Stereo vision-based self-localization system for RoboCup

Abstract

This work proposes a new Stereo Vision-Based Self-Localization System (SVBSLS) for the RoboCup soccer humanoid league rules for the 2010 competition. The humanoid robot integrates the information from the pan/tilt motors and stereo vision to accomplish the self-localization and measure the distance of the robot and the soccer ball. The proposed approach uses the trigonometric function to find the coarse distances from the robot to the landmark and the robot to the soccer ball, and then it further adopts the artificial neural network technique to increase the precision of the distance. The statistics approach is also used to calculate the relationship between the humanoid robot and the position of the landmark for self-localization. The experimental results indicate that the localization system of SVBSLS in this research work has 100% average accuracy ratio for localization. The average error of distance from the humanoid soccer robot to the soccer ball is only 0.64 cm.