Visual Servoing on the Generalized Voronoi Diagram Using an Omnidirectional Camera

Abstract

The Generalized Voronoi Diagram (GVD) is a powerful environment representation, since, among other reasons, it defines a set of paths at maximal distance from the obstacles. Many works implicitly use this property to define safe navigation strategies for a mobile robot, but, in practice, only a few explicitly extract in real time the GVD from its perception to induce motion. This article addresses this challenge for a mobile robot solely equipped with an omnidirectional camera, and proposes an autonomous navigation strategy in unknown indoor and/or outdoor environments. The problem is formulated as a visual servoing task, and uses an anisotropic skeletonization algorithm to identify the projection in the image of the local GVD, while suppressing unsafe navigable paths thanks to a context-defined pruning parameter. Unlike rangefinder based system, the use of an omnidirectional camera allows to efficiently deal with outdoor scenes, since the navigable space is extracted using photometric cues. The servoing is then performed using a local linear approximation of the GVD, defined by a conic extracted in real time in the omnidirectional image. To assert the relevancy and efficiency of the proposed approach, extended experimental results are presented, both on indoor and outdoor scenarios.