A sensor fusion system using enhanced extended Kalman filter with double fuzzy logics for autonomous robot guidance

This paper presents a sensor fusion system for autonomous guidance of a robot. The sensor fusion system is physically composed of a laser range finder and two vision sensors. Also, it is systematically designed to fuse the information obtained from sensors and to overcome those sensor´s drawbacks. To be specific, it utilizes double fuzzy logics for fusion and extended Kalman filter for estimation sequentially. In experimental setup, we compare the proposed sensor fusion system and systems using sensors independently by linking a wall-following algorithm for autonomous robot guidance. The result shows that the proposed system has robustness against environments with some difficult conditions.