A Novel Approach to Ball Detection for Humanoid Robot Soccer

Abstract

The ability to accurately track a ball is a critical issue in humanoid robot soccer, made difficult by processor limitations and resultant inability to process all available data from a high-definition image. This paper proposes a computationally efficient method of determining position and size of balls in a RoboCup environment, and compares the performance to two common methods: one utilising Levenberg-Marquardt least squares circle fitting, and the other utilising a circular Hough transform. The proposed method is able to determine the position of a non-occluded tennis ball with less than 10% error at a distance of 5 meters, and a half-occluded ball with less than 20% error, overall outperforming both compared methods whilst executing 300 times faster than the circular Hough transform method. The proposed method is described fully in the context of a colour based vision system, with an explanation of how it may be implemented independent of system paradigm. An extension to allow tracking of multiple balls utilising unsupervised learning and internal cluster validation is described.