Two-point point cloud registration method under known scanning point positions

Abstract

The invention discloses a two-point point cloud registration method under known scanning point positions. The two-point point cloud registration method comprises the implementation steps of: (1) acquiring point cloud data of an object to be scanned; (2) preprocessing the point cloud data; (3) calculating a number of iterations; (4) judging whether the current number of iterations reaches a threshold value, if so, executing a step (12), otherwise, executing a step (5); (5) calculating a first rotation matrix; (6) calculating a second rotation matrix; (7) calculating a rotation matrix of point cloud to be registered; (8) calculating a number of current correct corresponding points; (9) calculating a current cost assessed value; (10) updating parameters; (11) determining whether an iterationtermination condition is reached, if so, executing the step (12), otherwise, executing the step (4); (12) and registering the point cloud to be registered of two stations. According to the two-point point cloud registration method, the number of required samples is only 2, the probability of mismatching during the sampling process is reduced, the reliability of the registration result is high, andthe precision of the registration result is high.

Description

technical field [0001] The invention belongs to the technical field of image processing, and further relates to a two-point point cloud registration method under known scanning point positions in the technical field of computer vision. The present invention can be applied to specific application scenarios such as 3D reconstruction, aiming at the adjacent point cloud whose spatial position of the scanning point is known, realizes the simultaneous rotation and registration of the three-dimensional data of two stations, thereby constructing a complete three-dimensional point cloud model. Background technique [0002] Point cloud registration is to convert multi-site clouds scanned from multiple perspectives into a unified coordinate system to obtain a complete 3D model. Random sampling consensus algorithm RANSAC is a method to accurately fit a mathematical model from samples in point cloud registration. Chen first applied the idea of ​​RANSAC to point cloud registration, and t...

AI Technical Summary

Problems solved by technology

And this method obtains the center of the spherical target by means of spherical fitting, so the disadvantage of this method is that there is a certain error, and the accuracy of the registration result cannot be guaranteed

Because the method requires more sample points for sampling, the more complex the constraint relationship between sample points, the more errors there are, and the higher the probability of false matching in the sampling process, so the method has shortcomings. Yes, the three-point RANSAC used in the sampling process has a high probability of obtaining mismatching points, and there are many errors, the registration effect is difficult to achieve the ideal goal, and the initial registration accuracy is poor

Images