Hippocampal models for simultaneous localisation and mapping on an autonomous robot

Milford, Michael & Wyeth, Gordon (2003) Hippocampal models for simultaneous localisation and mapping on an autonomous robot. In Roberts, Jonathan & Wyeth, Gordon (Eds.) Proceedings of the Australasian Conference on Robotics and Automation, 2003, Australian Robotics and Automation Association Inc, Brisbane, Queensland.

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To navigate successfully in a novel environment a robot needs to be able to Simultaneously Localize And Map (SLAM) its surroundings. The most successful solutions to this problem so far have involved probabilistic algorithms, but there has been much promising work involving systems based on the workings of part of the rodent brain known as the hippocampus. In this paper we present a biologically plausible system called RatSLAM that uses competitive attractor networks to carry out SLAM in a probabilistic manner. The system can effectively perform parameter self-calibration and SLAM in one dimension. Tests in two dimensional environments revealed the inability of the RatSLAM system to maintain multiple pose hypotheses in the face of ambiguous visual input. These results support recent rat experimentation that suggest current competitive attractor models are not a complete solution to the hippocampal modelling problem.

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ID Code: 32819
Item Type: Conference Paper
Refereed: Yes
Additional URLs:
ISBN: 0958758352
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Adaptive Agents and Intelligent Robotics (080101)
Copyright Owner: Copyright 2003 [please consult the authors]
Deposited On: 23 Jun 2010 00:22
Last Modified: 23 Jun 2010 00:22

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