Geometrical matching for mobile robot localization

Abstract

Deals with an absolute mobile robot self-localization algorithm given a two-dimensional cartography of the robot's environment. The proposed method only requires the goniometrical observations. We present an original solution to compute the robot's pose estimation by an interpretation tree (IT) search. Our approach introduces the subdivision of the robot's evolution field into rectangles. The matching between the observations and the visual landmarks is effected by judicious angular tests (heuristics for the IT). Moreover, we show that our matching method implicitly takes uncertainties and inaccuracies into account. Finally, some experimental results with real noisy omnidirectional images, provided by our omnidirectional vision sensor SYCLOP, are shown.