针对传统点云配准方法效率低、误差大的问题，提出了一种沿竖直方向和水平方向分阶段变换的点云粗配准方法。首先对点云P、Q进行去中心化处理，使得两点云中心点重合；通过遍历距质心的距离寻找特征点，并将特征点旋转至y轴上，完成竖直方向上的对齐；然后在xOz平面内，再次通过遍历距质心的距离寻找特征点，将其绕y轴旋转，在水平方向完成对齐；最后配合迭代最近点精配准算法完成配准。所提方法不存在迭代计算，具有线性时间复杂度和常数空间复杂度。对所提方法与三种经典方法进行对比实验，采用了点数量不同以及尺度不同的三组点云。实验结果表明，对于不同的点云，所提方法具有较强的鲁棒性，配准时间在4 s左右，变化幅度较小，相对于三种传统方法，耗时减少了50%以上；同时所提方法的均方根误差控制在10^-8 mm左右，保持了较好的精度。

Due to the low efficiency and large error of traditional point cloud registration methods, this paper proposes a point cloud coarse registration method that is transformed in stages along with the vertical and horizontal directions. The proposed method first decentralizes the point clouds P and Q for coinciding the center points of two point clouds, then finds the feature point by traversing the distance from the center of mass, and rotates it to the y axis to complete the vertical alignment. The proposed method then finds the feature point again in the xOz plane by traversing the distance from the center of mass and rotates it around the y axis to align horizontally. Finally, to complete the registration, we use the iterative closest point fine registration algorithm. The proposed method has linear time complexity and constant space complexity, with no iterative computation. The proposed method is compared to three classical methods, and three groups of point clouds with varying numbers and scales are used. The experiment shows that the proposed method has high robustness for various point clouds. The proposed method has a registration time of about 4 s and a small change range; when compared to the three traditional methods, the time consumption is reduced by more than 50%. At the same time, the root mean square error of the proposed method is about 10^-8 mm, which maintains a good accuracy.