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

TL;DR

This paper introduces a probabilistic hierarchical clustering method for extracting finite planes from 3-D laser scans, improving accuracy by utilizing ray path information and evaluating on both benchmark and synthetic datasets.

Contribution

It presents a novel likelihood-based approach for plane detection that outperforms heuristic methods and provides a new synthetic dataset for evaluation.

Findings

Effective plane extraction demonstrated on benchmark data

Improved accuracy using ray path likelihood computation

Availability of implementation and synthetic dataset

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.