Towards eliminating manual color calibration at robocup

Authors:
Mohan Sridharan;Peter Stone
Affiliations:
Electrical and Computer Engineering, The University of Texas at Austin;Department of Computer Sciences, The University of Texas at Austin
Venue:
RoboCup 2005
Year:
2006

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Abstract

Color calibration is a time-consuming, and therefore costly requirement for most robot teams at RoboCup. This paper presents an approach for autonomous color learning on-board a mobile robot with limited computational and memory resources. It works without any labeled training data and trains autonomously from a color-coded map of its environment. The process is fully implemented, completely autonomous, and provides high degree of segmentation accuracy. Most importantly, it dramatically reduces the time needed to train a color map in a new environment.