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 [Video] [Preprint Paper]

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 (shapelimatkaistdotacdotkr)

Advisor: Hyun Myung (hmyungatkaistdotacdotkr)

• An example of running ERASOR in your own env. is provided.
  • Please refer to please refer to src/offline_map_updater/main_in_your_env.cpp and launch/run_erasor_in_your_env_vel16.launch. The more details are here.

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

The code is tested successfully at

• Linux 18.04 LTS
• ROS Melodic

• 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

• Thereafter, compile this package. We use catkin tools,

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

• 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

• 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

• 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<int> DYNAMIC_CLASSES in our code :).

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 for evaluation to fill corresponding labels. The voxelized map will be an input of step 2 as a naively accumulated map.

Step 2. Run 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_2670_w_interval_2_node.bag

News (22.03.01): The submap module is employed to speed up when extracing map VOI.

Plase check the below rosparams in run_erasor.launch:

<rosparam param="/large_scale/is_large_scale">true</rosparam>
<rosparam param="/large_scale/submap_size">160.0</rosparam>

Note that appropriate submap_size is > 2 * max_range.

• 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.

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 $EST_PCD_PATH$

E.g,

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.

• 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

• Then you can inspect all pcd results that are already parsed into static points and dynamic points.
• All examples are here:
  • Sequence 00: 4,390~4,530
  • Sequence 01: 150~250
  • Sequence 02: 860~950
  • Sequence 05: 2,350~2,670
  • Sequence 07: 630~820

To check generalization of ERASOR, we tested ERASOR in more crowded environments. In that experiment, Velodyne Puck 16 was employed, and poses are estimated by LIO-SAM.

Satellite map	Pcd map by LIO-SAM

When running ERASOR in your own environments, please refer to src/offline_map_updater/main_in_your_env.cpp file and launch/run_erasor_in_your_env_vel16.launch.

You can learn how to set experimental setting by repeating our pre-set configurations. Please follow our instructions.

• First, download pre-set dataset.

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

• Modify data_dir, MapUpdater/initial_map_path, and MapUpdater/save_path in config/your_own_env_vel16.yaml to be right directory for your machine, where data_dir should consist of following components as follows:

`data_dir`
_____pcds
     |___000000.pcd
     |___000001.pcd
     |___000002.pcd
     |...
_____dense_global_map.pcd
_____poses_lidar2body.csv
_____...

• Next, launch launch/run_erasor_in_your_env_vel16.launch as follows:

roslaunch erasor run_erasor_in_your_env_vel16.launch

• As shown in config, depending on your own sensor configuration, parameters must be changed. In particular, min_h and max_h, and th_bin_max_h should be changed (note that min_h and max_h, and th_bin_max_h is w.r.t. your body frame of a query pcd file.)
• If you use a low-channel LiDAR sensor such as Velodyne Puck-16, max_r and num_rings must be set as smaller values like config/your_own_env_vel16.yaml to guarantee the estimated normal vector for each bin is considered to be orthogonal to the ground.
• If too many points are considered as ground points for each bin, then reduce the value of gf_dist_thr.

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

@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}
}