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

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.