3-D processing of range image data for vision applications in manufacturing

Three-dimensional data present pertinent information about geometrical features of an object. It has been a classical approach that image data acquired by range sensors are processed as traditional 2.5-dimensional (2.5-D) images. Range data have rich information that needs some special treatment in order to fully understand and utilize them. In this report, two case studies are presented to investigate the 3-D aspects of range data and applied them to solve practical problems in manufacturing environment. The first case is the classical 'pick and place' problem where the range data were taken on the holding rack of car doors and there is a need to identify multiple holding points accurately, with the rack almost taking entire field of view and traditional image processing approach invalid. The second case is the range data correction and sub-pixel patch with large field of view. The applications are in automated quality assessment. Two approaches are from different vision solutions with one objective that is to process range image data with 3-D representation instead of that of traditional 2.5-D.