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 ...

We propose to study the relationship between the hierachical Hough transform and classical algorithms for partitioning curves into segments. We show that the set of segments found by classical algorithms is a subset of the segments found by the hierarchical Hough transform. This new relationship allows to have a better understanding of the behaviour of some parameters of the hierarchical Hough transform. Keywords: Hough transform; Pyramid; Line/segment extraction; Polygonal curve approximation 1.