Low-Cost AR-Based Dimensional Metrology for Assembly

Abstract

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 comparison 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.