An application of Kullback-Leibler divergence to active SLAM and exploration with Particle Filters

Abstract

Autonomous exploration under uncertain robot position requires the robot to plan a suitable motion policy in order to visit unknown areas while minimizing the uncertainty on its pose. The corresponding problem, namely active SLAM (Simultaneous Localization and Mapping) and exploration has received a large attention from the robotic community for its relevance in mobile robotics applications. In this work we tackle the problem of active SLAM and exploration with Rao-Blackwellized Particle Filters. We propose an application of Kullback-Leibler divergence for the purpose of evaluating the particle-based SLAM posterior approximation. This metric is then applied in the definition of the expected gain from a policy, which allows the robot to autonomously decide between exploration and place revisiting actions (i.e., loop closing). The technique is shown to enhance robot awareness in detecting loop closing occasions, which are often missed when using other state-of-the-art approaches. Results of extensive tests are reported to support our claims.