Balakrishnan, Karthik, Bousquet, Olivier and Honavar, Vasant (1997) Spatial Learning and Localization in Animals: A Computational Model and Its Implications for Mobile Robots. Technical Report TR97-20, Department of Computer Science, Iowa State University.
The ability to acquire a representation of 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 navigation in animals. This paper briefly
reviews the relevant neurobiological and cognitive data and
their relation to computational models of spatial learning
and localization used in mobile robots. It also
describes a hippocampal model of spatial learning and
navigation and analyzes it using Kalman filter based tools for
information fusion from multiple uncertain sources. The resulting model
allows a robot to learn a place-based, metric representation
of space in a-priori unknown environments and to localize itself
in a stochastically optimal manner. The paper also describes
an algorithmic implementation of the model and results of several
experiments that demonstrate its capabilities.
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