Spatial learning and localization in rodents: A computational model of thehippocampus and its implications for mobile robots

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 formation is believed to play a key role in spatial learning and localization in animals in general and r odents in particular. This paper briefly reviews the relevant neurobiologic al and cognitive data, and their relation to computational models of spatia l learning and localization used in contemporary mobile robots. It proposes a hippocampal model of spatial learning and localization, and characterize s it using a Kalman filter based tool for information fusion from multiple uncertain sources. The resulting model not only explains neurobiological an d 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 s tochastically optimal manner. The paper presents an algorithmic implementat ion of the model and results of several experiments that demonstrate its ca pabilities. These include the ability to disambiguate perceptually similar places, scale well with increasing errors, and the automatic acquisition of spatial information at multiple resolutions.