Landmark Based Global Self-localization of Mobile Soccer Robots

We present a stereo vision based global self-localization strategy for tiny autonomous mobile robots in a well-known dynamic environment. Global localization is required for an initial startup or when the robot loses track of its pose during navigation. Existing approaches are based on dense range scans, active beacon systems, artificial landmarks, bearing measurements using omni-directional cameras or bearing/range calculation using single frontal cameras, while we propose feature based stereo vision system for range calculation. Location of the robot is estimated using range measurements with respect to distinct landmarks such as color transitions, corners, junctions and line intersections. Unlike methods based on angle measurement, this method requires only two distinct landmarks. Simulation results show that robots can successfully localize themselves whenever two distinct landmarks are observed. As such marked minimization of landmarks for vision based self-localization of robots has been achieved.