Color Image Processing and Self-Localization for a Humanoid Soccer Robot

Abstract

The paper proposed a series of image processing algorithm to recognize the evidences in an image accurately for humanoid soccer robot, such as color image segmentation based on HSV model, edge detection based on four linear operator, field straight line extraction by Hough transform based on 8-neighbhour connected domain clusters and identification of line intersection shape based on Hopfield network. Based on evidences from image processing, Piecewise Monte Carlo localization is presented to solve kidnap problem so that localization of humanoid robot can be not only adapt to rule changes for competition, but also be more efficient and robust. The effectiveness of the piecewise MCL is verified by RoboCup Adult Size humanoid soccer robot, Erectus. The experimental results showed that the humanoid robot was able to solve the kidnap problem adaptively with two strategies: resetting or revising, in which resetting was more efficient than revising gradually.