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

This paper deals with self-localization of a mobile robot on the condition that no a-priori knowledge about the environment is available. The applied method features to be accurate, 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 preprocessed panorama scans and thereby renders a qualitative fusion of the sensory data. For an exact quantitative estimate of the robot’s current position, a robust localization module is employed. The knowledge gained about the environment along that way is stored in a self-growing, graph based map which combines geometrical information and topological restrictions. Preliminary experiments in a common office environment proved the reliability and efficiency