Multi-field point cloud registration method for blades based on overlapping features and local distance constraints

Abstract

The invention discloses a blade multi-field point cloud registration method based on overlapping features and local distance constraints, including step 100: obtaining multi-field point cloud data of a blade, and solving point clouds between two adjacent fields of view overlap area of ​​data and construct data sets X and Y; step 200: establish overlapping feature constraint matrix F MN ; Step 300: Establish a local distance constraint matrix D MN ; Step 400: Based on the Hadamard product of the matrix, constrain the matrix F by overlapping features MN and the local distance constraint matrix D MN Construct the membership probability matrix P between dataset X and dataset Y MN , P MN =F MN D. MN , is the Hadamard product; Step 500: Calculate the rotation matrix and translation matrix between the data sets X and Y based on the EM algorithm, and splicing the complete blade cross-sectional profile through the calculated rotation matrix and translation matrix. The invention proposes a blade multi-field point cloud data registration method that does not depend on the motion accuracy of the detection system, so as to achieve the purpose of improving the detection accuracy and reliability of the blade.

Description

technical field [0001] The invention belongs to the field of blade detection, and in particular relates to a blade multi-field point cloud registration method based on overlapping features and local distance constraints. Background technique [0002] The blade is the core component of an aero-engine, and its surface accuracy will directly affect the energy conversion efficiency and working reliability of the whole machine. Therefore, accurate detection of the blade profile has always been a key link in the blade manufacturing process. In recent years, due to its good efficiency and flexibility, the blade detection method based on optical measurement has received extensive attention. [0003] At present, the registration accuracy of multi-field point clouds mainly depends on the geometric accuracy and operational reliability of the built detection system. The Chinese invention patent with the patent number of 202011134900.7 discloses a method of calibrating the center of ro...

AI Technical Summary

Problems solved by technology

The Chinese invention patent with the patent number of 202011134900.7 discloses a method of calibrating the center of rotation based on the local leading edge curve characteristics of the blade. This method directly uses the motion parameters of the detection platform and can effectively complete the registration of multi-field point cloud data, but The registration result is easily affected by many factors such as the geometric accuracy and motion reliability of the detection system. After all, there will be certain mechanical errors in the motion parameters of the detection platform, resulting in poor reliability of the final blade detection; figure 1 In a and b, two adjacent splicing results are intercepted from the result of registering multi-field point cloud data using this method. It can be seen that the overlapping areas between the adjacent fields of view are not completely consistent, and the density is uneven ( figure 1 Point cloud data within the dotted line in a) problem

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