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

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