Multi-View Point Clouds Registration Method Based on Overlap-Area Features and Local Distance Constraints for the Optical Measurement of Blade Profiles

TL;DR: Wang et al. as discussed by the authors proposed a fine registration algorithm to realize accurate measurement of blade profiles, which mainly comprises two novel constraint mechanisms: overlap-area features and local distance constraints, which are constructed and integrated based on the Hadamard Product of matrices.

Abstract: The accurate and efficient measurement of blade profiles plays an important role in blade processing and quality monitoring. Currently, increasing attention is being paid to the optical measurement method for blade profiles due to its easy accessibility, high efficiency, and flexibility. However, as a key issue of blade profile measurement, the registration of multiview measured data still relies on the geometric accuracy and motion stability of the measurement system. Besides, multiple factors, including the various density and insufficient overlaps of multiview point cloud data, make the registration a challenging problem. To overcome these problems and realize accurate measurement of blade profiles, a fine registration algorithm is proposed in this article, which mainly comprises two novel constraint mechanisms: overlap-area features and local distance constraints. Two constraint matrices based on the features and distance information are constructed and integrated based on the Hadamard Product of matrices. In this way, the underlying corresponding mapping between multiview point clouds can be effectively recovered and the registration accuracy can be improved. The results of measurement experiment on three typical blades demonstrate the accuracy and feasibility of the proposed method.