Robust Moving Objects Detection in Lidar Data Exploiting Visual Cues

TL;DR

This paper presents a novel method for detecting moving objects in lidar data by enhancing existing occupancy-based techniques with visual cues and ground plane removal, achieving improved speed and accuracy.

Contribution

It introduces a discretized occupancy representation combined with visual cues and a ground plane removal algorithm, enhancing detection performance over prior methods.

Findings

Improved detection accuracy on KITTI dataset

Reduced false positives, especially on ground plane

Faster processing through octree indexing

Abstract

Detecting moving objects in dynamic scenes from sequences of lidar scans is an important task in object tracking, mapping, localization, and navigation. Many works focus on changes detection in previously observed scenes, while a very limited amount of literature addresses moving objects detection. The state-of-the-art method exploits Dempster-Shafer Theory to evaluate the occupancy of a lidar scan and to discriminate points belonging to the static scene from moving ones. In this paper we improve both speed and accuracy of this method by discretizing the occupancy representation, and by removing false positives through visual cues. Many false positives lying on the ground plane are also removed thanks to a novel ground plane removal algorithm. Efficiency is improved through an octree indexing strategy. Experimental evaluation against the KITTI public dataset shows the effectiveness of our approach, both qualitatively and quantitatively with respect to the state- of-the-art.