A comparative study on active SLAM and autonomous exploration with Particle Filters

Abstract

Planning under uncertainty in perception and action requires the robot to be able to use active strategies for trading-off between the contrasting tasks of exploring the scenario and satisfying given constraints on the admissible uncertainty in the estimation process. In this work we compare several state-of-the-art approaches to active SLAM (Simultaneous Localization and Mapping) and exploration using Rao-Blackwellized Particle Filters. The proposed numerical evaluation and analytical insight allow to have a clear picture of the advantages and limitations of each technique for real world applications. Extensive tests are performed in typical indoor and office environments and on well-known benchmarking scenarios belonging to SLAM literature, with the purpose of evaluating the maturity of the field and the potential of truly autonomous navigation systems based on particle filtering.