Sensing and recognition of rigid objects using structured light

Word directed toward the development of a vision system for bin picking of rigid 3D objects is reported. Any such system must have components for sensing, feature extraction, modeling, and matching. A structured light system which attempts to deliver a rich 2/sup 1///sub 2/D representation of the scene is described. Surface patches are evident as connected sets of stripes whose 3D coordinates are computed by means of triangulation and constraint propagation. Object edges are detected by the intersection of surface patches or by backprojecting image edges to intersect with the patches. Two matching paradigms are given for drawing correspondence between structures in the scene representation and structures in models. Three major contributions are reported: a method for sensing object surface patches without having to solve uniquely for stripe labels; the use of both an intensity image and a striped image, allowing scenes to be represented by detected edges along with 3D surface patches; and a pose-clustering algorithm, a uniform technique to accumulate matching evidence for recognition while averaging out substantial errors of pose.<>