β-SLAM: Simultaneous localization and grid mapping with beta distributions

• Published in: Information fusion Vol. 52; pp. 62 - 75
• Main Authors: Clemens, Joachim, Kluth, Tobias, Reineking, Thomas
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.12.2019
• Subjects: Beta distribution; Mobile robots; Occupancy grid maps; Rao-Blackwellized particle filter (RBPF); Simultaneous localization and mapping (SLAM); Rao-Blackwellized particle filter (RBPF); Occupancy grid maps; Beta distribution; Simultaneous localization and mapping (SLAM); Mobile robots
• ISSN: 1566-2535; 1872-6305
• DOI: 10.1016/j.inffus.2018.11.005

•A SLAM approach that model the map’s occupancy probabilities with beta distributions.•Different types of uncertainty are explicitly represented in those maps.•Multiple uncertainty measures for navigation and active exploration are provided.•Sensor models are derived that prepare standard models for the use in the algorithm. Simultaneous localization and mapping (SLAM) is one of the most frequently studied problems in mobile robotics. Different map representations have been proposed in the past and a popular one are occupancy grid maps, which are particularly well suited for navigation tasks. The uncertainty in these maps is usually modeled as a single Bernoulli distribution per grid cell. This has the disadvantage that one cannot distinguish between uncertainty caused by different phenomena like missing or conflicting information. In this paper, we overcome this limitation by modeling the occupancy probabilities as random variables. Those are assumed to be beta-distributed and account for the different causes of uncertainty. Based on this map representation, we derive a SLAM algorithm, including all necessary sensor models, for building maps composed of beta-distributed random variables and using these maps for localization. Furthermore, we propose measures for quantifying uncertainty in the resulting maps and for solving navigation tasks. We evaluate our approach using real-world as well as simulation-based datasets and we compare it to a state-of-the-art SLAM algorithm for building classical grid maps.