Quick point cloud registration method based on curved surface fitting coefficient features

Abstract

The invention discloses a quick point cloud registration method based on curved surface fitting coefficient features. Curvature mean differences of neighborhoods of different sizes are compared, points whose differences exceed a set threshold are selected to serve as key points, and key point candidate points are adaptively selected according to the differences. Multiple neighborhoods are selected at the key point for curved surface fitting, and a curved surface coefficient serves as a feature descriptor for the key point. Through comparing distances between key point feature descriptors, a key point pair with the smallest distance is selected to serve as an initial corresponding relation. A transformation matrix obtained through the initial corresponding relation is used for adjusting positions and orientations of the corresponding relation for basic coincidence, a distance threshold is set, and corresponding relations whose distances are larger than the threshold are removed. Then, a clustering method is used for enabling the corresponding relations to be uniformly distributed, a covariance matrix for the corresponding relations after optimization is calculated, and singular value decomposition is then carried out on the covariance matrix to obtain a final transformation matrix. The method of the invention has the advantages of quick registration, high precision and good anti-noise ability.

Description

technical field [0001] The invention belongs to the fields of structured light three-dimensional measurement and machine vision, and in particular relates to a point cloud rapid registration method based on surface fitting coefficient features. Background technique [0002] With the continuous upgrading of the computer industry, various computing devices are becoming more and more powerful, and it is possible to obtain object point clouds with low cost and high quality, thus promoting the application of point cloud registration technology in reverse engineering, game entertainment, medical images and industrial inspections. field development. Affected by factors such as the angle of view of the point cloud acquisition equipment and the shape of the measured object itself, it is difficult to obtain a complete point cloud of the object in a single measurement. Usually, this process is realized by the method of segmented measurement and splicing, that is, the use of point cloud...

AI Technical Summary

Problems solved by technology

The iterative closest point algorithm has been widely studied for its simplicity and high registration accuracy. However, the algorithm has high requirements for the coincidence degree of the initial position and point cloud, and the calculation is time-consuming. Although many scholars have improved it, the above problems still exist.

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