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.