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.
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Sridharan, M., Stone, P. (2006). Towards Eliminating Manual Color Calibration at RoboCup. In: Bredenfeld, A., Jacoff, A., Noda, I., Takahashi, Y. (eds) RoboCup 2005: Robot Soccer World Cup IX. RoboCup 2005. Lecture Notes in Computer Science(), vol 4020. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11780519_68
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DOI: https://doi.org/10.1007/11780519_68
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