Subjective mapping

Authors:
Michael Bowling;Dana Wilkinson;Ali Ghodsi
Affiliations:
Department of Computing Science, University of Alberta, Edmonton, Alberta, Canada;School of Computer Science, University of Waterloo, Waterloo, Ontario, Canada;Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, Ontario, Canada
Venue:
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Year:
2006

Citing 6
Cited 1

Map learning with uninterpreted sensors and effectors

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Learning predictive state representations in dynamical systems without reset

ICML '05 Proceedings of the 22nd international conference on Machine learning
Learning subjective representations for planning

IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Unsupervised learning of image manifolds by semidefinite programming

CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition

FAB-MAP: Probabilistic Localization and Mapping in the Space of Appearance

International Journal of Robotics Research

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Abstract

Extracting a map from a stream of experience is a key problem in robotics and artificial intelligence in general. We propose a technique, called subjective mapping, that seeks to learn a fully specified predictive model, or map, without the need for expert provided models of the robot's motion and sensor apparatus. We briefly overview the recent advancements presented elsewhere (ICML, IJCAI, and ISRR) that make this possible, examine its significance in relationship to other developments in the field. and outline open issues that remain to be addressed.