Moving Object Segmentation in 3D LiDAR Data: A Learning-based Approach Exploiting Sequential Data
This addresses the problem of distinguishing moving from static objects for autonomous robots and vehicles, representing a strong specific gain in LiDAR-only segmentation.
The paper tackles moving object segmentation in 3D LiDAR data by proposing a learning-based approach that uses sequential range images and a convolutional neural network, achieving superior segmentation quality in urban environments and running faster than the sensor frame rate.
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.