## 1 Introduction  
The original stress state of natural or artificial slopes will change in the process of geological evolution and engineering construction, resulting in stress redistribution and stress concentration [1]. In order to adapt to the new stress state, the rock and soil of slope will undergo different forms and scales of deformation (such as unloading rebound and creep) [2; 3; 4]. Revealing the probability and extent of disastrous deformation are the prerequisites for slope stability analysis [5]. Traditional slope deformation monitoring methods include manual survey, global navigation satellite system (GNSS) measurement, total station measurement and close-range photogrammetry [6]. Among them, manual survey is unsuitable for high and steep slopes, and the results depend on human interpretation of the terrain, which is highly subjective [7]; GNSS measurements and total station measurements are single-point measurement methods, which have difficulties in determining the deformation of the areas without monitoring points, moreover, once the monitoring points are destroyed, the continuity of the data will be affected [8; 9]. In close-range photogrammetry, each image covers only a small part of the scene and the data processing involves cumbersome processes such as orienteering and inlay, resulting in low work efficiency [10; 11]. Slope deformation monitoring based on terrestrial laser scanning (TLS) can provide discrete three-dimensional data of the slope surface, thereby avoiding the locality and one-sidedness of stress-strain analysis based on data from single monitoring point [12]. These advantages ensure that TLS-based slope deformation monitoring has broad application prospects [13; 14]. Affected by factors such as the performance of TLS facility and range of slope, the whole information of some slopes needs to be acquired through multi-station scanning, and the point cloud data of each station need to be registered to the same coordinate system [15; 16]. Point cloud registration includes feature-based registration and featureless registration [17], of which feature extraction is the core of the feature-based registration algorithm, and representative studies include: [PERSON] et al. [18] proposed an image registration algorithm named belief propagation (BP)-scale-invariant feature transform (SIFT), where the keypoint matching of SIFT descriptors was formulated as a global optimization problem and provided a suboptimum solution using BP; [PERSON] and [PERSON] [19] presented an improved SIFT algorithm on characteristic statistical distributions and consistency constraints since the feature descriptor section of conventional SIFT does not make full use of the local feature information; [PERSON] et al. [20] proposed an algorithm for automatic extraction of diagonal marks by combining template matching and gradient peaks, which improved the robustness of corner point detection; [PERSON] et al. [21] proposed a super 4-points congruent sets (4 PCS) algorithm combining the scale invariant features in light of the low efficiency of the traditional initial registration method; and [PERSON] and [PERSON] [22] proposed a point cloud registration method based on SIFT and geometry feature in order to improve the registration accuracy. The object was extracted based on threshold segmentation, the initial corresponding points were acquired based on the SIFT operator, and furthermore, the curvature was performed to remove the wrong points. [PERSON] and [PERSON] [23] proposed the iterative closest point (ICP) algorithm, which by far is the most widely used featureless point cloud registration algorithm. It calculates the rotation and translation transformation matrix of the point cloud to be registered by creating a corresponding point pair with the smallest Euclidean distance between the reference point cloud and point cloud to be registered [24].