VDI/VDE 2634–1 performance evaluation tests and systematic errors in passive stereo vision systems

Abstract

In this paper, we explore the sensitivity of the length tests described in the VDI/VDE 2634-1 guideline to systematic errors in a passive stereo vision system. The results reported here are based on a stereo vision system set up on the table of a Cartesian coordinate measuring machine (CMM) and simulations that match the experimental conditions. The stereo vision system is calibrated using a checkerboard pattern resulting in estimates for 26 camera model parameters. Errors in these model parameters cause errors in the triangulated 3D points, thus resulting in length errors evaluated from those points. In the first part of this study, we consider a grid of points in the measurement volume of the stereo vision system and simulate the effect of induced systematic errors in each model parameter to determine the resulting error in the length for all lines (i.e., between all pairs of points) in that grid. We identify the lines that provide the maximum error in the length for each model parameter. Thus, we identify a collection of lines, which we refer to as model-based lines, that provide maximum sensitivity to the model parameters. Also, through simulations, we assess whether the lines defined in the VDI/VDE guideline are sufficient to detect the systematic errors in the stereo vision system. In the second part of this study, we evaluate the performance of the stereo vision system, i.e., assess the effect of errors in the model parameters, by using the CMM as the reference. For this purpose, the CMM is commanded to move to the set of points in the rectangular grid previously considered in the simulations with the stereo vision system tracking a small checkerboard target installed on the ram of the CMM. We compare the VDI/VDE and model-based lines in terms of the experimentally obtained length errors. The studies show that selection of lines based on the model (determined through simulations and verified experimentally) is advantageous because of the superior ability in detecting systematic errors in stereo vision systems.