Automatic Acquisition of Robot Motion and Sensor Models

Authors:
A. Tuna Ozgelen;Elizabeth Sklar;Simon Parsons
Affiliations:
Department of Computer & Information Science, Brooklyn College, City University of New York, 2900 Bedford Avenue, Brooklyn NY 11210, USA;Department of Computer & Information Science, Brooklyn College, City University of New York, 2900 Bedford Avenue, Brooklyn NY 11210, USA;Department of Computer & Information Science, Brooklyn College, City University of New York, 2900 Bedford Avenue, Brooklyn NY 11210, USA
Venue:
RoboCup 2006: Robot Soccer World Cup X
Year:
2006

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Abstract

For accurate self-localization using probabilistic techniques, robots require robust models of motion and sensor characteristics. Such models are sensitive to variations in lighting conditions, terrain and other factors like robot battery strength. Each of these factors can introduce variations in the level of noise considered by probabilistic techniques. Manually constructing models of noise is time-consuming, tedious and error-prone. We have been developing techniques for automatically acquiring such models, using the AIBO robot and a modified RoboCup Four-Legged League field with an overhead camera. This paper describes our techniques and presents preliminary results.