Locating and Acquiring an Object In a Robot Workspace Using Multiple Stereo Images

Abstract

A method is presented for locating an object which is introduced into a well modelled robot workspace. A sparse, accurate depth map of the object can be found by examining images of the workspace captured prior to introduction of the object, extracting visual edges from the old and new images, and suppressing old edges by a window-correlation technique. The resulting features can be matched within the stereo pair. This focus-of-attention strategy yields points on the object which are sufficiently numerous and accurate that the object may be acquired by a robot gripper.