将一组3D点映射到具有最小距离总和的另一组

import numpy as np
from scipy.linalg import svd

def best_mapping(points1, points2):
    # 计算两组点的均值
    mean1 = np.mean(points1, axis=0)
    mean2 = np.mean(points2, axis=0)

    # 计算每组点的协方差矩阵
    cov1 = np.cov(points1.T)
    cov2 = np.cov(points2.T)

    # 计算协方差矩阵的特征值和特征向量
    U1, s1, Vt1 = svd(cov1)
    U2, s2, Vt2 = svd(cov2)

    # 通过特征向量和特征值计算最佳映射矩阵
    R = np.dot(Vt1, Vt2.T)
    s = np.diag(s2)
    t = np.dot(mean2.T - np.dot(R, mean1.T), s)

    # 使用最佳映射矩阵将一组点映射到另一组点
    return np.dot(points1, R) + t