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

Alexander Schaefer; Daniel Büscher; Lukas Luft; Wolfram Burgard

IROS 2018poster13 citations

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

Alexander Schaefer, Daniel Büscher, Lukas Luft, Wolfram Burgard

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.

BibTeX

@inproceedings{iros2018_amaximumlikeliho,
  title = {A Maximum Likelihood Approach to Extract Polylines from 2-D Laser Range Scans},
  author = {Alexander Schaefer and Daniel Büscher and Lukas Luft and Wolfram Burgard},
  booktitle = {IROS 2018},
  year = {2018}
}