###### Abstract
[12] put forward an improved ICP point cloud registration algorithm based on the genetic algorithm, which is provided the ability of optimal global search for rough registration, registers point cloud data with the 3D model, and provides a good iterative initial value for subsequent accurate registration. Experimental results showed that this method could effectively improve the accuracy and rate of point cloud registration. [PERSON] et al. [13] fulfilled point cloud registration using an iterative closest point algorithm based on hierarchical particle swarm optimization to improve the performance of 3D point cloud registration, then obtained the feature points precisely expressing the point cloud structure by jointly searching for the optimal particles using the curvature of the point cloud as the fitness value, and finally, performed the registration of the feature points using the iterative closest point algorithm and achieved better registration efficiency. Based on the ICP algorithm, [PERSON] [14] proposed an automatic registration algorithm that carries out automatic rough registration in view of boundary characteristics of the point cloud, followed by automatic fine registration through an improved ICP algorithm. Experimental results proved that this method possesses a sub-millimeter improvement versus the classical algorithm. The ICP algorithm presupposes that the coarse registration process establishes the correct initial registration point pairs and achieves accurate registration of point clouds. The quality of rough registration directly affects the efficiency and accuracy of fine registration. Until now, the coarse registration methods mainly include Gaussian-likelihood estimation factor analysis proposed by [PERSON] et al. [15], 3D-NDT-based point cloud registration by [PERSON] et al. [16], the feature-based method by [PERSON] et al. [17], and RANSAC algorithm by [PERSON] et al. [18]. [PERSON] et al. [19] combined the descriptor algorithm for internal morphology with the histogram algorithm with fast point characteristics and then improved the RANSAC algorithm for the initial registration of point cloud by means of pre-estimation and 3D grid segmentation to obtain better results. [PERSON] et al. [20] proposed a color-based point cloud alignment algorithm that extracts the hue components based on the color information of the point cloud and makes the hue distribution of the tangent plane continuous. The error function consists of the color of the point cloud to be aligned and the geometric error. The error function is optimized using the Gauss-Newton method, and the algorithm has good robustness under different lighting conditions. [PERSON] et al. [21] proposed a new algorithm to improve numerical stability. Unlike previous algorithms that rely heavily on point-to-plane distances, their algorithm constructs a cost function based on two different projection distances, and experiments show that the algorithm has high accuracy for color point cloud alignment. [PERSON] et al. [22] proposed a pairwise non-rigid alignment algorithm for 3D point clouds, which generates a correspondence between two deformed point clouds based on the isometric deformation property invariant, and subsequently uses a random sample consensus (RANSAC) algorithm for cloud point feature extraction to better prepare for the optimization of alignment. [PERSON] et al. [23] proposed a spatially decomposed and optimized RANSAC algorithm for indoor plane detection based on the phenomenon that the traditional RANSAC algorithm is prone to feature extraction errors for occluded and missing point cloud data. The method uses a weighted PCA method to estimate the normal vector of the point cloud and then employs angular clustering to partition the indoor space, followed by an optimized RANSAC method to calculate the alignment parameters from the obtained point cloud data. [PERSON] et al.