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arXiv · 1910.11146

A Maximum Likelihood Approach to Extract Finite Planes from 3-D Laser Scans

Alexander Schaefer·Johan Vertens·Daniel Büscher·Wolfram Burgard
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Abstract

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.

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Alexander Schaefer, Johan Vertens, Daniel Büscher, Wolfram Burgard. 2019-10-23. A Maximum Likelihood Approach to Extract Finite Planes from 3-D Laser Scans. https://doi.org/10.1109/icra.2019.8794318

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