Real-time self-localization in unknown indoor environment using a panorama laser range finder

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Abstract
This paper deals with self-localization of a mobile robot on the condition that no a-priori knowledge about the envi- ronment is available. The applied method features to be ac- curate, robust, independent of any artificial landmarks and feasible with such a moderate computational effort that all necessary tasks can be executed in real-time on a standard PC. The perception system used is a panorama laser range finder (PLRF) which takes scans of its present environment. A modified Dynamic Programming (DP) algorithm provides pattern matching and pattern recognition on the prepro- cessed panorama scans and thereby renders a qualitative fusion of the sensory data. For an exact quantitative esti- mate of the robot's current position, a robust localization module is employed. The knowledge gained about the envi- ronment along that way is stored in a self-growing, graph based map which combines geometrical information and topological restrictions. Preliminary experiments in a com- mon office environment proved the reliability and efficiency of the system.

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