Utilizing the structure of field lines for efficient soccer robot localization

Abstract. The rules in RoboCup soccer more and more discourage a solely color-based orientation on the soccer field. While the field size increases, field boundary markers and goals become smaller and less colorful. For robust game play, robots therefore need to maintain a state and rely on more subtle environmental clues. Field lines are particularly interesting, because they are hardly completely occluded and observing them significantly reduces the number of possible poses on the field. In this work we present a method for line-based localization. Unlike previous work, our method first recovers a line structure graph from the image. From the graph we can then easily derive features such as lines and corners. Finally, we describe optimizations for efficient use of the derived features in a particle filter. The method described in this paper is used regularly on our humanoid soccer robots.