写出python代码实现：1.将点云数据划分为若干个体素；2.对每个体素内的点云数据进行高斯分布拟合；3.计算每个体素内的点云数据与目标点云数据的匹配度；4.根据匹配度进行点云配准。

以下是 Python 代码实现：

1. 将点云数据划分为若干个体素：

import numpy as np
from sklearn.cluster import MeanShift

def voxel_downsample(points, voxel_size):
    ms = MeanShift(bin_seeding=True, bandwidth=voxel_size)
    ms.fit(points)
    return ms.cluster_centers_

2. 对每个体素内的点云数据进行高斯分布拟合：

from scipy.stats import multivariate_normal

def fit_gaussian(points):
    mean = np.mean(points, axis=0)
    cov = np.cov(points.T)
    return multivariate_normal(mean=mean, cov=cov)

3. 计算每个体素内的点云数据与目标点云数据的匹配度：

def compute_matching_score(source_points, target_points, voxel_size):
    source_voxels = voxel_downsample(source_points, voxel_size)
    target_voxels = voxel_downsample(target_points, voxel_size)
    source_gaussians = [fit_gaussian(points) for points in source_voxels]
    target_gaussians = [fit_gaussian(points) for points in target_voxels]
    matching_score = 0
    for source_gaussian in source_gaussians:
        matching_score += max([source_gaussian.pdf(target_gaussian.mean) for target_gaussian in target_gaussians])
    return matching_score

4. 根据匹配度进行点云配准：

from scipy.optimize import minimize

def point_cloud_registration(source_points, target_points, voxel_size):
    def objective_function(x):
        transformed_points = np.dot(source_points, x[:3, :3].T) + x[:3, 3]
        return -compute_matching_score(transformed_points, target_points, voxel_size)
    x0 = np.eye(4)
    res = minimize(objective_function, x0, method='Powell')
    return res.x[:3, :]

以上是 Python 代码实现，希望能对你有所帮助。