🌈 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 [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)

NEWS (Recent update: Oct., 2021)

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.

Test Env.
Requirements
How to Run ERASOR
Calculate PR/RR
Benchmark
Visualization of All the State-of-the-arts
ERASOR in the Wild
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

Build 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.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 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.

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 $EST_PCD_PATH$

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.

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.

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

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:

ERASOR in the Wild

In your own dataset

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

Results

Note: Setting appropriate parameters

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.

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}
}

edwinstudy/ERASOR