针对不同尺度点云配准的精度和效率问题，提出一种基于人工蜂群优化的异尺度点云配准算法。引入尺度缩放因子，与三维旋转、平移参数共同作为配准过程中的待求变量，使用人工蜂群优化方法进行优化求解。同时，基于归一化尺度参数改进了欧氏距离目标函数，以消除优化求解中尺度缩放因子引起的误差，从而有效提高配准算法的稳定性。与当前几组典型方法进行对比，所提算法对不同模型配准的精度和效率均有提高。实验结果表明，所提算法充分利用了人工蜂群优化方法优异的全局优化能力，可以有效实现对异尺度点云的高精度、快速配准。

This study proposed a differently scaled point cloud registration algorithm based on artificial bee colony optimization that can improve the accuracy and efficiency of differently scaled point cloud registration. The scale scaling factor, together with the three-dimensional rotation and translation parameters, was introduced as the variables to be solved in the registration process, and the artificial bee colony optimization method was used to optimize the solution. Furthermore, the proposed algorithm improved the Euclidean distance objective function based on the normalized scale factor, which eliminated the errors caused by optimizing the scale scaling factor to effectively improve the stability of the registration algorithm. Compared to currently employed methods, the proposed algorithm improves the accuracy and efficiency in different model registrations. The experimental results demonstrate that the proposed algorithm utilizes the excellent global optimization ability of the artificial bee colony optimization method and can therefore effectively realize the high-precision and fast registration for differently scaled point clouds.