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arXiv · 1910.10711

A Maximum Likelihood Approach to Extract Polylines from 2-D Laser Range Scans

Alexander Schaefer·Daniel Büscher·Lukas Luft·Wolfram Burgard
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Abstract

Man-made environments such as households, offices, or factory floors are typically composed of linear structures. Accordingly, polylines are a natural way to accurately represent their geometry. In this paper, we propose a novel probabilistic method to extract polylines from raw 2-D laser range scans. The key idea of our approach is to determine a set of polylines that maximizes the likelihood of a given scan. In extensive experiments carried out on publicly available real-world datasets and on simulated laser scans, we demonstrate that our method substantially outperforms existing state-of-the-art approaches in terms of accuracy, while showing comparable computational requirements. Our implementation is available under https://github.com/acschaefer/ple.

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Alexander Schaefer, Daniel Büscher, Lukas Luft, Wolfram Burgard. 2019-10-23. A Maximum Likelihood Approach to Extract Polylines from 2-D Laser Range Scans. https://doi.org/10.1109/iros.2018.8593844

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