Uncertainty evaluation of distributed Large-Scale-Metrology systems by a Monte Carlo approach

Distributed systems for Large-Scale-Metrology applications generally include a set of angular and/or distance sensors, distributed around the measurement volume, and some targets to be localized, in contact with the measured object’s surface. For these systems, estimating the uncertainty in target localization is far from trivial, as it may be affected by several factors: uncertainty in sensor calibration and angular/distance measurements, relative position between targets and sensors, etc. This paper proposes a novel approach based on the combined use of the Multivariate Law of Propagation of Uncertainty and Monte Carlo method. Preliminary results and experimental tests are presented and discussed.