The KITTI Vision Benchmark Suite

This is the odometry results of using our LiDAR
moving object segmentation method with SuMa. The
ability to detect and segment moving objects in a
scene is essential for building consistent maps,
making future state predictions, avoiding
collisions, and planning. In this paper, we
address the problem of moving object segmentation
from 3D LiDAR scans. We propose a novel approach
that pushes the current state of the art in
LiDAR-only moving object segmentation forward to
provide relevant information for autonomous
robots and other vehicles. Instead of segmenting
the point cloud semantically, i.e., predicting
the semantic classes such as vehicles,
pedestrians, buildings, roads, etc., our approach
accurately segments the scene into moving and
static objects, i.e., distinguishing between
moving cars vs. parked cars. Using our method,
one can easily improve his LiDAR-based
odometry/SLAM as well as 3D mapping results.

We created a new benchmark for LiDAR-based moving
object segmentation based on SemanticKITTI. We
publish it to allow other researchers to compare
their approaches transparently and we published our
code here: https://github.com/PRBonn/LiDAR-MOS

@article{chen2021ral,
title={{Moving Object Segmentation in 3D LiDAR
Data: A Learning-based Approach Exploiting
Sequential Data}},
author={X. Chen and S. Li and B. Mersch and L.
Wiesmann and J. Gall and J. Behley and C.
Stachniss},
year={2021},
journal={IEEE Robotics and Automation Letters
(RA-L)},
doi = {10.1109/LRA.2021.3093567},
issn = {2377-3766},
}