Registration of Point Clouds for 3D Shape Inspection

Point cloud registration and sensor calibration are two critical technical issues concerning robot-mounted, area sensor systems. Iterative closest point (ICP)-based algorithms developed in the past are commonly used for point cloud registration. However, due to its least squared fitting nature, registration quality depends on how closely the measured part matches its nominal definition, typical in the form of a CAD model in modern times. To eliminate the dependency of registration quality on part closeness to the CAD model, we present, in this paper, a more robust approach based in a series of coordinate transformations. Geometric features and surface gradients are accounted for to improve the registration performance. To achieve robot/sensor hand-eye calibration, an ICP-based method is used. The reason is that this calibration step typically utilizes standard parts or gauges machined for the purpose of calibration, as such they are known shapes that match their CAD models with much tighter tolerances. This offers us an unique opportunity to apply an ICPbased tool to find the transformation matrix from the robot end effector to an area sensor mounted onto it. The discussed method was successfully implemented and tested in a feedback-based, robot-mounted area sensor system developed for manufacturing quality control of 3D freeform surfaces