Autonomous Exploration and Mapping with RFS Occupancy-Grid SLAM

This short note addresses the problem of autonomous on-line path-panning for exploration and occupancy-grid mapping using a mobile robot. The underlying algorithm for simultaneous localisation and mapping (SLAM) is based on random-finite set (RFS) modelling of ranging sensor measurements, implemented as a Rao-Blackwellised particle filter. Path-planning in general must trade-off between exploration (which reduces the uncertainty in the map) and exploitation (which reduces the uncertainty in the robot pose). In this note we propose a reward function based on the Rényi divergence between the prior and the posterior densities, with RFS modelling of sensor measurements. This approach results in a joint map-pose uncertainty measure without a need to scale and tune their weights