No abstract

In this work, we present a portable 3D vision coordinate measuring machine (PCMM) for short range-real time photogrammetry. The PCMM performs 3D measurements of points using a single camera in combination with a hand tool and a computer. The hand tool has infrared LEDs serving as photogrammetric targets. The positions of these targets were pre-calibrated with an optical coordinate-measuring machine defining a local coordinate system on the hand tool. The camera has an infrared filter to exclude all ambient light but infrared targets. Positions of the imaged infrared targets are converted to 3D coordinates using pixel positions and precalibrated positions of the targets. Also, we present a set of criteria for selecting the infrared LEDs and the camera filter, a camera calibration method, a tracking and POSE algorithms, and a 3D coordinate error correction for the PCMM. The correction is performed using the PCMM as a range meter, which implies comparing the 3D coordinate points of the PCMM with a coordinate measuring machine, and then generating a look up table (LUT) for correction. The global error of the PCMM was evaluated under ASME B89.4.22-2004. Sphere and single point errors were around 1 mm, volumetric error were under 3 mm.

The goal of this work is to offer a comparative of measurement error for different computer vision techniques for 3D reconstruction and allow a metrological discrimination based on our evaluation results. The present work implements four 3D reconstruction techniques: passive stereoscopy, active stereoscopy, shape from contour and fringe profilometry to find the measurement error and its uncertainty using different gauges. We measured several dimensional and geometric known standards. We compared the results for the techniques, average errors, standard deviations, and uncertainties obtaining a guide to identify the tolerances that each technique can achieve and choose the best.