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

arXiv: https://arxiv.org/abs/1910.11146 · HTML: https://mlsanity.com/paper/1910.11146/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Oct 2019.

## Built on by (most cited first, 1 of 1)

- [Graph-theoretical approach to robust 3D normal extraction of LiDAR data](https://mlsanity.com/paper/2205.11460/) (2022)

## Abstract (arXiv)

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.