Abstract
As multisensor measurement technology is rapidly applied in industrial production, one key issue is the data fusion procedure by combining several datasets from multiple sensors to obtain the overall geometric measurement. In this paper, a multisensor data fusion method based on a Gaussian process model is proposed for complex surface measurements. A robust surface registration method based on the adaptive distance function is firstly used to unify the coordinate systems of different measurement datasets. By introducing an adjustment model, the residuals between several independent datasets from different sensors are then approximated to construct a Gaussian process model-based data fusion system. The proposed method is verified through both simulation verification and actual experiments, indicating that the proposed method can fuse multisensor measurement datasets with better fusion accuracy and faster computational efficiency compared to the existing method.
Highlights
With the development of advanced manufacturing technology, many complex surfaces such as freeform surfaces and structured surfaces can be machined with high precision [1,2]
Their measurement accuracy is much lower than the coordinate measuring machines (CMMs)
This paper proposes a data fusion method for multisensor combination measurement of complex surfaces
Summary
With the development of advanced manufacturing technology, many complex surfaces such as freeform surfaces and structured surfaces can be machined with high precision [1,2] These surfaces require a complete 3D characterization, with a large measurement range, high resolution and precision, and high measurement efficiency, which poses a challenge to current measurement technology [3,4]. Non-contact measuring devices, e.g., structured light scanners, could generate dense measurement data efficiently, which can capture the overall shape of the product well. Their measurement accuracy is much lower than the CMMs. the combination of multiple measurement sensors could be a better solution to solve complex measurement tasks, which maximizes the advantages of individual measurement sensors [8,9,10].
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