  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.
