基于多核并行和动态阈值的点云配准算法

针对点云配准中存在错误匹配点对、精度不高等问题，提出一种基于多核并行和动态阈值的点云配准算法。该算法采用改进的SAC-IA算法进行点云粗配准，利用OpenMP实现点云查询点的法向量、FPFH等特征的并行加速提取以及对应点对的并行查找，从而使整个配准算法的速度得到保持甚至提升。在点云精配准阶段，使用改进的ICP算法进行精配准，改进点着眼于错误对应点对的剔除及其阈值的动态确定，即以配准点重心作为参照点，按照动态阈值，使用点对距离约束剔除错误对应点对。实验结果表明，本文算法在提升配准精度的情况下，配准速度也得到了提升。

A Point Cloud Registration Algorithm Based on Multi-core Parallel and Dynamic Threshold

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.