An Entropy-Based Measurement of Certainty in Rao-Blackwellized Particle Filter Mapping

In Bayesian based approaches to mobile robot simultaneous localization and mapping, Rao-Blackwellized particle filters (RBPF) enable the efficient estimation of the posterior belief over robot poses and the map. These particle filters have been recently adopted by many exploration approaches, to whom a central issue is measuring the certainty inherent to a given estimation in order to be able to select robot actions that increase it. In this paper we propose a new certainty measurement grounded in information theory that unifies the two kinds of uncertainty which are intrinsic to SLAM: in the robot pose and in the map content. Most previous works have considered only one of them or a weighted average. Our method combines them more appropriately by first building an expected map (EM) which condenses all the current map hypotheses and then computing its mean information (MI) - an entropy derived measurement that quantifies the inconsistencies in the EM. Experimental results comparing our method (EMMI) with others verify its correctness and its better behavior for detecting the decrease in certainty when the robot enters unexplored areas and its increase after closing a loop