Automated assessment of object coverage in smart, optical form measurement

As part of a project to develop a smart 3D form measurement system for quality control of complex 3D parts, we present early work in algorithmic assessment of coverage achieved on the surfaces of a part subjected to optical measurement from a single pose. The method performs an automated detection of pose by finding the best-matching position of the measured point cloud to a reference CAD model. The point cloud is then partitioned by associating measured points to individual CAD surfaces and coverage quality indicators, such as sampling density and covered area, are computed for each surface. The method is developed as part of a smart measurement solution that will provide real-time feedback to the measurement system on the success of the measurement acquisition and autonomously adapt the measurement strategy for complex 3D geometries in real-time to achieve an optimised measurement result. As proof of concept, a prototype implementation is presented that involves the combination of commercial measurement hardware and 3D modelling software components.