Abstract: Aiming at the disadvantages of error correspondence points and low precision in point cloud registration, this paper proposes a point cloud registration algorithm based on multi-core parallel and dynamic threshold. This algorithm adopts the improved SAC-IA to complete rough registration for point cloud, and uses mainly OpenMP to realize the parallel extraction of the normal vector of point cloud query points, FPFH and parallel search of the correspondence points, so that the speed of the entire registration algorithm can be maintained or even improved. This paper uses the improved ICP algorithm to achieve registration in the point cloud fine registration. The improvement points focus on the culling of the error correspondence points and the dynamic determination of threshold. The center of gravity of registration points is used as the reference points. According to the dynamic threshold, the point pairs distance constraint is used to remove the error correspondence points. The experimental results show that the registration speed of this algorithm is improved when the registration accuracy is improved.

Key words:  point cloud registration, open multi-processing, center of gravity of registration points constraint, dynamic threshold, sample consensus initial aligment, iterative closest point