Self-Localization in the RoboCup Environment

Knowing the position and orientation of a mobile robot situated in an environment is a critical element for effectively accomplishing complex tasks requiring autonomous navigation. Techniques for robot self-localization have been extensively studied in the past, but an effective general solution does not exist, and it is often necessary to integrate different methods in order to improve the overall result.In this paper we present a self-localization method that is based on the Hough Transform for matching a geometric reference map with a representation of range information acquired by the robot’s sensors. The technique is adequate for indoor office-like environments, and specifically for those environments that can be represented by a set of segments. We have implemented and successfully tested this method in the RoboCup environment and we consider this a good benchmark for its use in office-like environments populated with unknown and moving obstacles (e.g. persons moving around).