The goal of this study was to create and demonstrate a system to perform fast and inexpensive quality dimensional inspection for industrial assembly line applications with submillimeter uncertainty. Our focus is on the positional errors of the assembled pieces on a larger part as it is assembled. This is achieved by using an open-source photogrammetry architecture to gather a point cloud data of an assembled part and then comparing this to a computer-aided design (CAD) model. The point cloud

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... parison to the CAD model is used to quantify errors in position using the iterative closest point (ICP) algorithm. Augmented reality is utilized to view the errors in a live-video feed and effectively display said errors. The initial demonstration showed an assembled position error of 9 mm ± 0.4 mm for a 40-mm high post.