Accuracy Analysis of Alignment Methods based on Reference Features for Robot-Based Optical Inspection Systems

Abstract In recent years, optical 3D sensors have reached a high level of accuracy suitable for many applications involved in the geometric quality assurance in modern production sites. In order to circumvent the tradeoff between the size of the field of view and the accuracy, a fusion of multiple point clouds is often performed by means of data-driven registration algorithms, such as the well-known ICP. These methods require a coarse alignment of point clouds, which also influences the accuracy and robustness of the actual 3D matching process. In the context of robot-based inspection systems, additional reference features are often applied. The references are well detectable and provide key points. This gives rise to the question of whether or not better initial alignments are obtainable from the measurement data, in contrast to the alignment obtained by the robot kinematic. Therefore, we investigated the accuracy of calculated transformations for translational and rotational modifications based on measured data. The results indicate that for mainly translational relative transformations high accuracies are obtainable. An improvement of the coarse alignment for subsequent fine registration processes promises a contribution towards having more accurate and robust alignments of point clouds, and therefore benefits geometric quality assurance applications in manufacturing industries.