In coordinate metrology, data fusion is either for data improvement or data collection. The reliability and quality of the final measurement result depend not only on the single measurement quality characteristics, but also on the data fusion procedure realization, including the registration process. Registration includes coordinate transformation that leads to the measurement uncertainty transformation. Data fusion requires many operations and represents a complicated multi-stage model. The structure of measurement uncertainty in a point cloud has to be taken into account. Since analytical derivations are complicated in this case, preferably the Monte-Carlo method is used for the uncertainty evaluation. The improvement of the quality of the measurement result by data fusion is achieved by redundancy and appropriate weighting techniques. An example of the form measurement of rotationally symmetric workpieces was discussed. The multiview measurement strategy requires data fusion for data collection. The measurement uncertainty reduction is achieved by the complementary data source (calibration data of support) and the stitching procedure improvement.
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