A sensor data fusion-based locating method for large-scale metrology

Abstract

The measurement of geometric and dimensional variations in the context of large-sized products is a complex operation. One of the most efficient ways to identify deviations is by comparing the nominal object with a digitalisation of the real object through a reverse engineering process. The accurate digitalisation of large geometric models usually requires multiple acquisitions from different acquiring locations; the acquired point clouds must then be correctly aligned in the 3D digital environment. The identification of the exact scanning location is crucial to correctly realign point clouds and generate an accurate 3D CAD model.

To achieve this, an acquisition method based on the use of a handling device is proposed that enhances reverse engineering scanning systems and is able to self-locate. The present paper tackles the device’s locating problem by using sensor data fusion based on a Kalman filter. The method was first simulated in a MatLAB environment; a prototype was then designed and developed using low-cost hardware. Tests on the sensor data fusion have shown a locating accuracy better than that of each individual sensor. Despite the low-cost hardware, the results are encouraging and open to future improvements