A Reconstruction-Registration Integrated Data Fusion Method for Measurement of Multiscaled Complex Surfaces

The combined use of multiple measurement sensors is considered as a promising solution in surface metrology. Such hybrid instruments require sophisticated data fusion process to achieve overall better measurement results. This paper presents a reconstruction-registration integrated data fusion method to address the difficulty in modeling and fusing multiscaled complex data sets. The method decomposes the data sets into different scales by fitting a common surface via reconstruction and registration process so that the modeling and fusion process are also decomposed, and are only performed among the fitting and matching residuals of the data sets. The quality of the fused results is improved based on weighted mean method with the aid of Gaussian process model by taking into account the associated errors of each data set. The validity of the proposed method is verified through a series of comparison tests with existing methods by both computer simulation and actual measurement. It is shown that both enhanced registration accuracy and fusion quality are achieved by the proposed method with acceptable computation cost. The method should improve the metrological performance of the multisensor instruments in measuring complex surfaces.