Rock mass structural plane intelligent interpretation method based on point cloud and GPU technologies

A structural surface and rock mass technology, applied in the field of intelligent interpretation of rock mass structural surfaces based on point cloud and GPU technology, can solve problems such as failure to meet efficiency requirements, loss of point clouds, and blockage of structural surface extraction from point clouds

Active Publication Date: 2021-03-16
HUBEI UNIV OF TECH +2
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  • Abstract
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AI Technical Summary

Problems solved by technology

[0004] There are roughly three types of existing point cloud-based rock mass structural plane measurement methods: the first type of method needs to manually select more than three points, and extract structural plane information based on this, which is highly subjective and inefficient
The second type of method is to use the TIN network model. This type of method has the following disadvantages: 1) part of the point cloud will be lost after triangular meshing, and there is some distortion in describing the surface details; 2) when there are many discrete points in the point cloud data, or When the point cloud data is in a closed state, the process of triangular meshing will produce more errors, and even many deformed triangles will appear
Although this type of method avoids the shortcomings of the above two types of methods, it takes about ten minutes to process a million-level point cloud; and in actual engineering applications, rock mass point cloud data is often tens of millions of levels. At this time, the traditional The structural surface recognition algorithm has been far from meeting the efficiency requirements, which has hindered the practical application of structural surface extraction based on point clouds

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  • Rock mass structural plane intelligent interpretation method based on point cloud and GPU technologies
  • Rock mass structural plane intelligent interpretation method based on point cloud and GPU technologies
  • Rock mass structural plane intelligent interpretation method based on point cloud and GPU technologies

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specific Embodiment approach

[0066] see figure 1 , shown as the concrete flowchart of the inventive method, a kind of embodiment of the present invention will be provided below in conjunction with flowchart, and the steps are as follows:

[0067] 1. Use 3D scanning technology to collect rock mass point cloud data.

[0068] This step is prior art. When collecting 3D laser point cloud data of rock masses in the field, the site range for the scanner should be designed according to the outcrop range, spatial conditions and accuracy requirements of the deposit; a tripod should be set up on a flat and hard ground within the site range to ensure scanning The stability of the instrument when working.

[0069] 2. Splice and denoise the point cloud data.

[0070] This step is a preprocessing step of the point cloud data, and also belongs to the conventional technology in the field of three-dimensional scanning technology. The purpose of preprocessing is to improve the accuracy of point cloud data and obtain poi...

Embodiment

[0158] In conventional methods, the calculation of normal vectors of point cloud points is the most time-consuming link. In the face of massive point clouds, the calculation speed of normal vectors fundamentally determines the efficiency of structural surface extraction. This embodiment adopts the method of the present invention (combination method of GPU and linear regression, abbreviated as GPU+linear regression) and conventional method (combination of CPU and PCA, abbreviated as CPU+PCA), respectively for two groups of point cloud data sets A and B Carry out the normal vector calculation, and compare the calculation time consumption, the results are shown in Table 1. It can be seen from the table that the method of the present invention is fully applicable to the normal vector calculation of massive point clouds, and significantly reduces time consumption.

[0159] Table 1 Time-consuming comparison of normal vector calculation

[0160]

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Abstract

The invention discloses a rock mass structural plane intelligent interpretation method based on point cloud and GPU technologies. Collected rock mass laser point cloud data are processed as follows: step one, normal vectors of all points in the point cloud data being calculated based on GPU parallel computing and a linear regression method; 2, identifying a structural plane feature point set by using an improved region growing method; 3, extracting a structural plane boundary from the final structural plane feature point set by utilizing an improved convex hull scanning method; and 4, calculating the trace length, occurrence and spacing of the automatic interpretation structural plane based on the contour points. According to the method, the top-speed operation of massive point cloud datacan be realized, the efficiency bottleneck of identifying the structural plane based on the point cloud in the past is broken through, the rock mass structural plane information is accurately, quicklyand efficiently acquired, and meanwhile, the boundary of the structural plane can be described with high precision, so that the accurate trace length of the structural plane is calculated.

Description

technical field [0001] The invention belongs to the technical field of rock mass structural plane extraction, in particular to an intelligent interpretation method for rock mass structural planes based on point cloud and GPU technology. Background technique [0002] Due to the developed structural planes inside the rock mass, the rock mass shows a certain structure, and also directly affects the mechanical properties of the rock mass. How to efficiently and accurately obtain the geometric information of rock mass discontinuities has become a hot and difficult problem in rock mass engineering. In actual engineering, due to the advantages of convenient measurement means and accurate measurement results, the manual on-site contact measurement method is widely used in structural surface measurement. However, this method has some disadvantages, such as low contact measurement efficiency, potential safety hazards and incomplete measurement results. [0003] Three-dimensional las...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T1/20G01B11/25
CPCG06T1/20G01B11/254
Inventor 陈娜夏金梧姜清辉李书尹浩然蔡小明张小波史超黄超李亚虎丁凡桠聂峰杜昌杰
Owner HUBEI UNIV OF TECH
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