Optimizing precision of self-localization in the simulated robotics soccer

Abstract

We show that previously published visual data processing methods for the simulated robotic soccer so far have not been utilizing all available information, because they were mainly based on heuristic considerations. Researchers have approached to estimating the agent location and orientation as two separate tasks, which caused systematic errors in the angular measurements. Further attempts to get rid of them (e.g. by completely neglecting the angular data) only aggravated the problem and resulted in the losses in the accuracy. We utilize all the potential of the visual sensor by jointly estimating the agent view direction angle and Cartesian coordinates using the extended Kalman filtering technique. Our experiments showed that the achievable average error limit for this particular application is about 25-33 per cent lower than that of the best algorithms published by far. © Springer-Verlag Berlin Heidelberg 2005.