When repairing worn components it is crucial to have detailed knowledge of the current object’s state. For this purpose a multi-sensor system was developed to measure objects in different scales and modalities. This work focuses on the 3-D measurement of worn turbine blades using a fringe projection system. The 3-D geometry of turbine blades is crucial for the overall performance and safety of an engine. Therefore it is not sufficient to rely on single fringe projection measurements for a functional evaluation. To obtain a 3-D model the blade has to be measured from multiple directions. Gathered data are combined to form the model. This process is called registration or stitching. To reduce uncertainties during the process markers can be applied on or near the measurement object. However, common methods using markers are insufficient in automatability and feature density and therefore are not applicable in this case. In this work a novel registration strategy based on projected random patterns is developed. Multiple projectors are placed around the object to illuminate its geometry. Keypoints are identified by capturing additional grayscale images and applying state-of-the art feature detection algorithms. Feature matching is performed on consecutive measurements. Matches are preprocessed and a random sample consensus approach is chosen to calculate the rigid body transformation. Multiple measurements of the turbine blade and other geometries have been successfully aligned using the proposed strategy. Beyond that the high density of features allows the alignment of measurements with different scales and resolutions.
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