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In this paper, a humanoid robot soccer perception system, consisting of a ball, field detection, and localization, is developed in order to deal with the new rules in RoboCup. Color segmentation and image morphology are improved together with a more sophisticated machine learning algorithm to detect a soccer ball robustly. Those algorithms are still favorable due to its real-time running in most of the embedded platform. For localization, the field is divided into pre-deðned grids and employing k-NN (k-nearest neighbor) to determine the robot location in the grids. Pre-defined grids are used to reduce computation due to matching with a map. Experiment results show that the developed system relatively well for adapting to the new rules update.

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