Range image segmentation and surface parameter extraction for 3-D object recognition of industrial parts

This paper addresses the problem of extracting certain types of surfaces for 3-dimensional object recognition in the presence of partial occlusion and noise using range information. We restrict consideration to man made industrial parts. In this case, we need only work with a small set of surface shapes such as planes, cylinders, and spheres since such shapes are common in industrial parts and it is only necessary to recognize and locate a sufficient set of surface regions to uniquely distinguish the part being observed from all others that might be present. We describe a way of extracting the parameters of these types of surfaces using normal analysis. The use of surface parameters then allows efficient and robust use of a matching process for object recognition and pose determination even though there are many undefined or occluded surface regions.