Three-dimensional matching detection method for complex curved surface parts based on electric field properties

Abstract

The invention belongs to the technical field of detection of part processing errors, and discloses a method for detecting the size of a complex curved part in a three-dimensional matching manner based on electric-field properties. The method comprises the following steps: scanning and measuring a part to be detected so as to obtain measurement point clouds, and unifying a design model and a measurement model in the same coordinate system; defining two point clouds in an electrified body electric field, and giving a certain amount of charges; respectively calculating the electric potentials of the design model and a scanning model at the position of a probe charge by taking the origin of the coordinate as the probe charge; calculating electric field intensities of various points in a gravity centre neighbour field of two electron clouds, and acquiring a rigid body transformation matrix according to singular value decomposition; and updating and measuring the electron clouds from this, and circularly calculating the electric potential difference between the two electron clouds at the origin till an end condition is satisfied. By means of the invention, the problems of being sensitive to an initial position and easily fallen into local optimum in the existing matching technology can be effectively solved; and the method disclosed by the invention is particularly suitable for three-dimensional high-precision matching purposes of a large-scale depth image.

Description

technical field

[0001] The invention belongs to the technical field of component processing error detection, and more specifically relates to a three-dimensional matching detection method for complex curved surface component sizes based on electric field properties. Background technique

[0002] 3D point cloud matching technology has a wide range of applications in the fields of processing error detection, reverse engineering, pattern recognition, and medical image processing. In the application field of machining error detection, the machining error is usually determined by comparing the matching error between the measurement model and the design model. However, since the design and measurement point clouds to be matched are in the design coordinate system and the measurement coordinate system respectively, it is necessary to unify the coordinate systems of the two point clouds. In addition, since the initial positions of the two point clouds are unknown, and the models to...

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