Multi-sensor fusion for reduced uncertainty in autonomous mobile robot docking and recharging

The power management system for autonomous mobile robots is an important issue for keeping robots in their long-time functionality. Recharging is necessary before the power of the robot has exhausted. In this paper, we propose a multi-sensor fusing method using intensity and range data fusion with covariance intersection approach to locate the robot pose while performing the docking for recharging. An artificial landmark is employed as a visual cue on a docking station in order to recognize the location by using inverse perspective projection. At the same time, the range data acquired by laser range finder are modeled as multiple line segments which are the hypothetical walls in the environment. Then the geometrical relationship between the robot and the docking station is estimated much more precise by using covariance intersection approach. We have demonstrated the success of the proposed algorithms through experimental results.