Method and system for determining tunnel point cloud axis height

Abstract

The invention discloses a method and a system for determining the height of a tunnel point cloud axis. The method comprises the following steps: acquiring original point cloud data of a tunnel and preprocessing the original point cloud data; performing tunnel contour fitting on the preprocessed point cloud data; extracting a highest point representing a tunnel axis from a tunnel contour fitting result to form a highest point space set; and forming a continuous tunnel axis based on the highest point space set. The method can quickly and accurately determine the height of the tunnel axis.

Description

Technical Field

[0001] The present invention relates to the technical field of laser radar point cloud data processing, and in particular to a method and system for determining the height of a tunnel point cloud axis. Background Art

[0002] Most existing tunnel point cloud data processing methods rely on manual operations, which are inefficient and their accuracy is limited by the operator's experience. Automated point cloud data processing methods improve efficiency and accuracy, but existing technologies often have difficulty in accurately and quickly determining the height of the tunnel axis when processing tunnel point cloud data.

[0003] When processing tunnel point cloud data, the existing technology usually adopts the following methods to determine the height of the tunnel axis, but these methods have certain limitations, which makes it difficult to accurately and quickly determine the height of the tunnel axis.

[0004] Some technologies use methods based on geometric feature extraction to process tunnel point cloud data, that is, by analyzing the geometric shape features of point cloud data, such as finding symmetrical points on the tunnel wall or fitting planes, to infer the position and height of the tunnel axis. For example, the tunnel wall is first fitted with a plane, and then the height of the tunnel axis is estimated based on the positional relationship of the fitted plane. In addition, some technologies use methods based on cluster analysis to process tunnel point cloud data, that is, clustering the point cloud data, classifying points with similar features into one category, and then identifying the point set representing the tunnel axis from the clustering results. For example, clustering is performed based on the spatial distribution and density of the point cloud, and points that may belong to the tunnel axis are divided into a specific cluster.

[0005] However, when processing tunnel point cloud data, the above-mentioned prior art is difficult to accurately and quickly determine the height of the tunnel axis due to factors such as data noise, complexity of tunnel shape and lack of effective feature extraction methods. Summary of the invention

[0006] The purpose of the present invention is to provide a solution that can accurately and quickly determine the height of the tunnel axis.

[0007] In order to solve the above technical problems, an embodiment of the present invention provides a method for determining the height of a tunnel point cloud axis, comprising: acquiring the original point cloud data of the tunnel and preprocessing it; performing tunnel contour fitting on the preprocessed point cloud data; extracting the highest point representing the tunnel axis from the tunnel contour fitting result to form a highest point spatial set; based on the highest point spatial set, forming a continuous tunnel axis.

[0008] Preferably, the step of preprocessing the original point cloud data includes: collecting tunnel design parameters and determining the elevation range of the point cloud points representing the bottom surface of the tunnel; based on the original point cloud data, using the bottom elevation range, preliminarily classifying the original point cloud data into tunnel wall points and tunnel bottom points; based on the preliminarily classified point cloud points, combining the spatial distribution characteristics of the points and the degree of elevation change of the local area to which they belong, performing a secondary classification on the preliminarily classified point cloud data to obtain intermediate point cloud data marked with the classification results, so as to use the intermediate point cloud data to fit the tunnel contour.

[0009] Preferably, the step of preprocessing the original point cloud data also includes: correcting the classification results of the intermediate point cloud data by analyzing the consistency between the type of each point in the intermediate point cloud data and the point type in the neighborhood of the point, obtaining corrected point cloud data, and using the corrected point cloud data to perform tunnel contour fitting.

[0010] Preferably, the step of fitting the tunnel contour to the intermediate point cloud data includes: segmenting the intermediate point cloud data, identifying the overall contour geometry, tunnel surface change trend characteristics and geological characteristics of the tunnel crossing area of ​​each point cloud data segment; selecting a suitable fitting algorithm to fit the tunnel contour to each point cloud data segment based on the overall contour geometry, tunnel surface change trend characteristics and geological characteristics of the tunnel crossing area of ​​each point cloud data segment; and integrating the fitting results of each segment.

[0011] Preferably, the tunnel surface trend change characteristics include flatness change, curvature change and deformation characteristics.

[0012] Preferably, the step of selecting a suitable fitting algorithm includes: for point cloud data segments that belong to straight segments and flat tunnel surfaces, a geometric shape fitting algorithm based on the RANSAC algorithm is used to fit the tunnel contour; for point cloud data segments that belong to curved segments, a spline curve fitting or a polynomial fitting algorithm is used to fit the tunnel contour; for point cloud data segments that have local deformation or unevenness or geological anomalies, an overall fitting algorithm is used to determine the approximate contour, and then a local fitting algorithm is used to correct the local deformation area in the approximate contour fitting result to obtain the tunnel contour fitting result, the overall fitting algorithm is a geometric shape fitting algorithm, and the local fitting algorithm is a spline curve fitting or a polynomial fitting algorithm.

[0013] Preferably, the step of extracting the highest point representing the tunnel axis from the tunnel profile fitting result to form a spatial set of highest points includes: extracting the highest point of the curve by analyzing the first-order derivative and the second-order derivative of each cross-sectional curve in the tunnel profile fitting result to obtain the spatial set of highest points.

[0014] Preferably, the step of extracting the highest point of the curve by analyzing the first-order derivative and the second-order derivative of each cross-section curve in the tunnel profile fitting result includes: extracting at least one initial extreme point according to the first-order derivative of the current cross-section curve; substituting the at least one initial extreme point into the second-order derivative of the current cross-section curve to obtain the function value of the at least one initial extreme point; and determining the highest point of the current cross-section curve according to the function value of the at least one initial extreme point.

[0015] Preferably, in the step of determining the highest point of the current cross-section curve according to the function value of the at least one initial extreme point, it includes: selecting a point whose function value is less than zero from the at least one initial extreme point as a candidate point cloud point; if there are multiple candidate point cloud points, then selecting the point cloud point with the largest elevation value as the highest point, wherein if there is only one candidate point cloud point, then the candidate point cloud point is selected as the highest point.

[0016] Preferably, the step of forming a continuous tunnel axis based on the highest point space set includes: determining the adjacent point cloud data of each highest point in the highest point space set according to a preset range; sorting all the highest points and their corresponding adjacent point cloud data according to the coordinate values ​​in the axial direction of the tunnel to form a continuous sequence; and using spline interpolation to perform curve fitting on the interval formed by each pair of adjacent element points in the continuous sequence to form the continuous tunnel axis.

[0017] On the other hand, an embodiment of the present invention provides a system for determining the height of a tunnel point cloud axis, comprising: a point cloud data preprocessing module, configured to obtain and preprocess the original point cloud data of the tunnel; a contour fitting module, configured to perform tunnel contour fitting on the preprocessed point cloud data; a highest point extraction module, configured to extract the highest point representing the tunnel axis from the tunnel contour fitting result to form a highest point space set; and a tunnel axis generation module, configured to form a continuous tunnel axis based on the highest point space set.

[0018] Compared with the prior art, one or more embodiments of the above scheme may have the following advantages or beneficial effects: The present invention proposes a method and system for determining the height of a tunnel point cloud axis. The method and system provide an automated laser radar tunnel point cloud data processing solution, which can quickly and accurately determine the height of the tunnel axis. The present invention has a high degree of automation, reduces manual intervention, improves the efficiency of data processing, and improves the accuracy of determining the height of the tunnel axis through a fitting algorithm. In addition, the present invention is also applicable to tunnels of different types and sizes, and has good versatility and adaptability.

[0019] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the description, claims and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention.

[0021] Figure 1 A schematic diagram of the steps of a method for determining the height of a tunnel point cloud axis according to an embodiment of the present application.

[0022] Figure 2 Schematic diagram of the structure of a system for determining the height of a tunnel point cloud axis according to an embodiment of the present application. DETAILED DESCRIPTION

[0023] The following will describe the implementation methods of the present invention in detail with reference to the accompanying drawings and embodiments, so that the implementation process of how the present invention applies technical means to solve technical problems and achieve technical effects can be fully understood and implemented accordingly. It should be noted that as long as there is no conflict, the various embodiments of the present invention and the various features in the embodiments can be combined with each other, and the technical solutions formed are all within the protection scope of the present invention.

[0024] In addition, the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions. Also, although a logical sequence is shown in the flowchart, in some cases, the steps shown or described can be performed in a sequence different from that here.

[0025] The terms used herein are only for describing specific embodiments and are not intended to limit exemplary embodiments. Unless the context clearly indicates otherwise, the singular forms "one", "one" and "item" used herein are also intended to include plural numbers. It should also be understood that the terms "include" and / or "comprise" used herein specify the existence of stated features, integers, steps, operations, units and / or components, without excluding the existence or addition of one or more other features, integers, steps, operations, units, components and / or combinations thereof.

[0026] In the existing tunnel point cloud data processing technology, it is often difficult to accurately and quickly determine the height of the tunnel axis due to the following reasons.

[0027] 1. The impact of data noise and outliers.

[0028] Tunnel point cloud data is usually affected by measurement errors, environmental interference and other factors, and there are a lot of noise and outliers. These noise and outliers will interfere with the process of geometric feature extraction and cluster analysis, making it difficult to determine the height of the tunnel axis. For example, in the plane fitting process, noise points may cause inaccurate fitting planes, thereby affecting the estimation of the axis height. For another example, the noise in the point cloud data may cause deviations in the clustering results, incorrectly classifying points that do not belong to the tunnel axis into the axis-related cluster, or classifying the axis points into the wrong cluster, affecting the determination of the axis height.

[0029] 2. The tunnel shape is irregular and complex.

[0030] The shape of an actual tunnel is often not a completely regular cylinder, and there may be bends, deformations, branches, etc. This irregularity and complexity increases the difficulty of determining the height of the tunnel axis. For example, in a curved tunnel, the traditional method based on symmetry points or plane fitting may no longer be applicable because it is difficult to find accurate symmetry points or suitable planes for fitting. In addition, there may be various ancillary facilities inside the tunnel, such as cable trays, ventilation ducts, etc. These facilities will also increase the complexity of the point cloud data and interfere with the identification and height determination of the tunnel axis.

[0031] 3. Lack of effective feature extraction methods.

[0032] When processing tunnel point cloud data, current feature extraction methods often have difficulty accurately extracting features related to the tunnel axis height. For example, some geometric feature extraction methods may be sensitive to local features of the tunnel wall and ignore the overall axis height information. In addition, existing clustering analysis methods may not be able to effectively distinguish tunnel axis points from other points, especially when the point cloud data density is uneven or there are a large number of interference points.

[0033] In summary, when processing tunnel point cloud data, the existing technology is difficult to accurately and quickly determine the height of the tunnel axis due to factors such as data noise, complexity of tunnel shape and lack of effective feature extraction methods.

[0034] In order to solve the technical problem that the prior art is difficult to accurately and quickly determine the height of the tunnel axis due to factors such as data noise, tunnel shape complexity and lack of effective feature extraction methods when processing tunnel point cloud data, the present invention provides a method and system for determining the height of the tunnel point cloud axis. The method and system use point cloud data collected by laser radar, through a fitting algorithm, combined with the tunnel design contour, to automatically, quickly and accurately determine the highest point of the tunnel axis, and fit it into a line, with good versatility and adaptability.

[0035] Figure 1This is a schematic diagram of the steps of the method for determining the height of the tunnel point cloud axis according to an embodiment of the present application. Figure 1 , the specific steps of the method for determining the height of the tunnel point cloud axis (also referred to as “tunnel axis height determination method”) described in an embodiment of the present invention are described.

[0036] like Figure 1 As shown, in step S110, the original point cloud data of the tunnel is obtained and preprocessed.

[0037] In step S110, a laser radar device is first used to scan the tunnel to be processed to collect original point cloud data about the tunnel to be processed, and then the current original point cloud data is pre-processed.

[0038] In the preprocessing process, first, the tunnel design parameters are collected and the elevation range of the point cloud points representing the bottom surface of the tunnel is determined; then, based on the original point cloud data, the original point cloud data is preliminarily classified using the bottom elevation range, and the classification is into tunnel wall points and tunnel bottom points; finally, based on the preliminarily classified point cloud points, combined with the spatial distribution characteristics of the points and the degree of elevation change of the local area to which they belong, the preliminarily classified point cloud data is secondary classified to obtain intermediate point cloud data marked with the classification results, so as to use the intermediate point cloud data as the preprocessing result to perform tunnel contour fitting.

[0039] Specifically, tunnel design parameters are collected. In the embodiment of the present invention, tunnel design parameters include: tunnel design dimensions and tunnel morphological characteristics. Among them, tunnel design dimensions include at least information such as tunnel height, width, bottom elevation, etc. Tunnel morphological characteristics include at least parameters such as tunnel shape (for example: circular tunnel, rectangular tunnel, horseshoe tunnel, etc.), tunnel design slope, etc.

[0040] Then, the preliminary classification result of each point in the original point cloud data is marked by determining the elevation range representing the point cloud point belonging to the tunnel bottom surface.

[0041] Specifically, the tunnel bottom elevation information in the tunnel design parameters is used to determine the approximate bottom elevation range. Then, all the point cloud points in the original point cloud data are traversed, and the point cloud points with elevation values ​​within the bottom elevation range are preliminarily marked as points that may belong to the tunnel bottom, and the remaining points are temporarily marked as points that may belong to the tunnel wall.

[0042] Then, the initially classified point cloud data is further classified secondary based on the geometric features to obtain intermediate point cloud data marked with the classification results, so as to use the intermediate point cloud data as the preprocessing result.

[0043] Specifically, the geometric centroid of the three-dimensional space of the initially classified point cloud data is calculated as the tunnel center. For points that may belong to the tunnel wall, the spatial distribution characteristics of these points are first calculated, such as the distance from the point to the tunnel center. Specifically, for a circular tunnel, the distance from the point to the center of the tunnel section where the point is located is calculated; for a rectangular tunnel, the distance from the point to each side of the tunnel section where the point is located is calculated.

[0044] Then, according to the designed width and height of the tunnel, the corresponding tunnel wall space area range is determined for marking the tunnel wall points. Finally, according to the spatial distribution characteristics of these points, combined with the tunnel wall space area range, it is determined whether the point cloud points initially classified and marked as tunnel wall points are still tunnel wall points. If the distance from a point initially classified and marked as a tunnel wall to the center or edge of the tunnel is within the design range of the tunnel wall, the point is determined to be a tunnel wall point. If the distance from a point initially classified and marked as a tunnel wall to the center or edge of the tunnel is not within the design range of the tunnel wall, the point is determined to be an abnormal noise point and deleted.

[0045] For points that may belong to the bottom of the tunnel, the flatness and continuity of these points are first analyzed, so as to determine whether the point cloud points initially classified and marked as tunnel bottom points are still tunnel bottom points based on the analysis results.

[0046] Specifically, the point cloud data composed of all point cloud points marked as tunnel bottom points after preliminary classification are divided into several local areas according to the first preset local area range, and the elevation change degree of the points in each local area is calculated. If the elevation change is less than the preset elevation change threshold and the points in the local area are continuously distributed in space, the slope verification of the points in this local area continues; otherwise, the points in the local area that make the elevation change exceed the elevation change threshold or are discontinuous in the local area space are determined as abnormal points, and the abnormal points in the local area are deleted.

[0047] During the slope verification process, the elevation change degree of the current local area is compared with the tunnel design slope, that is, to check whether the elevation change of the point cloud points in the local area meets the slope requirements. If the elevation change of the point is consistent with the tunnel design slope, it is confirmed that the point is still the bottom point of the tunnel. If the elevation change degree of the current local area does not match the tunnel design slope, the points in the local area that make the elevation change degree inconsistent with the design slope are determined as abnormal noise points and deleted.

[0048] In this way, after determining whether the tunnel wall points after preliminary classification are still tunnel wall points and whether the tunnel bottom points are still tunnel bottom points, the intermediate point cloud data is obtained, and the intermediate point cloud data is used to carry out the subsequent tunnel contour fitting process. Among them, the intermediate point cloud data filters out the interference points such as abnormal values, discrete values, noise, etc. in the original point cloud data.

[0049] Furthermore, in order to improve the accuracy of the tunnel profile fitting results described below, the embodiment of the present invention also includes a step of correcting the pre-processed original point cloud data (i.e., intermediate point cloud data), so that the corrected intermediate point cloud data can also be used for subsequent tunnel profile fitting processing.

[0050] In one embodiment, the classification result of the intermediate point cloud data is corrected by analyzing the consistency between the classification type of each point in the intermediate point cloud data and the point type of the point neighborhood, and the corrected point cloud data is obtained, so as to use the corrected point cloud data to perform tunnel contour fitting.

[0051] For each point in the intermediate point cloud data, the classification characteristics of its neighboring points are analyzed. If a point is marked as a tunnel wall point, and most of its surrounding neighboring points are marked as tunnel bottom points, recheck whether the point is misclassified. Similarly, if a point is marked as a tunnel bottom point, and most of its surrounding neighboring points are marked as tunnel wall points, recheck and adjust. Finally, perform multiple iterations to optimize the corrected classification results, and continuously adjust the misclassified points until the classification results are stable, thereby obtaining the corrected intermediate point cloud data, that is, the corrected point cloud data.

[0052] By correcting the intermediate point cloud data, the point cloud data can be more accurately classified into point cloud data of the tunnel wall and the tunnel bottom based on the tunnel design parameters, so as to accurately filter out abnormal points and noise points according to the classification results.

[0053] Step S120, performing tunnel contour fitting on the pre-processed point cloud data. Further, the intermediate point cloud data or the corrected point cloud data is used as the point cloud data to be processed, so as to perform tunnel contour fitting on the point cloud data to be processed.

[0054] In step S120, first, the current point cloud data to be processed is segmented to identify the overall contour geometry, tunnel surface change trend characteristics and geological characteristics of the tunnel crossing area of ​​each point cloud data segment; then, according to the overall contour geometry, tunnel surface change trend characteristics and geological characteristics of the tunnel crossing area of ​​each point cloud data segment, a suitable fitting algorithm is selected to fit the tunnel contour of each point cloud data segment; finally, the fitting results of each segment are integrated to obtain the overall tunnel contour fitting result.

[0055] In an embodiment of the present invention, the tunnel surface trend change characteristics include flatness change, curvature change and deformation characteristics.

[0056] Furthermore, in order to improve the accuracy and adaptability of the tunnel fitting result, the embodiment of the present invention selects a suitable fitting algorithm for point cloud data segments in different deformation states.

[0057] In the first embodiment, for the point cloud data segments belonging to the straight line segments and the flat tunnel surface, a geometric shape fitting algorithm based on the RANSAC algorithm is used to fit the tunnel contour.

[0058] In the second embodiment, for the point cloud data segments belonging to the curve segments, a spline curve fitting or a polynomial fitting algorithm is used to fit the tunnel profile.

[0059] In the third embodiment, for a point cloud data segment with local deformation, unevenness or geological anomalies, an overall fitting algorithm is used to determine the general outline, and then a local fitting algorithm is used to correct the local deformation area in the general outline fitting result to obtain a tunnel outline fitting result. The overall fitting algorithm is a geometric shape fitting algorithm, and the local fitting algorithm is a spline curve fitting algorithm or a polynomial fitting algorithm.

[0060] Specifically, the tunnel is divided into different sections according to tunnel design parameters, construction records and on-site survey results. The tunnel is divided according to factors such as changes in geological conditions, different construction methods, curved and straight sections, etc. Then, the point cloud data segments of each section are stored and marked separately for subsequent processing.

[0061] Analyze the overall contour geometry of each point cloud data segment, the tunnel face change trend characteristics, and the geological characteristics of the tunnel crossing area. For each segment, carefully analyze the tunnel contour geometry presented by the corresponding point cloud data segment. For example, a straight line segment may present a relatively regular rectangular or circular contour, while a curved segment may have certain deformation and irregularity.

[0062] Analyze the tunnel surface change trend characteristics including flatness, curvature change and deformation of the tunnel wall. Some sections of the tunnel wall may be relatively smooth, while others may be uneven or partially deformed.

[0063] Analyze the geological factors affecting the tunnel sections. Tunnels pass through different geological areas, and the tunnel profiles of different sections may be affected by geological conditions. For example, in soft soil sections, the tunnel may have greater deformation; in hard rock sections, the tunnel profile may be more regular.

[0064] In the process of selecting the fitting algorithm, for straight segments and relatively regular tunnel contours, simple geometric shape fitting algorithms such as rectangular fitting or circular fitting are selected. Rectangular fitting determines the parameters of the rectangle by finding four boundary points and is suitable for tunnels with rectangular cross-sections. Circular fitting uses algorithms such as the least squares method to determine the center and radius of the circle and is suitable for circular tunnels.

[0065] For curve segments, a more complex fitting algorithm is used, and circular curves are usually used to fit tunnel curve segments. Specifically, spline curves can be used for fitting, which can adapt well to changes in curve shape. The shape of the curve is adjusted by control points, so that the fitting result reflects the tunnel profile more accurately. Polynomials can also be used for fitting, and different degrees of polynomials can be used to adapt to different degrees of curve changes.

[0066] For special sections with local deformation, unevenness or geological anomalies, multiple algorithms are combined for fitting. For example, the overall fitting algorithm is first used to determine the general outline, and then the local deformation area is corrected using the local fitting algorithm.

[0067] It should be noted that, in the actual fitting process, the embodiment of the present invention can also adjust the parameters of the selected fitting algorithm according to the specific characteristics of each section. Specifically, for polynomial fitting, according to the point cloud data of the corresponding section and the tunnel design parameters, a suitable polynomial degree is selected to avoid overfitting or underfitting.

[0068] Finally, step S120 can also verify the fitting results of each section, so as to integrate the verified and adjusted tunnel section contour fitting results into the overall tunnel contour fitting results. The fitting error (such as calculating the root mean square error and other indicators) is calculated by comparing the degree of fit between the tunnel section contour fitting results and the original point cloud data of the corresponding section, so as to evaluate the fitting results using the calculated fitting error.

[0069] Therefore, through the above step S120, the embodiment of the present invention can select a suitable fitting algorithm for different sections of the tunnel to better adapt to the changes in the tunnel contour and improve the processing accuracy of the tunnel point cloud data.

[0070] Step S130, extracting the highest point representing the tunnel axis from the tunnel profile fitting result to form a spatial set of highest points.

[0071] In one embodiment, the highest point of the curve is extracted by analyzing the first-order derivative and the second-order derivative of each cross-section curve in the tunnel profile fitting result, thereby obtaining a spatial set of highest points.

[0072] The first-order derivative reflects the rate of change of a function at a certain point. For a fitted curve, the first-order derivative represents the slope of the curve at that point. When the first-order derivative is zero, the slope of the curve at that point is zero, which may be the extreme point (maximum or minimum) or inflection point of the curve. In addition, the second-order derivative reflects the change in the curvature of the function. For a fitted curve, the second-order derivative represents the degree of curvature of the curve. When the second-order derivative is greater than zero, the curve is convex at that point, which may be a minimum point; when the second-order derivative is less than zero, the curve is convex at that point, which may be a maximum point; when the second-order derivative is zero, it may be an inflection point.

[0073] In one embodiment, step S130, first, the fitting result of the overall tunnel profile needs to be fitted, and multiple tunnel sections need to be extracted according to the preset axial interval; then, the top of each tunnel section is determined for curve fitting to generate the corresponding section curve expression; finally, the highest point of the tunnel of each section is determined according to each section curve expression.

[0074] Specifically, according to the characteristics of the tunnel point cloud data and the fitting requirements, a suitable function is selected to perform curve fitting on the top of each tunnel section. In an embodiment of the present invention, the tunnel top curve fitting function is a circular curve, and a spline curve fitting or a polynomial fitting algorithm is used to achieve the fitting of the section curve.

[0075] Furthermore, in the process of determining the highest point of the tunnel of each section according to each section curve expression, it includes: extracting at least one initial extreme point according to the first-order derivative of the current section curve; then, substituting the at least one initial extreme point into the second-order derivative of the current section curve to obtain the function value of the at least one initial extreme point; finally, determining the highest point of the current section curve according to the function value of the at least one initial extreme point.

[0076] In a specific embodiment of determining the highest point of the current cross-section curve based on the function value of at least one initial extreme point, a point whose function value is less than zero is selected from at least one initial extreme point as an alternative point cloud point. If there are multiple alternative point cloud points, the point cloud point with the largest elevation value is selected as the highest point. If there is only one alternative point cloud point, the alternative point cloud point is selected as the highest point.

[0077] Set the first derivative of the cross-sectional curve equal to zero, that is, solve the equation to obtain possible extreme points, thereby obtaining at least one initial extreme point. For polynomial equations, use numerical methods such as Newton's method, bisection method, etc. to solve the roots of the equation.

[0078] Then, the highest point is determined by analyzing the second-order derivative of the cross-section curve. Specifically, the initial extreme point obtained by the solution is substituted into the second-order derivative for judgment. If the second-order derivative value is less than zero, the point is a maximum point and may be the highest point of the curve. If there are multiple maximum points, compare their function values ​​(elevation values) and determine that the point with the maximum elevation value is the highest point.

[0079] Therefore, by solving the highest points of multiple cross-sectional circular curves, a spatial set of highest points is formed.

[0080] Step S140, forming a continuous tunnel axis based on the highest point space set generated in step S130.

[0081] In step S140, first, the adjacent point cloud data of each highest point in the highest point space set is determined according to a preset range; then, all the highest points and their corresponding adjacent point cloud data are sorted according to the coordinate values ​​in the axial direction of the tunnel to form a continuous sequence; finally, spline interpolation is used to perform curve fitting on the interval formed by each pair of adjacent element points in the current continuous sequence, thereby forming a continuous tunnel axis.

[0082] The cubic spline interpolation method is used to generate a continuous curve of the tunnel axis based on the highest point set and adjacent point cloud data.

[0083] In one embodiment, the step of generating the tunnel axis continuous curve is performed using a cubic spline interpolation method.

[0084] 1. Determine the highest point set.

[0085] 2. Select adjacent point cloud data.

[0086] Select a certain number of adjacent point cloud data around the highest point. These points should be able to better reflect the trend of the tunnel axis in this area. It should be noted that the selection range and number are determined according to the actual situation. Generally speaking, select points within a certain distance from the highest point, or select a certain number of points according to the density of the point cloud.

[0087] Cubic spline interpolation is a method for constructing a smooth curve through a series of known data points. For a given set of data points (xi,yi), where i = 0,1,2,...,n, the cubic spline interpolation function S(x) satisfies the following conditions.

[0088] 1) On each subinterval [xi,xi+1], S(x) is a cubic polynomial.

[0089] 2) S(xi)=yi, that is, the value of the interpolation function at the known data point is equal to the given data value.

[0090] 3) S(x), S'(x) and S''(x) are continuous over the entire interval.

[0091] A. Divide the intervals.

[0092] Sort the selected highest point and adjacent point cloud data according to the value of the horizontal coordinate (or other appropriate parameters).

[0093] Using these points as nodes, the interval is divided into several sub-intervals.

[0094] B. Establish a system of equations.

[0095] For each subinterval [xi,xi+1], let the cubic spline interpolation function be Si(x)=ai+bi(x-xi)+ci(x-xi)^2+di(x-xi)^3.

[0096] According to the conditions of cubic spline interpolation, the following set of equations (B1-B4) is established.

[0097] B1. Interpolation conditions: Si(xi)=yi and Si(xi + 1)=y(i + 1).

[0098] B2. First-order derivative continuity condition: S'i -1 (xi)=S'i(xi).

[0099] B3. Second-order derivative continuity condition: S''i -1 (xi)=S''i(xi).

[0100] B4. Boundary conditions: Select appropriate boundary conditions according to actual conditions, such as natural boundary conditions (the second-order derivative at the two end points is zero), fixed boundary conditions, etc.

[0101] C. Solve the system of equations.

[0102] Write the system of equations in matrix form and obtain the coefficients ai, bi, ci, di by solving the linear system of equations. Use numerical methods such as Gaussian elimination and LU decomposition to solve the linear system of equations.

[0103] D. Generate a continuous curve.

[0104] For a given abscissa value x, determine the subinterval [xi,xi + 1] in which it lies.

[0105] Substitute x into the corresponding cubic spline interpolation function Si(x) and calculate the ordinate value y.

[0106] This process is repeated, and for a series of abscissa values, corresponding ordinate values ​​are obtained, thereby generating a continuous curve of the tunnel axis.

[0107] For example, suppose there is a highest point (x0,y0) and four adjacent points (x1,y1),(x2,y2),(x3,y3),(x4,y4).

[0108] First, divide the interval into four subintervals: [x0,x1], [x1,x2], [x2,x3], and [x3,x4].

[0109] Then, a set of equations is established according to the interpolation conditions, derivative continuity conditions and boundary conditions, and the coefficients of the cubic spline interpolation function of each subinterval are solved.

[0110] Finally, for a given range of abscissas, a continuous curve of the tunnel axis is generated.

[0111] Therefore, through step S140, using, for example, cubic spline interpolation method, a continuous curve of the tunnel axis is generated according to the highest point and adjacent point cloud data. The curve has high smoothness and accuracy and can better reflect the actual shape of the tunnel axis.

[0112] In addition, in order to ensure the accuracy of the continuous tunnel axis, the embodiment of the present invention also verifies the continuous tunnel axis generated in step S140. Step S150 (not shown) verifies the fitted continuous tunnel axis and evaluates the accuracy and reliability of the fitting by comparing it with the tunnel design profile.

[0113] 1. Collect tunnel design information.

[0114] 1. Obtain design drawings and parameters.

[0115] Collect the design drawings of the tunnel, including plan layout, section drawings, etc., to clarify the design outline, dimensions and theoretical position of the axis of the tunnel. Also, record the key parameters of the tunnel design, such as tunnel length, width, height, curvature radius, etc.

[0116] 2. Extract the fitted tunnel axis features.

[0117] 1. Axis parameter measurement.

[0118] Determine the key parameters of the fitted continuous tunnel axis, such as the coordinates of the starting and ending points of the axis, the length and curvature of the axis, etc.

[0119] 2. Axis point sampling.

[0120] A certain number of points are uniformly selected on the fitted continuous tunnel axis for comparison with the design profile. These points should cover different sections of the tunnel.

[0121] 3. Compare with the tunnel design profile.

[0122] 1. Plane position comparison.

[0123] Project the points selected on the continuous tunnel axis into the plane coordinate system of the tunnel design; measure the distance deviation between these points and the design axis on the plane. If the deviation is within the preset error tolerance range, it means that the plane position fitting is relatively accurate; if the deviation exceeds the preset error tolerance range, further analysis of the cause is required.

[0124] 2. Elevation comparison.

[0125] Determine the elevation values ​​of the points selected on the continuous tunnel axis and compare them with the elevation of the design contour at the corresponding position; calculate the elevation deviation and evaluate the accuracy of the fitted axis in the elevation direction.

[0126] 3. Curvature comparison.

[0127] For the curved tunnel on the continuous tunnel axis, the curvature of the fitted axis is calculated and compared with the designed curvature; the difference in curvature is measured by calculating the radius of the fitted curve or the radius of curvature. If the difference is within the preset difference allowable range, it means that the fitted curvature is relatively accurate.

[0128] Fourth, evaluate the accuracy and reliability of the fit.

[0129] 1. Statistical analysis.

[0130] The deviations of all selected points on the continuous tunnel axis are statistically analyzed to calculate the average deviation, standard deviation and other indicators. Smaller average deviation and standard deviation indicate that the fitting result is closer to the design profile and has higher accuracy.

[0131] 2. Visual inspection.

[0132] The fitted continuous tunnel axis and the designed outline are simultaneously drawn in the graphics software for visual comparison. The overlap between the fitted axis and the designed outline, as well as the differences in different sections, can be intuitively observed.

[0133] 3. Reliability assessment.

[0134] Consider the uncertainty factors in data acquisition and processing, such as measurement errors, noise effects, etc.

[0135] By repeatedly measuring and performing fitting operations according to steps S110 to S140, the stability and reliability of the results are evaluated. If the results are relatively consistent, it means that the fitting method has high reliability.

[0136] For example, suppose there is a circular tunnel with a design radius (R). Select n points on the fitted axis and measure the distance (di) from these points to the design center (i = 1,2,...,n). Calculate the average deviation and standard deviation. If the average deviation and standard deviation are small (usually less than 5mm), it means that the fitting accuracy is high. At the same time, the deviation of the axis from the design contour can be intuitively seen through visual inspection to further evaluate the reliability of the fitting.

[0137] Therefore, through the above step S150, the fitted continuous tunnel axis is comprehensively verified, the accuracy and reliability of the fitting are evaluated, and a reliable basis is provided for the design, construction and monitoring of the tunnel project.

[0138] On the other hand, based on the above tunnel axis height determination method, an embodiment of the present invention further provides a system for determining the tunnel point cloud axis height (also referred to as a "tunnel axis height determination system"). The tunnel axis height determination system is used to implement the above tunnel axis height determination method.

[0139] Figure 2 Schematic diagram of the structure of the system for determining the height of the tunnel point cloud axis according to an embodiment of the present application. Figure 2 As shown, the tunnel axis height determination system described in the embodiment of the present invention includes: a point cloud data preprocessing module 21, a contour fitting module 22, a highest point extraction module 23 and a tunnel axis generation module 24.

[0140] In one embodiment, the point cloud data preprocessing module 21 is implemented according to the steps and methods described in the above step S110, and is configured to obtain the original point cloud data of the tunnel and preprocess it; the contour fitting module 22 is implemented according to the steps and methods described in the above step S120, and is configured to perform tunnel contour fitting on the preprocessed point cloud data; the highest point extraction module 23 is implemented according to the steps and methods described in the above step S130, and is configured to extract the highest point representing the tunnel axis from the tunnel contour fitting result to form a highest point space set; the tunnel axis generation module 24 is implemented according to the steps and methods described in the above step S130, and is configured to form a continuous tunnel axis based on the highest point space set.

[0141] The present invention discloses a method and system for determining the height of a tunnel point cloud axis. The method and system provide an automated laser radar tunnel point cloud data processing solution, which can quickly and accurately determine the height of the tunnel axis. The present invention has a high degree of automation, reduces manual intervention, improves the efficiency of data processing, and improves the accuracy of determining the height of the tunnel axis through a fitting algorithm. In addition, the present invention is also applicable to tunnels of different types and sizes, and has good versatility and adaptability.

[0142] The above is only a preferred specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by any person familiar with the technology within the technical scope disclosed by the present invention should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

[0143] In the description of the present invention, unless otherwise specified, "plurality" means two or more than two; the orientations or positional relationships indicated by the terms "upper", "lower", "left", "right", "inner", "outer", "front end", "rear end", "head", "tail", etc. are based on the orientations or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operate in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, the terms "first", "second", "third", etc. are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0144] In the description of the present invention, it should be noted that, unless otherwise clearly specified and limited, the terms "connected" and "connection" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0145] It should be understood that the embodiments disclosed in the present invention are not limited to the specific structures, processing steps or materials disclosed herein, but should be extended to equivalent substitutions of these features understood by ordinary technicians in the relevant field. It should also be understood that the terms used herein are only used for the purpose of describing specific embodiments and are not meant to be limiting.

[0146] The "one embodiment" or "embodiment" mentioned in the specification means that the specific features, structures or characteristics described in conjunction with the embodiment are included in at least one embodiment of the present invention. Therefore, the phrases "one embodiment" or "embodiment" appearing in various places throughout the specification do not necessarily refer to the same embodiment.

[0147] Although the embodiments disclosed in the present invention are as above, the contents described are only embodiments adopted for facilitating the understanding of the present invention and are not intended to limit the present invention. Any technician in the technical field to which the present invention belongs can make any modifications and changes in the form and details of the implementation without departing from the spirit and scope disclosed in the present invention, but the patent protection scope of the present invention shall still be subject to the scope defined in the attached claims.

Claims

1. A method for determining the height of a tunnel point cloud axis, characterized in that: include: Obtain the original point cloud data of the tunnel and pre-process it; Perform tunnel contour fitting on the preprocessed point cloud data; Extract the highest point representing the tunnel axis from the tunnel profile fitting result to form a spatial set of highest points; Based on the spatial set of highest points, a continuous tunnel axis is formed.

2. The method according to claim 1, characterized in that The steps of preprocessing the original point cloud data include: Collect tunnel design parameters and determine the elevation range of the point cloud representing the tunnel bottom surface; According to the original point cloud data, using the bottom elevation range, the original point cloud data is preliminarily classified into tunnel wall points and tunnel bottom points; According to the initially classified point cloud points, combined with the spatial distribution characteristics of the points and the degree of elevation change of the local area to which they belong, the initially classified point cloud data is secondary classified to obtain intermediate point cloud data marked with the classification results, so as to use the intermediate point cloud data to fit the tunnel contour.

3. The method according to claim 2, characterized in that The steps of preprocessing the original point cloud data also include: The classification result of the intermediate point cloud data is corrected by analyzing the consistency between the type of each point in the intermediate point cloud data and the point type of the point neighborhood, and the corrected point cloud data is obtained, so as to use the corrected point cloud data to perform tunnel contour fitting.

4. The method according to claim 2, characterized in that: The step of fitting the tunnel contour to the intermediate point cloud data includes: Segmenting the intermediate point cloud data to identify the overall contour geometry, tunnel surface change trend characteristics, and geological characteristics of the tunnel crossing area of ​​each point cloud data segment; According to the overall contour geometry of each point cloud data segment, the tunnel surface change trend characteristics and the geological characteristics of the tunnel crossing area, a suitable fitting algorithm is selected to perform tunnel contour fitting on each point cloud data segment; Integrate the fitting results of each segment.

5. The method according to claim 4, characterized in that The tunnel surface trend change characteristics include flatness change, curvature change and deformation characteristics.

6. The method according to claim 4, characterized in that The steps of selecting a suitable fitting algorithm include: For the point cloud data segments that belong to straight line segments and flat tunnel surfaces, a geometric shape fitting algorithm based on the RANSAC algorithm is used to fit the tunnel contour; For the point cloud data segments belonging to the curve segments, spline curve fitting or polynomial fitting algorithm is used to fit the tunnel contour; For point cloud data segments with local deformation, unevenness or geological anomalies, an overall fitting algorithm is used to determine the approximate contour, and then a local fitting algorithm is used to correct the local deformation area in the approximate contour fitting result to obtain the tunnel contour fitting result. The overall fitting algorithm is a geometric shape fitting algorithm, and the local fitting algorithm is a spline curve fitting algorithm or a polynomial fitting algorithm.

7. The method according to any one of claims 4 to 6, characterized in that The step of extracting the highest point representing the tunnel axis from the tunnel profile fitting result to form a spatial set of the highest points includes: The highest point of the curve is extracted by analyzing the first-order derivative and the second-order derivative of each cross-section curve in the tunnel profile fitting result, and the highest point space set is obtained.

8. The method according to claim 7, characterized in that The step of extracting the highest point of the curve by analyzing the first-order derivative and the second-order derivative of each cross-section curve in the tunnel profile fitting result includes: Extract at least one initial extreme point according to the first-order derivative of the current cross-section curve; Substituting the at least one initial extreme value point into the second-order derivative of the current cross-section curve to obtain a function value of the at least one initial extreme value point; The highest point of the current cross-section curve is determined according to the function value of the at least one initial extreme point.

9. The method according to claim 8, characterized in that The step of determining the highest point of the current cross-section curve according to the function value of the at least one initial extreme point includes: Selecting a point whose function value is less than zero from the at least one initial extreme value point as a candidate point cloud point; If there are multiple candidate point cloud points, the point cloud point with the largest elevation value is selected as the highest point. If there is only one candidate point cloud point, the candidate point cloud point is selected as the highest point.

10. The method according to claim 7, characterized in that The step of forming a continuous tunnel axis based on the highest point space set comprises: Determine the adjacent point cloud data of each highest point in the highest point space set according to a preset range; Sort all the highest points and their corresponding adjacent point cloud data according to the coordinate values ​​in the axial direction of the tunnel to form a continuous sequence; Spline interpolation is used to perform curve fitting on the interval formed by each pair of adjacent element points in the continuous sequence to form the continuous tunnel axis.

11. A system for determining the height of a tunnel point cloud axis, characterized in that: include: A point cloud data preprocessing module configured to obtain original point cloud data of the tunnel and preprocess the original point cloud data; A contour fitting module configured to perform tunnel contour fitting on the preprocessed point cloud data; A highest point extraction module configured to extract the highest point representing the tunnel axis from the tunnel profile fitting result to form a highest point space set; The tunnel axis generation module is configured to form a continuous tunnel axis based on the highest point space set.

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