Particle Filters

Rao–Blackwellized particle filters (RBPFs) are an implementation of sequential Bayesian filtering that has been successfully applied to mobile robot simultaneous localization and mapping (SLAM) and ex-ploration. Measuring the uncertainty of the distribution estimated by a RBPF is required for tasks such as information gain-guided explo-ration or detecting loop closures in nested loop environments. In this paper we propose a new measure that takes the uncertainty in both the robot path and the map into account. Our approach relies on the entropy of the expected map (EM) of the RBPF, a new variable built by integrating the map hypotheses from all of the particles. Unlike previous works that use the joint entropy of the RBPF for active ex-ploration, our proposal is better suited to detect opportunities to close loops, a key aspect to reduce the robot path uncertainty and conse-quently to improve the quality of the maps being built. We provide a theoretical discussion and experimental results with real data that support our claims. KEY WORDS—localization, mapping 1