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Abstract
Slope deformation monitoring is the prerequisite for disaster risk assessment and engineering control. Terrestrial laser scanning (TLS) is highly applicable to this field. Coarse registration method of point cloud based on scale-invariant feature transform (SIFT) feature points and fine registration method based on the k-dimensional tree (K-D tree) improved iterative closest point (ICP) algorithm were proposed. The results show that they were superior to other algorithms (such as speeded-up robust features (SURF) feature points, Harris feature points, and Levenberg-Marquardt (LM) improved ICP algorithm) when taking the Stanford Bunny as an example, and had high applicability in coarse and fine registration. In order to integrate the advantages of point measurement and surface measurement, an improved point cloud comparison method was proposed and the optimal model parameters were determined through model tests. A case study was conducted on the left side of the K146 + 150 point at S236 Boshan section, Shandong Province, and research results show that from 14 August 2018 and 9 November 2019, the overall deformation of the slope was small with a maximum value of 0.183 m, and the slope will continue to maintain a stable state without special inducing factors such as earthquake, heavy rainfall and artificial excavation.
Highlights
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 [2,3,4]
The coarse registration method of point cloud based on scale-invariant feature transform (SIFT) feature points and the fine registration
iterative closest point (ICP) algorithm were feature proposed. Theand results they were superior other on algorithms as SURF feature points, Harris points, LM. Showed that they were superior to other algorithms when taking the Stanford Bunny as an example, and have high applicability points, and LM improved ICP algorithm) when taking the Stanford Bunny as an example, and have in coarse and fine registration
Summary
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]. 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]. Studies include Schürch et al deformation [35], who realized landslide of stability monitoring calculating the landslide volume in different periods based thecalculated point cloud and slopes based on by TLS that extracted the normal vector of the reference pointson and the center digital ofelevation model (DEM); Zhu etwhich al. Slope deformation monitoring based on TLS can still beextraction improved algorithms in the following such as low extraction efficiency, require multiple parameters, and have poor real-time performance (1) Regarding point cloud registration, existing feature extraction algorithms have for large scenes; existing improved. Provide a direct basis for disaster risk assessment and engineering control
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