3次元レーザースキャンから有限平面を抽出する最尤推定法
A Maximum Likelihood Approach to Extract Finite Planes from 3-D Laser Scans
3次元レーザースキャン中の有限平面を、点の追加で計測尤度が最も下がらないように階層クラスタリングで抽出する確率的手法を提案し、SegCompベンチマークと新規合成データセットで評価した。
著者: Alexander Schaefer, Johan Vertens, Daniel Büscher, Wolfram Burgard
分類: cs.RO, cs.CV
原文アブストラクト
Whether it is object detection, model reconstruction, laser odometry, or point cloud registration: Plane extraction is a vital component of many robotic systems. In this paper, we propose a strictly probabilistic method to detect finite planes in organized 3-D laser range scans. An agglomerative hierarchical clustering technique, our algorithm builds planes from bottom up, always extending a plane by the point that decreases the measurement likelihood of the scan the least. In contrast to most related methods, which rely on heuristics like orthogonal point-to-plane distance, we leverage the ray path information to compute the measurement likelihood. We evaluate our approach not only on the popular SegComp benchmark, but also provide a challenging synthetic dataset that overcomes SegComp's deficiencies. Both our implementation and the suggested dataset are available at www.github.com/acschaefer/ppe.
