Automated calibration of multi-sensor optical shape measurement system

A multi-sensor optical shape measurement system (SMS) based on the fringe projection method and temporal phase unwrapping has recently been commercialised as a result of its easy implementation, computer control using a spatial light modulator, and fast full-field measurement. The main advantage of a multi-sensor SMS is the ability to make measurements for 360° coverage without the requirement for mounting the measured component on translation and/or rotation stages. However, for greater acceptance in industry, issues relating to a user-friendly calibration of the multi-sensor SMS in an industrial environment for presentation of the measured data in a single coordinate system need to be addressed. The calibration of multi-sensor SMSs typically requires a calibration artefact, which consequently leads to significant user input for the processing of calibration data, in order to obtain the respective sensor's optimal imaging geometry parameters. The imaging geometry parameters provide a mapping from the acquired shape data to real world Cartesian coordinates. However, the process of obtaining optimal sensor imaging geometry parameters (which involves a nonlinear numerical optimization process known as bundle adjustment), requires labelling regions within each point cloud as belonging to known features of the calibration artefact. This thesis describes an automated calibration procedure which ensures that calibration data is processed through automated feature detection of the calibration artefact, artefact pose estimation, automated control point selection, and finally bundle adjustment itself. [Continues.]