  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.
