The ability to acquire a representation of the spatial environment and the ability to localize within it are essential for successful navigation in a-priori unknown environments. The hippocampal forma tion is believed to play a key role in spatial learning and localization in animals in general and rodents in particular. This paper briefly reviews the relevant neurobiological and cognitive data, and their relation to computational models of spatial learning and localization used in contempo rary mobile robots. It proposes a hippocampal model of spatial learning and localization, and characterizes it using a Kalman filter based tool for information fusion from multiple uncertain sources. The resulting model not only explains neurobiological and behavioral data from rodent experiments, but also allows a robot to learn a place-based metric representation of space and to localize itself in a stochastically optimal manner. The paper presents an algorithmic implementa tion of the model and results of several experiments that demonstrate its capabilities. These include the ability to disambiguate perceptually similar places, scale well with increasing errors, and the automatic acquisition of spatial information at multiple resolutions.
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