低解像度LiDAR点群における地面検出の改善
On Enhancing Ground Surface Detection from Sparse Lidar Point Cloud
低ビーム解像度LiDARの疎な点群に対し、点ごとの接線特徴とRANSAC平面当てはめを組み合わせ、複数の平面領域分割を積分画像で高速に最適化する地面検出手法を提案した。
著者: Bo Li
分類: cs.RO, cs.CV
原文アブストラクト
Ground surface detection in point cloud is widely used as a key module in autonomous driving systems. Different from previous approaches which are mostly developed for lidars with high beam resolution, e.g. Velodyne HDL-64, this paper proposes ground detection techniques applicable to much sparser point cloud captured by lidars with low beam resolution, e.g. Velodyne VLP-16. The approach is based on the RANSAC scheme of plane fitting. Inlier verification for plane hypotheses is enhanced by exploiting the point-wise tangent, which is a local feature available to compute regardless of the density of lidar beams. Ground surface which is not perfectly planar is fitted by multiple (specifically 4 in our implementation) disjoint plane regions. By assuming these plane regions to be rectanglar and exploiting the integral image technique, our approach approximately finds the optimal region partition and plane hypotheses under the RANSAC scheme with real-time computational complexity.
