Abstract
The measurement of ultra-precision freeform surfaces commonly requires several datasets from different sensors to realize holistic measurements with high efficiency. The effectiveness of the technology heavily depends on the quality of the data registration and fusion in the measurement process. This paper presents methods and algorithms to address these issues. An intrinsic feature pattern is proposed to represent the geometry of the measured datasets so that the registration of the datasets in 3D space is casted as a feature pattern registration problem in a 2D plane. The accuracy of the overlapping area is further improved by developing a Gaussian process based data fusion method with full consideration of the associated uncertainties in the measured datasets. Experimental studies are undertaken to examine the effectiveness of the proposed method. The study should contribute to the high precision and efficient measurement of ultra-precision freeform surfaces on multi-sensor systems.
Highlights
Ultra-precision freeform surfaces with sub-micrometer-form accuracy and surface finish in the nanometric range are widely adopted in opto-mechatronic applications, due to their superior mechanical and optical properties in improving the performance of the products in both functionality and size reduction [1]
This paper presents a study of data registration and fusion for measuring ultra-precision freeform surfaces on multi-sensor instruments
Freeform data stitching and fusion technology provides a practical solution for multi-sensor measurement of freeform surfaces, and for enhancing the measuring ability of some high precision measurement instruments
Summary
Ultra-precision freeform surfaces with sub-micrometer-form accuracy and surface finish in the nanometric range are widely adopted in opto-mechatronic applications, due to their superior mechanical and optical properties in improving the performance of the products in both functionality and size reduction [1]. WITec GmbH [8] integrates confocal Raman microscopy, atomic force microscopy, and scanning near-field optical microscopy, and can perform relatively fast measurements of large-area samples These instruments integrate several sensors into a common system, and lack the much-required multi-sensor data fusion functionality and characterization that would achieve improved measurement results. There is still little research into data registration and fusion of ultra-precision freeform surfaces with sub-micrometer form accuracy. This paper presents a study of data registration and fusion for measuring ultra-precision freeform surfaces on multi-sensor instruments. The method performs the data registration by representing the geometry of each dataset based on intrinsic surface features which are invariant to coordinate transformation, and free from the implicit parameterization of the surface. Experimental studies are presented to demonstrate the validity of the proposed method
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