基于区域分割的低覆盖点云配准算法

针对低覆盖点云配准的时间复杂度高、收敛速度缓慢以及对应点匹配易错等问题，提出一种基于区域分割的点云配准算法。首先，利用体积积分不变量计算点云上点的凹凸性，并提取凹凸特征点集；然后，采用基于混合流形谱聚类的分割算法对特征点集进行区域分割，并采用基于奇异值分解（SVD）的迭代最近点（ICP）算法对区域进行配准，从而实现点云的精确配准。实验结果表明，所提算法通过区域分割可以大幅提高点云区域的覆盖率，并且无需迭代即可计算刚体变换的最佳旋转矩阵，其配准精度比已有算法提高了10%以上，配准时间降低了20%以上。因此，所提算法是一种精度高、速度快的低覆盖点云配准算法。

Low coverage point cloud registration algorithm based on region segmentation

Aiming at the problems of high time complexity, slow convergence speed and error-prone matching of low coverage point cloud registration, a point cloud registration algorithm based on region segmentation was proposed. Firstly, the volume integral invariant was used to calculate the concavity and convexity of points on the point cloud, and then the concavity and convexity feature point sets were extracted. Secondly, the regions of the feature points were partitioned by the segmentation algorithm based on the mixed manifold spectral clustering, and the regions were registered by the Iterative Closest Point (ICP) algorithm based on Singular Value Decomposition (SVD), so that the accurate registration of point clouds could be achieved. The experimental results show that the proposed algorithm can greatly improve the coverage of point clouds by region segmentation, and the optimal rotation matrix of rigid body transformation can be calculated without iteration. The algorithm has the registration accuracy increased by more than 10% and the registration time reduced by more than 20%. Therefore, the proposed algorithm can achieve fast and accurate registration of point clouds with low coverage.