Line-based landmark recognition for self-localization of soccer robots

This paper describes visual landmark extraction for self-localization of an autonomous mobile robot in a well-known dynamic environment. The gradient based Hough transform provides the strongest groupings of collinear pixels having roughly the same edge orientation. Groups of pixels are then processed to calculate length and end points of line segments, which together with the length and direction of the normal completes the description of the line segments. This is followed by classification of field markings which are bright lines and arcs on a dark background forming double edges of opposite gradient. Corners, junctions and line intersections are determined by the interpretation of detected line segments. Simulation results illustrate the performance of the method using both real and synthetic images.