## 1 Introduction
The ICP algorithm needs to fulfill two basic conditions: (1) There is a common area between the reference point cloud and point cloud to be registered; and (2) The initial relative deviation between the reference point cloud and point cloud to be registered is not large [25; 26]. Some scholars have improved the ICP algorithm and representative studies include [PERSON] et al. [27], who proposed an ICP algorithm based on point cloud homography and explained the method of establishing homography point pairs; [PERSON] and [PERSON] [28], who proposed a vector information registration method based on facet triangles in the stereolithography (STL) format file and introduced a dynamic adjustment factor to speed up the iterative convergence; [PERSON] et al. [29] proposed an improved ICP algorithm, which used a point-to-point correspondence instead of point-to-surface correspondence to reduce the calculation cost; [PERSON] et al. [30] proposed a novel robust scale ICP algorithm by introducing the maximum correntropy criterion (MCC) as the similarity measure when the point sets had a large number of outliers and noises; [PERSON] and [PERSON] [31] presented an efficient expectation-maximisation (EM)-ICP algorithm for non-linear pairwise registration of large 3D point sets, compared to other methods using the same \"EM-ICP\" framework, four key modifications leading to an efficient algorithm: (1) truncation of the cost function; (2) symmetrization of the point-to-point correspondences; (3) specification of priors on these correspondences using differential geometry; and (4) efficient encoding of deformations using the reproducing kernel Hilbert space (RKHS) theory and the Fourier analysis. Slope deformation measurement based on TLS determines the spatial position of the comparison point relative to the reference point by processing the reference point cloud (point cloud of the first phase) and the comparison point cloud (point cloud of the second phase) [32]. It includes four types of methods: point cloud superposition method, point cloud comparison method, center of gravity method, and fitting method [33]. Among them, the point cloud superposition method and point cloud comparison method belong to surface measurement as their monitoring points do not necessarily correspond to one-to-one, and the center of gravity method and fitting method can achieve one-to-one correspondence of monitoring points, equivalent to conventional feature point measurement [34]. Representative studies include [PERSON] et al. [35], who realized landslide stability monitoring by calculating the landslide volume in different periods based on the point cloud and digital elevation model (DEM); [PERSON] et al. [36] proposed a method for surface deformation monitoring of high-risk slopes based on TLS that extracted the normal vector of the reference points and calculated the center of gravity of the comparison point cloud, which were used as the basis for comparison of surface deformation; and [PERSON] et al. [37] took the front edge of Jinpingzi landslide II region as the research object, arranged target bodies in \"surface monitoring areas\" and performed deformation analysis on the target bodies. Slope deformation monitoring based on TLS can still be improved in the following aspects:  
(1) Regarding point cloud registration, existing feature extraction algorithms have disadvantages such as low extraction efficiency, require multiple parameters, and have poor real-time performance for large scenes; existing improved ICP algorithms have disadvantages such as high time and space complexity and are prone to matching errors and slow iterative convergence;  
(2) Regarding slope deformation measurement, point measurement methods have high accuracies, but only the key parts can be monitored and the deformations in areas without monitoring points cannot be revealed, whereas surface measurement methods can describe the deformation of each part of the slope surface, but the accuracies are low.