This Article Full Text (PDF) References Alert me when this article is cited Alert me if a correction is posted Citation Map Services Email this article to a friend Similar articles in this journal Alert me to new issues of the journal Add to Saved Citations Download to citation manager Request Permissions Request Reprints Add to My Marked Citations Citing Articles Citing Articles via Google Scholar Google Scholar Articles by Balakrishnan, K. Articles by Honavar, V. Search for Related Content Social Bookmarking                   What's this?

Spatial Learning and Localization in Rodents: A Computational Model of the Hippocampus and its Implications for Mobile Robots

Allstate Research and Planning Center

Olivier Bousquet

Ecole Nationale Superieure des Telecommunications, Paris

Vasant Honavar

Iowa State University

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.

Key Words: spatial learning • robot localization • hippocampal model • Kalman filter • probabilistic localization.

CiteULike    Connotea    Del.icio.us    Digg    Reddit    Technorati    What's this?