Vision-based detection and coordinate metrology of a spatially encoded multi-sphere artefact

New developments in vision algorithms prioritise identification and perception over accurate coordinate measurement due to the complex problem of resolving object form and pose from images. Consequently, many vision algorithms for coordinate measurements rely on known targets of primitive forms that are typically planar targets with coded patterns placed in the field of view of vision systems. Although planar targets are commonly used, they have some drawbacks, including calibration difficulties, limited viewing angles, and increased localisation uncertainties. While traditional tactile coordinate measurement systems (CMSs) adopt spherical targets as the de facto artefacts for calibration and 3D registration, the use of spheres in vision systems is limited to occasional performance verification tasks. Despite being simple to calibrate and not having orientationdependant limitations, sphere targets are infrequently used for vision-based in-situ coordinate metrology due to the lack of efficient multi-view vision algorithms for accurate sphere measurements. Here, we propose an edge-based vision measurement system that uses a multi-sphere artefact and new measurement models to extract sphere information and derive 3D coordinate measurements. Using a spatially encoded sphere identities embedded in the artefact, a sphere matching algorithm is developed to support pose determination and tracking. The proposed algorithms are evaluated for robustness, measurement quality and computational speed to assess their performance. At the range of 500 mm to 750 mm, sphere size errors of less than 25 mu m and sphere-to-sphere length errors of less than 100 mu m are achievable. In addition, the proposed algorithms are shown to improve robustness by up to a factor of four and boost computational speed.