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

Alexander Schaefer1, Johan Vertens2, Daniel Büscher3, Wolfram Burgard4

  • 1Freiburg University
  • 2University of Freiburg
  • 3Albert-Ludwigs-Universität Freiburg
  • 4University of Technology Nuremberg

Details

10:45 - 12:00 | Mon 20 May | Room 220 POD 02 | MoA1-02.5

Session: Object Recognition I - 1.1.02

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 https://github.com/acschaefer/ppe.