Automatic Acquisition of Robot Motion and Sensor Models

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.

DOI: 10.1007/978-3-540-74024-7_57

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@inproceedings{Ozgelen2006AutomaticAO, title={Automatic Acquisition of Robot Motion and Sensor Models}, author={Arif Tuna Ozgelen and Elizabeth Sklar and Simon Parsons}, booktitle={RoboCup}, year={2006} }