Acquiring models of rectangular 3D objects for robot maps

Abstract

State-of-the-art robot mapping approaches are capable of acquiring impressively accurate 2D and 3D models of their environments. To the best of our knowledge few of them can acquire models of task-relevant objects. In this paper, we introduce a novel method for acquiring models of task-relevant objects from stereo images. The proposed algorithm applies methods from projective geometry and works for rectangular objects, which are, in office- and museum-like environments, the most commonly found subclass of geometric objects. The method is shown to work accurately and for a wide range of viewing angles and distances.