3D objects detection with Bayesian networks for vision-guided mobile robot navigation

Abstract

Recognition of environmental features, which is now the central research topic both in computer vision and mobile robot fields, is prerequisite for vision-guided mobile robot navigation. Perceptual organization is a powerful tool for object recognition through grouping low-level features into objects. In this paper, a perceptual organization algorithm based-on Bayesian networks is proposed to recognize 3D polyhedrons, e.g. compartments and doors, from 2D image in office environment. The algorithm makes full use of knowledge representation and probability inference characteristics of Bayesian networks, thus generating robust recognition results. Moreover, mobile robot active ability is developed to enhance recognition effects. Experimental results demonstrate the validity of the algorithm.