Calibration and uncertainty analysis of a combined tracking-based vision measurement system using Monte Carlo simulation

Abstract

Abstract A global stereovision system combined with a local vision sensor is an effective approach to large-scale object measurement. However, obtaining the error distribution of such an approach remains a key research challenge in vision metrological applications. This paper investigates the calibration and the reconstruction uncertainty estimation method of the combined vision system. The measurement principle and the calibration method of the transformation matrix between the tracking-based measurement coordinate systems are presented. Furthermore, Monte Carlo simulation is utilized to determine the reconstruction uncertainty based on the theoretical measurement model and the experiment-based input uncertainty. The overall measurement uncertainty of the combined system is found to be 34.5% higher than that of the global vision system, which is more sensitive to the input pixel uncertainty than the local vision system. However, the combined vision system can achieve comparable measurement results within its larger working volume. This work contributes to a better understanding of the measurement uncertainty in combined tracking-based vision systems, as well as providing a few useful practice guidelines for using such a visual system.