METROLOGICAL CHARACTERIZATION OF A LASER-CAMERA 3D VISION SYSTEM THROUGH PERSPECTIVE-N-POINT POSE COMPUTATION AND MONTE CARLO SIMULATIONS

This study focuses on the metrological characterization of a 3D vision system consisting in the fusion of a CMOS camera sensor with a 2D laser scanner for contactless dimensional measurements. The purpose is to obtain an enhanced measurement information as a result of the combination of two different data sources. On one side, we can estimate the pose of the target measurand by solving the well-known Perspective-n-Point (PnP) problem from the calibrated camera. On the other side, the 2D laser scanner generates a discrete point cloud which describes the profile of the intercepted surface of the same target object. This solution allows to estimate the target's geometrical parameters through the application of fit-to-purpose algorithms that see the data acquired by the overall system as their input. The measurement uncertainty is evaluated by applying the Monte Carlo Method (MCM) to estimate the uncertainty deriving from the Probability Distribution Functions (PDF) of the input variables. Through a Design of Experiments (DOE) model the effects of different influence factors were evaluated.