag play 05_2350_to_2672_w_interval_2_node.bag
  • IMPORTANT: After finishing running ERASOR, run the following command to save the static map as a pcd file on another bash.
  • “0.2” denotes voxelization size.
rostopic pub /saveflag std_msgs/Float32 "data: 0.2"
  • Then, you can see the printed command as follows:


  • The results will be saved under the save_path folder, i.e. $save_path$/05_result.pcd.

Calculate PR/RR

You can check our results directly.

  • First, download all pcd materials.

wget https://urserver.kaist.ac.kr/publicdata/erasor/erasor_paper_pcds.zip
unzip erasor_paper_pcds.zip

Then, run the analysis code as follows:

python analysis.py --gt $GT_PCD_PATH$ --est $EST_PCD_PATH$


python analysis.py --gt /home/shapelim/erasor_paper_pcds/gt/05_voxel_0_2.pcd --est /home/shapelim/erasor_paper_pcds/estimate/05_ERASOR.pcd

NOTE: For estimating PR/RR, more dense pcd file, which is generated in the mapgen.launch procedure, is better to estimate PR/RR precisely.


  • Error metrics are a little bit different from those in the paper:

    Seq. PR [%] RR [%]
    00 91.72 97.00
    01 91.93 94.63
    02 81.08 99.11
    05 86.98 97.88
    07 92.00 98.33
  • But we provide all pcd files! Don’t worry. See Visualization of All the State-of-the-arts Section.

Run Your Own Code

TBU: The code is already in this repository, yet the explanation is incomplete.

Visualization of All the State-of-the-arts

  • First, download all pcd materials.

wget https://urserver.kaist.ac.kr/publicdata/erasor/erasor_paper_pcds.zip
unzip erasor_paper_pcds.zip
  • Set parameters in config/viz_params.yaml correctly

    • abs_dir: The absolute directory of pcd directory
    • seq: Target sequence (00, 01, 02, 05, or 07)
  • After setting the parameters, launch following command:

roslaunch erasor compare_results.launch


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

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},