Robust stereo-vision based 3D modelling of real-world objects for assistive robotic applications

This paper addresses the problem of recognizing and reconstructing real-world objects in cluttered environments to enable service robot to grasp the objects and manipulate them. A novel approach to combine disparity segmentation method with the closed-loop color region based segmentation is presented. Disparity map segmentation leads to definition of object region of interest (ROI) enabling autonomous functioning of robot system in cluttered environments. Closed-loop object region segmentation is robust against variable illumination providing reliable operation of robot system in different lighting conditions. Starting from the segmented object in both stereo images, the 3D contour of the object is generated and the object geometry is recovered from it. The proposed method needs no a-priori knowledge about the object color, its appearance or geometry. The performance of the presented method has been tested within the working scenario of the assistive robotic system FRIEND.