Case-Based Acquisition of Place Knowledge

In this paper we deene the task of place learning and describe one approach to this problem. The framework represents distinct places using evidence grids, a probabilistic description of occupancy. Place recognition relies on case-based classiication, augmented by a registration process to correct for translations. The learning mechanism is also similar to that in case-based systems, involving the simple storage of inferred evidence grids. Experimental studies with both physical and simulated robots suggest that this approach improves place recognition with experience, that it can handle signiicant sensor noise, and that it scales well to increasing numbers of places. Previous researchers have studied evidence grids and place learning, but they have not combined these two powerful concepts, nor have they used the experimental methods of machine learning to evaluate their methods' abilities.