Moving Object Segmentation in 3D LiDAR Data: A Learning-based Approach Exploiting Sequential Data

Understand

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.