Robust Moving Objects Detection in Lidar Data Exploiting Visual Cues
This work addresses moving object detection for autonomous navigation, but it is incremental as it builds on a prior state-of-the-art method.
The paper tackled the problem of detecting moving objects in lidar data by improving an existing method's speed and accuracy through discretization, visual cues, and ground plane removal, achieving enhanced performance on the KITTI dataset.
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.