A framework of data fusion algorithm for precision measurement of multiscale surfaces

Multiscale surfaces enriched with different scales of features are becoming widely used in many fields such as MEMS and optics industries. Due to the complexity of the multiple scale geometries, a single sensor or a single measurement can hardly obtain the holistic information of these surfaces. Multiple measurement method can solve this problem while it will introduce a lot of challenges for multiscale data fusion for the measurement results. This paper presents a framework of a data fusion algorithm for precision measurement of multiscale surfaces. The method makes use of iterative closest point based method to precisely register the datasets obtained in multiple measurements, and a Gaussian zero-order regression filter is used to separate the geometric features in different scales. Hence, the datasets are fused based on an edge intensity data fusion algorithm within the same wavelength. Finally, the fused datasets of different wavelengths were merged and replaced to the corresponding area in the large scale measurement to form a new surface with holistic multiscale information. The effectiveness of the proposed method has been verified on a v-grooved surface through a series of simulation experiments.