Mobile robots can be considered completely autonomous if they embed active algorithms for Simultaneous Localization And Mapping (SLAM). This means that the robot is able to autonomously, or actively, explore and create a reliable map of the environment, while simultaneously estimating its pose. In this paper, we propose a novel framework to robustly solve the active SLAM problem, in scenarios in which some prior information about the environment is available in the form of a topo-metric graph. This information is typically available or can be easily developed in industrial environments, but it is usually affected by uncertainties. In particular, the distinguishing features of our approach are: the inclusion of prior information for solving the active SLAM problem; the exploitation of this information to pursue active loop closure; the on-line correction of the inconsistencies in the provided data. We present some experiments, that are performed in different simulated environments: the results suggest that our method improves on state-of-the-art approaches, as it is able to deal with a wide variety of possibly large uncertainties.
2019, 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Pages 340-346
Active SLAM using Connectivity Graphs as Priors (04b Atto di convegno in volume)
Soragna A., Baldini M., Joho D., Kummerle R., Grisetti G.
Gruppo di ricerca: Artificial Intelligence and Robotics