Three-dimensional point cloud planeness computing method based on local optimization

Abstract

The invention belongs to the field of optical measurement, and specifically discloses a three-dimensional point cloud planeness computing method based on local optimization. The method comprises the following steps: selecting a measurement plane, obtaining the point cloud of the measurement plane, and performing grid division; performing planeness fitting on the point cloud in a single grid to obtain a fitting plane, and then computing the distance between each point in the grid to the fitting plane, performing descending order arrangement on absolute values of the distances, and finally eliminating the front points as noise points; and repeating the computing until all grids are traversed, and performing planeness fitting on the remaining point cloud to obtain the planeness of the measurement plane. According to the three-dimensional point cloud planeness computing method disclosed by the invention, in view of the characteristic that the noise points are generated easily in the optical measurement and the original data size is large, the grid division is performed on the point cloud of the measurement plane, the points with relatively large distances with the fitting plane in eachgrid are removed, and then the remaining points are fitted to obtain the planeness of the measurement plane, thereby avoiding the problem of uneven local distribution of the point cloud due to the noise points and making the computing be more accurate.

Description

technical field [0001] The invention belongs to the field of optical measurement, and more specifically relates to a method for calculating the flatness of a three-dimensional point cloud based on local optimization. Background technique [0002] In the fields of aviation, aerospace and nuclear power, large ring parts are relatively common parts, and these parts are usually large in diameter. For example, the diameter of the flange sealing surface of the main pump of nuclear power can reach 1.6m. After long-term service, such large ring parts will have defects such as scratches and peeling off on the surface. [0003] The traditional maintenance method is to use vernier calipers and micrometers to detect directly on the surface of parts. This detection method is not only inconvenient to operate, but also will cause harm to the human body in a nuclear power environment. At the same time, this kind of detection is more random, and can only detect local dimensions, and cannot ...

AI Technical Summary

Problems solved by technology

[0005] Aiming at the above defects or improvement needs of the prior art, the present invention provides a method for calculating the flatness of 3D point cloud based on local optimization, in which the error can be reduced and the calculation accuracy can be improved by meshing and eliminating noise points. , which can effectively solve the problem that optical measurement is prone to noise, so it is especially suitable for applications that use optical measurement data to calculate flatness

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