視覚的手がかりを活用したLiDARデータ中の移動物体のロバスト検出
Robust Moving Objects Detection in Lidar Data Exploiting Visual Cues
LiDARスキャンから移動物体を検出する手法を改良し、占有表現の離散化・視覚的手がかりによる誤検出除去・地面平面除去・octree索引化で速度と精度を向上させた。KITTIデータセットで有効性を実証。
著者: Gheorghii Postica, Andrea Romanoni, Matteo Matteucci
分類: cs.RO, cs.CV
原文アブストラクト
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.
