Acquisition of Place Knowledge Through Case-Based Learning.

Abstract

In this paper we define the task of place learning and describe one approach to this problem. The framework represents distinct places as evidence grids, a probabilistic description of occupancy. Place recognition relies on case-based classification, augmented by a registration process to correct for translations. The learning mechanism is also similar to that in case-based system's, involving the simple storage of inferred evidence grids. Experimental studies with physical and simulated robots suggest that this approach improves place recognition with experience, that it can handle significant sensor noise, that it benefits from improved quality in stored cases, and that it scales well to environments with many distinct 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.

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