RatSLAM: a hippocampal model for simultaneous localization and mapping

Abstract

The work presents a new approach to the problem of simultaneous localization and mapping - SLAM - inspired by computational models of the hippocampus of rodents. The rodent hippocampus has been extensively studied with respect to navigation tasks, and displays many of the properties of a desirable SLAM solution. RatSLAM is an implementation of a hippocampal model that can perform SLAM in real time on a real robot. It uses a competitive attractor network to integrate odometric information with landmark sensing to form a consistent representation of the environment. Experimental results show that RatSLAM can operate with ambiguous landmark information and recover from both minor and major path integration errors.

DOI: 10.1109/ROBOT.2004.1307183

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@article{Milford2004RatSLAMAH, title={RatSLAM: a hippocampal model for simultaneous localization and mapping}, author={Michael Milford and Gordon Wyeth and David Prasser}, journal={IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA '04. 2004}, year={2004}, volume={1}, pages={403-408 Vol.1} }