Automated Robot-based Large-Scale 3D Surface Imaging

Abstract

Abstract This work develops a robot-based automated 3D imaging system for large-scale surface measurement at high resolution. The system has the advantages of allowing 1) high-resolution 3D surface imaging based on photometric stereo, and 2) automatic stitching of multiple images collected by a robot for large-scale surface measurement. We developed a dome-shaped image acquisition system with 16 individually controlled lights, mounted on a robot (Kuka iiwa lbr). A photometric stereo with a lighting selection mechanism is used for the reconstruction of local surface regions. To allow image stitching for large-scale surface measurement, one challenge arises from the robot arm’s limited encoder precision and accuracy, which is about ±150 µm for its repeatability and even lower for its nominal accuracy. This is unsuitable for the applications of surface metrology or inspection. To compensate the errors introduced by the chained robot arm’s encoders, for image stitching, we experimented with two feature descriptors extracted from the normal and the curvature space respectively, and performed comparative studies with standard feature descriptors from the standard grey-scale intensity space. The normal-based feature descriptor demonstrated advantages of illumination invariance while the curvature-based feature descriptor demonstrated clear advantages of rotation invariance, and feasibility of aligning multiple images with high accuracy.