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. © Springer-Verlag Berlin Heidelberg 2006.
CITATION STYLE
Sridharan, M., & Stone, P. (2006). Towards eliminating manual color calibration at RoboCup. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4020 LNAI, pp. 673–681). Springer Verlag. https://doi.org/10.1007/11780519_68
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