20chase / ERASOR

Official page of ERASOR RA-L with ICRA'21

🌈 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! :)

(PLEASE REMIND THAT THIS REPO IS IN PROGRESS!! ERASOR is operated well, but other utility files may be completed by ~6/6)

So, ⚠️ implies that code is already in this repo but the explanation is not elaborated)

Contact: Hyungtae Lim ([e-mail])

Advisor: Hyun Myung ([e-mail])

Contents

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

Test Env.

The code is tested successfully at

• Linux 18.04 LTS
• ROS Melodic

Requirements

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

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

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<int> DYNAMIC_CLASSES 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 (:warning: To be explained). 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_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"

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

Calculate PR/RR

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

python analysis.py

Benchmark

⚠️

• 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

• 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

Citation

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}
}