Removert

What is removert?

Static map construction in the wild.
A dynamic points removing tool by constructing a static map
The name is from the abbreviation of our title "Remove-then-revert" (IROS 2020): paper, video

What can we do using removert?

We can easily construct and save a static map.
We can easily parse dynamic points

Example

Preparations

Step 1: Get a set of LiDAR scans and corresponding poses by running any open source LiDAR odometry or SLAM algorithm (e.g., pose-and-scan saver of SC-LIO-SAM or pose-and-scan saver of SC-A-LOAM)
Step 2: Make a pair of a scan's point cloud and a corresponding pose using associated timestamps. We note that you need to save a scan as a binary format as KITTI and the pose file as a single text file where SE(3) poses are written line-by-line (12 numbers for a single line), which is also the equivalent format as KITTI odometry's ground truth pose txt file.

Requirements

Based on C++17
ROS (and Eigen, PCL, OpenMP): the all examples in this readme are tested under Ubuntu 18.04 and ROS Melodic.
FYI: We uses ROS's parameter parser for the convenience, despite no topic flows within our system (our repository currently runs at offline on the pre-prepared scans saved on a HDD or a SSD). But the speed is fast (over 10Hz for a single removing) and plan to extend to real-time slam integration in future.

How to use

First, compile the source

$ mkdir -p ~/catkin/removert_ws/src
$ cd ~/catkin/removert_ws/src
$ git clone https://github.com/irapkaist/removert.git
$ cd ..
$ catkin_make
$ source devel/setup.bash

Before to start the launch file, you need to replace data paths in the config/params.yaml file. More details about it, you can refer the above tutorial video (KITTI 09)
Then, you can start the Removert

$ roslaunch removert run_kitti.launch # if you use KITTI dataset 
 or
$ roslaunch removert run_scliosam.launch # see this tutorial: https://youtu.be/UiYYrPMcIRU

(Optional) we supports Matlab tools to visulaize comparasions of original/cleaned maps (see tools/matlab).

Further Improvements

We propose combining recent deep learning-based dynamic removal (e.g., LiDAR-MOS) and our method for better map cleaning
- Deep learning-based removal could run online and good for proactive removal of bunch of points.
- Removert currently runs offline but good at finer cleaning for the remained 3D points after LiDAR-MOS ran.
A tutorial video and an example result for the KITTI 01 sequence:

Contact

paulgkim@kaist.ac.kr

Cite Removert

@INPROCEEDINGS { gskim-2020-iros,
    AUTHOR = { Giseop Kim and Ayoung Kim },
    TITLE = { Remove, then Revert: Static Point cloud Map Construction using Multiresolution Range Images },
    BOOKTITLE = { Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) },
    YEAR = { 2020 },
    MONTH = { Oct. },
    ADDRESS = { Las Vegas },
    NOTE = { Accepted. To appear. },
}

License

This work is supported by Naver Labs Corporation and by the National Research Foundation of Korea (NRF). This work is also licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

TODO (in order)

Near future

Full sequence cleaned-scan saver by automatically iterating batches (because using 50-100 scans for a single batch is recommended for computation speed)
Adding revert steps (I think certainly removing dynamic points is generally more worthy for many applications, so reverting step is omitted currently)
Automatically parse dynamic segments from the dynamic points in a scan (e.g., using DBSCAN on dynamic points in a scan)
Exmaples from MulRan dataset (for showing removert's availability for various LiDAR configurations) — see this tutorial
(scan, pose) pair saver using SC-LeGO-LOAM or SC-LIO-SAM, which includes a loop closing that can make a globally consistent map. — see this tutorial
Examples from the arbitrary datasets using the above input data pair saver.
Providing a SemanticKITTI (as a truth) evaluation tool (i.e., calculating the number of points of TP, FP, TN, and FN)
(Not certain now) Changing all floats to double

Future

Real-time LiDAR SLAM integration for better odometry robust to dynamic objects in urban sites (e.g., with LIO-SAM in the Riverside sequences of MulRan dataset)
Multi-session (i.e., inter-session) change detection example
Defining and measuring the quality of a static map
Using the above measure, deciding when removing can be stopped with which resolution (generally 1-3 removings are empirically enough but for highly crowded environments such as urban roads)

Jaramyy/removert