CN-121998991-A - Tunnel super-undermining rapid analysis method and system based on model and point cloud matching

CN121998991ACN 121998991 ACN121998991 ACN 121998991ACN-121998991-A

Abstract

The invention provides a method and a system for quickly analyzing tunnel super-underexcavation based on model and point cloud matching, which relate to the technical field of tunnel engineering construction and comprise the steps of obtaining tunnel point cloud data, preprocessing the tunnel point cloud data to obtain high-quality real-time point cloud data; the method comprises the steps of constructing a design model, extracting points on each section contour line in the design model to obtain a design point cloud, extracting characteristic points of the design point cloud and the actual point cloud, carrying out initial registration on the characteristic points of the design point cloud and the actual point cloud, utilizing an improved ICP algorithm to optimize matching precision, monitoring matching errors in real time in the matching process, carrying out error correction and dynamic optimization, and finally outputting to obtain a matching result, carrying out space difference analysis on the aligned actual point cloud and the design model based on the matching result, and accurately calculating the super-undermining volume of each point or region, thereby realizing quantitative evaluation on the tunnel super-undermining condition. The quality control level of tunnel construction is effectively promoted to this disclosure.

Inventors

JIANG XINBO
TIAN JIAKAI
LIU HONGLIANG
CHEN CHANGYUAN
CHEN YUXUE
TU WENFENG
MA SHILUN
CAI HUI

Assignees

山东大学

Dates

Publication Date: 20260508
Application Date: 20260410

Claims (10)

1. The tunnel super-undermining rapid analysis method based on model and point cloud matching is characterized by comprising the following steps of: Acquiring tunnel point cloud data, and preprocessing the tunnel point cloud data to obtain high-quality real point cloud data; constructing a design model, and extracting points on each section contour line in the design model to obtain a design point cloud; extracting characteristic points of the design point cloud and the real point cloud, and respectively establishing a spatial index structure of the characteristic points; Based on a spatial index result, carrying out initial registration on characteristic points of the design point cloud and the actual point cloud, optimizing matching precision by utilizing an improved ICP algorithm, iteratively searching the nearest corresponding point between the actual point cloud and the design model, minimizing the distance error between the closest corresponding point and the closest corresponding point, monitoring the matching error in real time in the matching process, carrying out error correction and dynamic optimization, and finally outputting to obtain a matching result; Based on the matching result, carrying out space difference analysis on the aligned real-time point cloud and the design model, and accurately calculating the super-undermining volume of each point or region, thereby realizing quantitative evaluation on the tunnel super-undermining condition.
2. The method for rapidly analyzing the tunnel super-undermining based on the matching of the model and the point cloud according to claim 1, wherein the obtaining the tunnel point cloud data and preprocessing the tunnel point cloud data to obtain high-quality real-time point cloud data comprises the following steps: The three-dimensional laser scanner is provided with a plurality of measuring stations in the tunnel, each measuring station collects point cloud data in a set range, and the three-dimensional laser scanner measures the distance by emitting laser beams and receiving reflected light, so that three-dimensional coordinates and reflection intensity of the surface points of the tunnel are obtained; and removing noise points of the point cloud data by adopting a self-adaptive filtering algorithm, and simplifying the denoised point cloud data by adopting a voxel grid downsampling technology to obtain high-quality real-time point cloud data.
3. The method for rapidly analyzing the tunnel super-underexcavation based on the matching of the model and the point cloud as set forth in claim 1, wherein the constructing the design model, extracting the points on each section contour line in the design model to obtain the design point cloud, comprises: constructing a design model in the form of a three-dimensional CAD model, wherein the design model comprises each section contour, each axis and each key geometric feature point of the tunnel; converting the design model into a discrete point cloud or grid format, and extracting points on the contour line according to a set interval for each section contour in the design model to form a design point cloud data set; And carrying out standardized processing on the design point cloud data, and unifying the dimensions of a coordinate system.
4. The method for rapidly analyzing the tunnel super-undermining based on the matching of the model and the point cloud according to claim 1, wherein the steps of extracting the characteristic points of the design point cloud and the real point cloud, and respectively establishing the spatial index structures of the characteristic points comprise: Respectively adopting a boundary detection algorithm to extract contour lines of the tunnel section for the design model and the real-time point cloud; for the design model, directly extracting characteristic points on a theoretical contour line according to parameterized representation of the design model; for the real-time point cloud, identifying characteristic points of the contour line by utilizing the geometric characteristics of the local curvature and the normal vector; After the feature points of the design model and the real-time point cloud are extracted, respectively establishing a spatial index structure of the feature points.
5. The method for rapid analysis of tunnel super-undermining based on model and point cloud matching according to claim 1, wherein the initial registration of feature points of the design point cloud and the real point cloud based on the spatial index result, and the optimization of matching accuracy by using an improved ICP algorithm, the iterative search of the nearest corresponding point between the real point cloud and the design model, and the minimization of the distance error between them, comprise: Performing initial registration through the geometric relationship between the feature points, respectively selecting a plurality of pairs of matched feature points in the feature point set of the design model and the real-point cloud, and calculating an initial space transformation matrix comprising a rotation matrix and a translation vector by utilizing the matched point pairs; The initial transformation matrix aligns the real-point cloud to the coordinate system of the design model; On the basis of initial registration, an improved ICP algorithm is applied to optimize matching precision, in each iteration, firstly, nearest neighbor search is conducted on a feature point set in a real-point cloud, a feature corresponding point pair set is established, on the basis, corresponding point matching is conducted on other points, and abnormal corresponding points with distances exceeding a preset threshold are removed.
6. The rapid analysis method for tunnel overexcitation based on model and point cloud matching according to claim 1, wherein the spatial difference analysis is performed on the aligned real point cloud and design model based on the matching result, the underexcavation volume of each point or area is accurately calculated, and further quantitative evaluation of tunnel underexcavation conditions is realized, and the method comprises the following steps: After the design model and the real-point clouds are matched, calculating a space difference value between each real-point cloud and the design model, wherein the space difference value is defined as a vertical distance from the real-point to the design model; Constructing a voxel model by the design model and the real-time point cloud, calculating the total super-underexcavation volume of each triangle unit body or voxel unit body, and accumulating to obtain the total super-underexcavation volume of the whole tunnel section; And outputting the over-run and under-run calculation result in a structured data format.
7. Tunnel super-underexcavation rapid analysis system based on model and point cloud matching, which is characterized by comprising: The point cloud acquisition module is used for acquiring tunnel point cloud data and preprocessing the tunnel point cloud data to obtain high-quality real-time point cloud data; The characteristic point extraction module is used for constructing a design model, extracting points on each section contour line in the design model to obtain a design point cloud, extracting characteristic points of the design point cloud and the real point cloud, and respectively establishing a spatial index structure of the characteristic points; The registration module is used for carrying out initial registration on the characteristic points of the design point cloud and the real point cloud based on the spatial index result, optimizing the matching precision by utilizing an improved ICP algorithm, iteratively searching the nearest corresponding point between the real point cloud and the design model, minimizing the distance error between the real point cloud and the design model, monitoring the matching error in real time in the matching process, carrying out error correction and dynamic optimization, and finally outputting to obtain a matching result; And the undermining volume quantification module is used for carrying out space difference analysis on the aligned real-time point cloud and the design model based on the matching result, accurately calculating the undermining volume of each point or region, and further realizing quantification assessment on the tunnel undermining condition.
8. A computer program product comprising a computer program, characterized in that the computer program, when executed by a processor, implements the model-and point cloud-matching based tunnel super undermining rapid analysis method according to any of claims 1-6.
9. A non-transitory computer readable storage medium storing computer instructions which, when executed by a processor, implement the model and point cloud matching based tunnel super undermining fast analysis method of any of claims 1-6.
10. An electronic device comprising a processor, a memory and a computer program, wherein the processor is connected to the memory, the computer program is stored in the memory, and when the electronic device is running, the processor executes the computer program stored in the memory, so that the electronic device executes the tunnel underrun rapid analysis method based on model and point cloud matching according to any one of claims 1-6.

Description

Tunnel super-undermining rapid analysis method and system based on model and point cloud matching Technical Field The disclosure relates to the technical field of tunnel engineering construction, in particular to a method and a system for rapidly analyzing tunnel super-underexcavation based on model and point cloud matching. Background The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art. In the tunnel engineering construction process, the over-and-under-excavation control is one of key factors affecting engineering quality, construction safety and economic benefits. The overdrawing can cause the increase of the supporting cost and the extension of the construction period, and the underdigging can cause secondary treatment and even influence the stability of the tunnel structure. The traditional super-undermining detection method mainly relies on single-point measurement equipment such as total stations and section meters, and performs comparison analysis by collecting limited characteristic point data and designing sections. However, the method has the defects of low measurement efficiency, insufficient data density, more manual intervention and the like, and is difficult to meet the requirements of modern tunnel engineering on high-precision, real-time and automatic detection. In recent years, three-dimensional laser scanning technology is gradually introduced into the field of tunnel engineering detection due to the advantages of high precision, high efficiency, non-contact measurement and the like. The technology can rapidly acquire mass point cloud data of the tunnel surface, and provides more comprehensive space information for the super-undermining analysis. However, the existing method for calculating the undermining based on laser scanning still has some limitations: on one hand, the point cloud data processing efficiency is low, and noise interference is large. The tunnel point cloud data obtained by three-dimensional laser scanning is huge in volume, and is influenced by construction environments (such as dust, illumination, equipment vibration and the like), and often contains a large amount of noise and redundant data. Traditional data filtering and denoising algorithms are high in computational complexity, quick processing is difficult to achieve while accuracy is guaranteed, the efficiency of super-undermining analysis is low, and the requirements of construction real-time monitoring cannot be met. How to efficiently remove noise, simplify point cloud data and retain effective tunnel profile features is a key technical problem for realizing rapid calculation of super-undermining. And (II) on the other hand, the problem of automatic and accurate matching of the design model and the real-time point cloud is solved. The existing super-undermining analysis method generally depends on manual selection of a reference plane or segmentation comparison, and lacks an automatic global matching strategy, so that the subjectivity of the calculation process is strong, and the error accumulation is obvious. Because local deformation or construction deviation possibly exists in the tunneling process, how to realize automatic alignment of the actual point cloud and the design model and accurately quantify the super-underexcavated volume is a technical difficulty which is not effectively solved at present. And (III) how to dynamically optimize the matching algorithm to adapt to the geometric characteristics of different tunnel sections is also an important challenge for improving the calculation accuracy. The existing method is mostly dependent on manual selection of a reference plane or local comparison, and lacks an automatic global undermining assessment means, so that the reliability and consistency of a calculation result are insufficient. Disclosure of Invention In order to solve the problems, the method and the system for quickly analyzing the tunnel super-undermining based on the matching of the model and the point cloud are provided, the automatic alignment of the design model and the real-time point cloud is carried out by optimizing a point cloud processing algorithm and combining an improved Iterative Closest Point (ICP) algorithm, so that automatic matching and calculation are realized, the detection efficiency and the detection precision are improved, and a reliable technical support is provided for the control of the tunnel construction quality. According to some embodiments, the present disclosure employs the following technical solutions: a tunnel super-undermining rapid analysis method based on model and point cloud matching comprises the following steps: Acquiring tunnel point cloud data, and preprocessing the tunnel point cloud data to obtain high-quality real point cloud data; constructing a design model, and extracting points on each section contour line in the design model to obtain a design po