A reliable position estimation method of the service robot by map matching

In this paper, a reliable position estimation method of the indoor service robot is proposed. The service robot PSR1 is a wheeled mobile manipulator which navigates in office buildings. Our localization method is a map-matching scheme using scanned range data, without using any artificial landmark. The proposed algorithm can provide solutions for both a global localization problem and a local position tracking. A probabilistic position estimation scheme is designed based on MCL (Monte Carlo localization). Two measure functions are developed for computing positional probabilities. The robot automatically decides whether it uses geometric pattern matching (i.e. walls, pillars) by Hough transform. The proposed scheme shows reliable performance in both polygonal environments and non-polygonal environments even there exist many obstacles. Experimental results demonstrate the validity and feasibility of the proposed localization algorithm for the service robot to navigate in an office building, using the natural environmental characteristics.