A Maximum Likelihood Approach to Extract Finite Planes from 3-D Laser Scans
This work addresses plane extraction for robotic systems, offering a more principled approach compared to heuristic methods, though it is incremental in nature.
The paper tackles the problem of detecting finite planes in 3-D laser scans by proposing a probabilistic method that uses ray path information to compute measurement likelihood, achieving competitive results on benchmarks like SegComp and a new synthetic dataset.
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.