Robust robot localization and map building using a global scan matching method

This paper presents a global localization method in which the robot can relocalize itself when getting lost due to large slippage and kidnapping. In the proposed method, the robot generates pose hypotheses using a global scan matching method. The robot selects one hypothesis as the correct pose by filtering out false hypotheses using a multiple hypothesis tracking scheme. While the robot is tracking a single pose, the map is updated based on the SLAM framework. Experimental results show that the robot successfully localized itself robustly to disturbances including noises and kidnapping.