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, roads, etc., our approach accurately
segments the scene into moving and static objects, i.e., also distinguishing
between moving cars vs. parked cars. Our proposed approach exploits sequential
range images from a rotating 3D LiDAR sensor as an intermediate representation
combined with a convolutional neural network and runs faster than the frame
rate of the sensor. We compare our approach to several other state-of-the-art
methods showing superior segmentation quality in urban environments.
Additionally, we created a new benchmark for LiDAR-based moving object
segmentation based on SemanticKITTI. We published it to allow other researchers
to compare their approaches transparently and we furthermore published our
code.

本篇论文提出了一种基于 3D LiDAR 扫描的新颖的运动对象分割方法，可以将环境精确地分割为运动和静态对象，相比于其他先进的方法在城市环境中具有更高的分割质量。