A Maximum Likelihood Approach to Extract Polylines from 2-D Laser Range Scans
This work addresses the need for accurate geometric representation in man-made environments like households and offices, offering a novel method that improves upon prior techniques.
The authors tackled the problem of extracting polylines from 2-D laser range scans by proposing a probabilistic method that maximizes scan likelihood, and they demonstrated that it substantially outperforms existing state-of-the-art approaches in accuracy while maintaining comparable computational requirements.
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.