Recovering Particle Diversity in a Rao-Blackwellized Particle Filter for SLAM After Actively Closing Loops

Acquiring models of the environment belongs to the fundamental tasks of mobile robots. Approaches addressing the problem of simultaneous localization and mapping (SLAM) typically process the perceived sensor data and do not influence the motion of the mobile robot. In this paper, we present an approach to actively closing loops during exploration. It applies a Rao-Blackwellized particle filter to maintain multiple hypotheses about potential trajectories of the robot and corresponding maps. To prevent the particle filter from becoming overly confident, we present a technique to recover the particle diversity after successfully closing a loop. This way the particle depletion problem is avoided. The combination of our approach with the active loop closing strategy allows to deal with multiple nested loops. Experimental results presented in this paper illustrate the advantage of our method over pervious approaches to mapping with Rao-Blackwellized particle filters.