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

TL;DR

This paper introduces a learning-based method for segmenting moving objects in 3D LiDAR data, leveraging sequential range images and neural networks to outperform existing techniques in urban environments.

Contribution

It presents a novel approach that uses sequential range images with CNNs for real-time moving object segmentation, and introduces a new benchmark dataset based on SemanticKITTI.

Findings

Superior segmentation quality in urban environments

Runs faster than the LiDAR sensor frame rate

Provides a new benchmark for LiDAR-based moving object segmentation

Abstract

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.