Segment roundness measurement method and system based on two-dimensional laser radar

Abstract

The invention belongs to but not limited to the technical field of tunnel engineering, and particularly relates to a segment roundness measurement method and system based on a two-dimensional laser radar, and the method comprises the steps: S1, obtaining the point cloud data of a tunnel pipe wall under a polar coordinate system through the laser radar; s2, performing random noise filtering on the point cloud data and converting the point cloud data into a Cartesian coordinate system; s3, useless point cloud data generated by sundries such as pipelines and tracks on the pipe wall are filtered out; and S4, projecting the point cloud data to a two-dimensional plane, carrying out ellipse fitting on the filtered point cloud data to fit an ellipse contour of the section of the tunnel, and calculating a long axis and a short axis of the ellipse contour according to an ellipse curve equation so as to calculate the roundness of the tunnel. According to the invention, the laser radar is installed on the segment assembling machine for scanning, the installation of a redundant scanning device is avoided, the influence of a dust environment and sundries on scanning data is avoided to the greatest extent through the annular rotation of the segment assembling machine, and the operability and robustness of the scanning method are greatly improved.

Description

Technical Field

[0001] The present invention belongs to but is not limited to the field of tunnel engineering technology, and in particular relates to a method and system for measuring the roundness of a pipe segment based on a two-dimensional laser radar. Background Art

[0002] Due to its high degree of automation, fast excavation speed and high safety, the shield method has been widely used in modern tunnel construction projects. Shield tunnels can be divided into circular, arched, rectangular and horseshoe-shaped sections. Among them, the circular structure lining is the most widely used due to its advantages such as simple assembly, convenient replacement and strong resistance to earth pressure. During the construction of a circular shield tunnel, multiple prefabricated wedge-shaped segments are assembled into a ring structure (called a tube ring), which is connected to each other at different positions to form tunnels of different shapes. However, while this construction method brings convenience to tunnel construction, it also faces some quality control issues that cannot be ignored. Theoretically, the cross section of each tube ring should be circular. However, due to factors such as dimensional deviation of the tube segments during production and inaccurate positioning during installation, the assembled tube rings will be elliptical rather than circular. This deformation will seriously threaten the service life of the tunnel, so it should be discovered early so that appropriate measures can be taken to prevent its deterioration. In tunnel engineering, the difference between the major axis and the minor axis of the ellipse fitted to the tunnel cross section is defined as the true roundness (also called ellipticity), which is used to quantify this deformation. Timely and high-precision measurement of the true roundness of each ring of segments is of great significance for guiding the assembly quality control and subsequent maintenance of the next ring of segments.

[0003] There are four main methods for scanning the true roundness of tunnels. The manual method is simple and direct. After each segment is spliced, the shield tail gap is manually measured, and the true roundness is estimated by sampling several different axes. This method is simple and direct, and has low environmental requirements, but due to measurement and accidental errors, the reliability of the results is not high. The total station scanning method uses a high-precision optical instrument such as a total station to measure the horizontal angle, vertical angle and slant distance, thereby performing a three-dimensional scan of the tunnel interface, and finally generating a three-dimensional model or cross-sectional diagram, which is analyzed by software to determine the true roundness. This method requires the station to be changed as the shield machine moves forward, which is cumbersome. Each measurement takes too long, affecting the excavation efficiency. The laser scanning method uses a two-dimensional or three-dimensional laser radar to quickly obtain the three-dimensional coordinate information of the tunnel ring segment and generate high-precision three-dimensional point cloud data to determine the true roundness. However, the three-dimensional laser radar is expensive, so in actual measurement, two-dimensional laser radar is often used for circular scanning to achieve measurement. The two-dimensional mobile scanning method uses laser radar with a mobile measuring vehicle to continuously measure the tunnel. However, this method requires laying tracks, which takes up a lot of space, and the scanning results are not timely.

[0004] Through real-time monitoring of tunnel deformation, structural problems can be identified in a timely manner, safety hazards can be prevented, and the long-term stability and safety of the tunnel can be ensured. It can also improve construction efficiency and effectiveness, reduce the difficulty of workers' operations, and provide experience and reference for future tunnel engineering construction.

[0005] In view of the above analysis, the technical problems that urgently need to be solved in the existing technology are: the two-dimensional mobile scanning method uses laser radar assisted by a mobile measuring vehicle to continuously measure the tunnel, but this method requires laying tracks, occupies a large space, and the scanning results are not timely. Summary of the invention

[0006] In view of the problems existing in the prior art, the present invention provides a method and system for measuring the roundness of a pipe segment based on a two-dimensional laser radar, which can realize timely scanning of each ring of the pipe segment and improve the efficiency of the tunnel engineering.

[0007] The present invention is implemented as follows: a method for measuring the roundness of a pipe segment based on a two-dimensional laser radar, comprising:

[0008] S1: Use laser radar to obtain tunnel wall point cloud data in polar coordinate system;

[0009] S2: Perform random noise filtering on the point cloud data and convert it to a Cartesian coordinate system;

[0010] S3: Filter out useless point cloud data generated by pipelines, tracks and other debris on the pipe wall;

[0011] S4: Project the point cloud data onto a two-dimensional plane, perform ellipse fitting on the filtered point cloud data, fit the ellipse profile of the tunnel cross section, calculate its major and minor axes according to the ellipse curve equation, and then calculate the true roundness of the tunnel.

[0012] Further, in step S1, obtaining tunnel wall point cloud data using laser radar means:

[0013] After completing the assembly of a ring of segments, the segment assembly machine is moved to the middle of the segment. The segment assembly machine is used to drive the lidar to rotate at a uniform speed to obtain a number of lidar cross-sectional data points (r, Φ) and the corresponding encoder rotation angle θ, which are combined to obtain the tunnel wall point cloud data in the polar coordinate system.

[0014] Further, in step S2, the point cloud data random noise filtering and coordinate space conversion refers to:

[0015] Step S2.1: In polar coordinate space, perform multi-layer wavelet decomposition on the point cloud data to obtain the corresponding approximate coefficients and detail coefficients, then set the detail coefficients to zero and reconstruct the point cloud data to filter out the random noise caused by the uneven roughness of the measured surface and the measurement error of the lidar itself.

[0016] Step S2.2: Convert the point cloud data from polar coordinate space to three-dimensional coordinate space. Assume that the polar coordinates of each data point are (r, θ, Φ) and the rectangular coordinates are (x, y, z), where r represents the distance value of the scanning point, θ represents the scanning deflection angle of the laser radar, and Φ represents the rotation angle of the segment assembly machine. The corresponding relationship is as follows:

[0017]

[0018] Further, the specific steps of step S2.1 are as follows:

[0019] Step S2.1.1 selects db8 wavelet as the mother wavelet and sets the decomposition level to 3.

[0020] Step S2.1.2 initializes the wavelet filters, which are a low-pass filter and a high-pass filter, respectively. These filters are obtained by the scale function and the wavelet function of the wavelet function.

[0021] The decomposition formula of low-pass filter a[n] is:

[0022]

[0023] High-pass filter d[n] decomposition formula:

[0024]

[0025] Step S2.1.3 decomposes the point cloud data and calculates the approximate coefficient and the detail coefficient respectively.

[0026] Calculation of the approximate coefficient cA:

[0027]

[0028] Calculation of detail coefficient cD:

[0029]

[0030] Step S2.1.4 repeats the decomposition process.

[0031] Step S2.1.5 sets the detail coefficient to 0 and reconstructs the point cloud data.

[0032] Approximate coefficient reconstruction:

[0033]

[0034] Detail coefficient reconstruction:

[0035]

[0036] Further, in step S3, removing outliers in the point cloud data according to the tunnel section includes the following steps:

[0037] Step S3.1: Calculate the Gaussian curvature of each data point in the point cloud, sort the curvature change rate from large to small, set a threshold and set the larger 20% data points as initial outliers.

[0038] Step S3.2: Convert the point cloud data into a bool property matrix according to whether it is an initial outlier point, where non-outlier points are set to 0 and outlier points are set to 1. Perform several consecutive morphological erosion and dilation operations on the property matrix, and determine the point cloud data corresponding to the part of the property matrix that is 1 as the final outlier point, and filter it out.

[0039] Further, the specific steps of step S3.1 are as follows:

[0040] Step S3.1.1 For each point p in the point cloud i , find the distance point p i The nearest k points, get the domain point set N(p i ).

[0041] Step S3.1.2 For point p i The least squares plane is fitted to the neighborhood points.

[0042] Through the domain point set N(p i ) Calculate the covariance matrix C:

[0043]

[0044] Perform eigenvalue decomposition on the covariance matrix C to obtain eigenvalues ​​and eigenvectors. The eigenvector of the minimum eigenvalue pair is point p i The normal vector n i .

[0045] Step S3.1.3 uses the normal vector and the domain point to calculate the principal curvature. Project the domain point onto the fitting plane and perform quadratic surface fitting on the projected two-dimensional point set:

[0046] z=ax 2 +by 2 +cxy+dx+ey+f

[0047] Calculate the principal curvature based on the quadratic surface fitting coefficients a, b, c:

[0048]

[0049] Step S3.1.4 Based on the principal curvature k 1 and k 2 , and get the Gaussian curvature K of each point.

[0050] K=k 1 ·k 2

[0051] Further, in step S4, performing ellipse fitting on the filtered point cloud data includes the following steps:

[0052] Step S4.1: Perform eigenvalue decomposition on the covariance matrix of the point cloud data through PCA principal component analysis, cross-product the two eigenvectors with larger eigenvalues, and regard the obtained unit vector as the tunnel axis direction vector. Project the three-dimensional point cloud onto the two-dimensional plane according to the tunnel axis direction vector.

[0053] Step S4.2: Use the least squares method to fit the projected point cloud data into an elliptic curve. The least squares parameter matrix is:

[0054]

[0055] To minimize the fitting error, the cost function can be expressed as:

[0056] min||Da|| 2

[0057] where a is a vector [A,B,C,D,E,F] containing the parameters of the ellipse that satisfy the general formula for the ellipse equation:

[0058] Ax 2 +Bxy+Cy 2 +Dx+Ey+F=0

[0059] According to the fitted ellipse equation A * x 2 +B * xy+C * y 2 +D * x+E * y+F * = 0, and the major axis l and minor axis s of the tunnel cross-section profile are obtained. According to the theoretical radius D of the tunnel cross-section, the true roundness of the tunnel is calculated:

[0060] γ=2(ls) / D

[0061] Another object of the present invention is to provide a segment roundness measurement system based on two-dimensional laser radar for realizing the segment roundness measurement method based on two-dimensional laser radar, comprising a PC, a laser radar, a segment assembly machine and an encoder thereof;

[0062] The laser radar is fixed on the counterweight block of the segment assembly machine, and its measurement plane coincides with the rotation axis of the segment assembly machine; the PC end realizes TCP communication with the laser radar and the segment assembly machine to ensure that the data points collected by the laser radar and the encoder of the segment assembly machine can be sent to the PC end in real time.

[0063] Another object of the present invention is to provide a computer device, which includes a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the method for measuring the roundness of a pipe segment based on two-dimensional laser radar.

[0064] Another object of the present invention is to provide a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, the processor executes the steps of the method for measuring the roundness of a pipe segment based on two-dimensional laser radar.

[0065] In combination with the above technical solutions and the technical problems solved, the advantages and positive effects of the technical solutions to be protected by the present invention are as follows:

[0066] First, the present invention avoids the installation of redundant scanning devices by installing the laser radar on the segment assembly machine for scanning. By relying on the circumferential rotation of the segment assembly machine, the influence of dust environment and debris on the scanning data is avoided to the greatest extent, which greatly improves the operability and robustness of the scanning method.

[0067] The present invention improves the quality of the collected point cloud by wavelet transform random noise filtering, and the outlier point filtering accurately eliminates the outliers that are not on the tunnel wall, thereby improving the accuracy and reliability of the measurement results.

[0068] The present invention can complete the measurement of the true roundness of pipe segments at different diameters. By using high-precision laser radar and accurate filtering methods, various splicing indicators of the pipe segments can be detected without contact, thereby ensuring the splicing quality of the tunnel segments. Compared with the existing measurement methods, the labor cost required for tunnel monitoring is significantly reduced.

[0069] Second, as auxiliary evidence of the inventiveness of the claims of the present invention, it is also reflected in the following important aspects:

[0070] (1) The expected benefits and commercial value of the technical solution of the present invention after transformation are:

[0071] The application of this technology in tunnel construction has significantly improved the construction quality, ensured that the project is highly consistent with the design standards, effectively reduced the cost of rework and repair, and comprehensively improved the efficiency and quality level of the overall project. At the same time, accurate roundness measurement not only enhances the safety of the tunnel structure, but also accurately identifies potential structural hazards, takes precautions before they occur, and effectively avoids accident risks. The application of this technology can effectively reduce measurement errors, improve construction accuracy, and thus reduce material loss and construction delays.

[0072] High-precision segment roundness data is of great significance to tunnel engineering. It not only provides a key basis for optimizing tunnel design and construction technology, but also lays a solid data foundation for subsequent operation and maintenance management. From an environmental protection perspective, this technology reduces material waste and energy consumption, effectively promotes the construction process to comply with environmental protection regulations, and has both economic and environmental benefits.

[0073] (2) The technical solution of the present invention fills the technical gap in the industry at home and abroad:

[0074] In the field of tunnel engineering at home and abroad, the measurement of the true roundness of the pipe segment usually relies on a 3D laser scanner. However, this measurement method has obvious limitations, and its equipment cost is high, which greatly reduces the economic efficiency of the overall measurement. Therefore, it is urgent to develop a method that is both economical and can quickly and in real time measure the true roundness of the pipe segment.

[0075] The present invention innovatively uses a two-dimensional laser radar as the measurement core, finding a balance between measurement accuracy and instrument price. Compared with a three-dimensional laser scanner, a two-dimensional laser radar has significant advantages in cost control, and its measurement accuracy can meet the standards of engineering requirements. In addition, through a matching professional software system, it can realize the real-time measurement function of the true roundness of the pipe segment, effectively improving the efficiency and timeliness of the measurement work, and providing a new and cost-effective solution for the true roundness measurement of the pipe segment in tunnel engineering.

[0076] (3) The technical solution of the present invention solves the technical problems that people have been eager to solve but have never been able to solve successfully:

[0077] It is a major demand to quickly and in real time measure the roundness on the production line to ensure product quality. The current technology has a trade-off between speed and accuracy. The present invention does not affect the normal advancement of the shield machine, and can quickly complete the real-time segment measurement in the splicing gap with an accuracy of up to millimeter level, which has a good application prospect.

[0078] Third, the present invention relates to a method for measuring the roundness of a tunnel segment based on a two-dimensional laser radar. The laser radar is used to obtain the pipe wall point cloud data and perform random noise filtering and coordinate conversion. After eliminating invalid data caused by debris, the ellipse fitting technology is used to calculate the major and minor axes of the tunnel section, thereby accurately evaluating the roundness of the tunnel segment. This method overcomes the problems of low accuracy and complex data processing in traditional measurement methods, and can be widely used in tunnel construction quality inspection and segment assembly accuracy evaluation.

[0079] The present invention collects multiple sets of cross-sectional data using a rotating laser radar after the segment is assembled, and converts it into point cloud data in a polar coordinate system to achieve high-precision data collection of the tunnel wall. The point cloud data is filtered by combining multi-layer wavelet decomposition, which can effectively remove random noise caused by laser radar measurement errors and uneven surface roughness of the wall, significantly improving the reliability and accuracy of the measurement data.

[0080] In the process of denoising and cleaning point cloud data, the present invention effectively identifies and removes outliers caused by pipelines, tracks and other debris from point cloud data based on Gaussian curvature calculation and morphological filtering methods. By sorting the point cloud data by Gaussian curvature and performing continuous morphological operations, outliers can be accurately located and filtered out, ensuring the integrity and accuracy of tunnel section data, thereby providing high-quality input data for subsequent ellipse fitting.

[0081] The present invention significantly improves the measurement accuracy and efficiency of the roundness of tunnel segments, and effectively overcomes the problems of large measurement errors and weak anti-interference ability in the prior art by using algorithms such as wavelet filtering and curvature screening. It further promotes the development of tunnel engineering quality monitoring technology by combining precise data processing models and mathematical algorithms, provides technical support for the intelligent and refined management of tunnel construction, and has important engineering application value and economic benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0082] Figure 1 is a flow chart of a method for measuring the true roundness of a tunnel based on a two-dimensional laser radar provided in an embodiment of the present invention;

[0083] Figure 2 Schematic diagram of a method for measuring the roundness of a tunnel based on a two-dimensional laser radar provided in an embodiment of the present invention;

[0084] Figure 3 is a structural diagram of a tunnel roundness measurement system based on a two-dimensional laser radar provided in an embodiment of the present invention;

[0085] In the picture: 1. Segment hoist; 2. Segment assembly machine; 3. Counterweight; 4. LiDAR.

[0086] Figure 4This is a segment scanning effect diagram of a tunnel roundness measurement system based on a two-dimensional laser radar provided in an embodiment of the present invention;

[0087] Figure 5 This is a diagram of the interface of supporting software for a tunnel roundness measurement system based on a two-dimensional laser radar provided in an embodiment of the present invention;

[0088] Figure 6 , 7 This is a random noise filtering effect diagram of a tunnel roundness measurement system based on a two-dimensional laser radar provided in an embodiment of the present invention;

[0089] Figure 8 It is a distribution diagram of Gaussian curvature change of point cloud of a tunnel roundness measurement system based on two-dimensional laser radar provided in an embodiment of the present invention;

[0090] Fig. 9 This is a diagram of the final filtering effect of the point cloud of the tunnel roundness measurement system based on two-dimensional laser radar provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0091] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0092] like Figure 1 , Figure 2 As shown, a method for measuring the true roundness of a tunnel based on a two-dimensional laser radar comprises the following steps:

[0093] Step S1: using laser radar to obtain tunnel wall point cloud data in a polar coordinate system.

[0094] Step S2: Perform random noise filtering on the point cloud data and convert it into a Cartesian coordinate system.

[0095] Step S3: Filter out useless point cloud data generated by debris such as pipelines and tracks on the pipe wall.

[0096] Step S4: Project the point cloud data onto a two-dimensional plane, perform ellipse fitting on the filtered point cloud data, fit the ellipse contour of the tunnel section, calculate its major and minor axes according to the ellipse curve equation, and then calculate the true roundness of the tunnel.

[0097] In step S1, using laser radar to obtain tunnel wall point cloud data refers to:

[0098] After completing the assembly of a ring of segments, the segment assembly machine 2 is moved to the middle of the segment, and the segment assembly machine 2 is used to drive the laser radar 4 to rotate at a constant speed to obtain a number of laser radar 4 cross-sectional data points (r, φ) and the corresponding encoder rotation angle θ, which are combined to obtain the tunnel wall point cloud data in the polar coordinate system.

[0099] In step S2, the point cloud data random noise filtering and coordinate space conversion refers to:

[0100] Step S2.1: In polar coordinate space, perform multi-layer wavelet decomposition on the point cloud data, obtain the corresponding approximate coefficients and detail coefficients, set the detail coefficients to zero and reconstruct the point cloud data to filter out random noise caused by uneven surface roughness and measurement errors of the laser radar 4 itself.

[0101] The specific steps of step S2.1 are as follows:

[0102] Step S2.1.1 selects db8 wavelet as the mother wavelet and sets the decomposition level to 3.

[0103] Step S2.1.2 initializes the wavelet filters, which are a low-pass filter and a high-pass filter, respectively. These filters are obtained by the scale function and the wavelet function of the wavelet function.

[0104] Low-pass filter decomposition formula:

[0105]

[0106] High pass filter decomposition formula:

[0107]

[0108] Step S2.1.3 decomposes the point cloud data and calculates the approximate coefficient and the detail coefficient respectively.

[0109] Calculation of the approximate coefficient cA:

[0110]

[0111] Calculation of detail coefficient cD:

[0112]

[0113] Step S2.1.4 repeats the decomposition process.

[0114] Step S2.1.5 sets the detail coefficient to 0 and reconstructs the point cloud data.

[0115] Approximate coefficient reconstruction:

[0116]

[0117] Detail coefficient reconstruction:

[0118]

[0119] Step S2.2: Convert the point cloud data from polar coordinate space to three-dimensional coordinate space. Assume that the polar coordinates of each data point are (r, φ, θ) and the rectangular coordinates are (x, y, z), where r represents the distance value of the scanning point, Φ represents the scanning deflection angle of the laser radar 4, and θ represents the rotation angle of the segment assembly machine 2. The following corresponding relationship exists:

[0120]

[0121] In step S3, removing outliers in the point cloud data according to the tunnel section includes the following steps:

[0122] Step S3.1: Calculate the Gaussian curvature of each data point in the point cloud, sort the curvature change rate from large to small, set a threshold and set the larger 20% data points as initial outliers.

[0123] The specific steps of step S3.1 are as follows:

[0124] Step S3.1.1 For each point p in the point cloud i , find the distance point p i The nearest k points, get the domain point set N(p i ).

[0125] Step S3.1.2 For point p i The least squares plane is fitted to the neighborhood points.

[0126] Through the domain point set N(p i ) Calculate the covariance matrix C:

[0127]

[0128] Perform eigenvalue decomposition on the covariance matrix C to obtain eigenvalues ​​and eigenvectors. The eigenvector of the minimum eigenvalue pair is point p i The normal vector n i .

[0129] Step S3.1.3 uses the normal vector and the domain point to calculate the principal curvature. Project the domain point onto the fitting plane and perform quadratic surface fitting on the projected two-dimensional point set:

[0130] z=ax 2 +by 2 +cxy+dx+ey+f

[0131] Calculate the principal curvature based on the quadratic surface fitting coefficients a, b, c:

[0132]

[0133] Step S3.1.4 Based on the principal curvature k 1 and k 2 , and get the Gaussian curvature of each point.

[0134] K=k 1 ·k 2

[0135] Step S3.2: Convert the point cloud data into a bool property matrix according to whether it is an initial outlier point, where non-outlier points are set to 0 and outlier points are set to 1. Perform several consecutive morphological erosion and dilation operations on the property matrix, and determine the point cloud data corresponding to the part of the property matrix that is 1 as the final outlier point, and filter it out.

[0136] In step S4, performing ellipse fitting on the filtered point cloud data includes the following steps:

[0137] Step S4.1: Perform eigenvalue decomposition on the covariance matrix of the point cloud data through PCA principal component analysis, cross-product the two eigenvectors with larger eigenvalues, and regard the obtained unit vector as the tunnel axis direction vector. Project the three-dimensional point cloud onto the two-dimensional plane according to the tunnel axis direction vector.

[0138] The specific steps of step S4.1 are as follows

[0139] Step S4.1.1 calculates the mean of the three directions of the point cloud data (X, Y, Z), subtracts the mean from each point to make the mean of the data set zero, and then centers each point.

[0140]

[0141] The average variance in the z direction is:

[0142]

[0143] Taking the maximum value is the required projection direction:

[0144]

[0145] Step S4.1.2 Calculate the covariance matrix C and perform eigenvalue decomposition on the covariance matrix C Sort the eigenvalues ​​and eigenvectors, take the first two largest eigenvalues ​​and their corresponding eigenvectors, and form a new eigenvector matrix V. The principal component vectors are the columns of V. The first basis is the most representative direction of Xi, and the remaining bases are orthogonal to each other. Finally, the data is restored through decoding and decentralization:

[0146]

[0147] Step S4.2: Use the least squares method to fit the projected point cloud data into an elliptic curve.

[0148] The specific steps of step S4.2 are as follows:

[0149] Step S4.2.1 constructs a design matrix D, where each row corresponds to a data point and each column of the matrix is ​​a function of the parameters of the ellipse equation, including x2, xy, y2, x, y, and a constant term.

[0150]

[0151] Step S4..2 constructs a cost function, the goal is to minimize the fitting error, and the cost function can be expressed as:

[0152] min||Da|| 2

[0153] where a is a vector [A,B,C,D,E,F] containing the parameters of the ellipse that satisfy the equation of the ellipse:

[0154] Ax 2 +Bxy+Cy 2 +Dx+Ey+F=0

[0155] Step S4.2.3 In order to ensure that the fitting result is an ellipse rather than other conic sections, constraint C is introduced.

[0156]

[0157] Step S4.2.4 Solve the generalized eigenvalue problem (D T D) a = λCa, and obtain the optimal parameter vector a.

[0158] According to the fitted ellipse equation A * x 2 +B * xy+C * y 2 +D * x+E * y+F * = 0, and the major axis l and minor axis s of the tunnel cross-section profile are obtained. According to the theoretical radius D of the tunnel cross-section, the true roundness of the tunnel is calculated:

[0159] γ=2(ls) / D

[0160] like Figure 3As shown, a segment roundness measurement system for evaluating the assembly quality of shield tunnel segments is characterized in that the segment roundness measurement system includes a PC terminal, a laser radar 4, and a segment assembly machine 2;

[0161] The laser radar 4 is fixed on the counterweight block 3 of the segment assembling machine 2 , and its measuring plane coincides with the rotation axis of the segment assembling machine 2 .

[0162] The PC terminal realizes TCP communication with the laser radar 4 and the segment assembly machine 2, ensuring that the data points collected by the laser radar 4 and the encoder of the segment assembly machine 2 can be sent to the PC terminal in real time.

[0163] An application embodiment of the present invention provides a computer device, which includes a memory and a processor. The memory stores a computer program. When the computer program is executed by the processor, the processor executes the steps of a method for measuring the roundness of a pipe segment based on a two-dimensional laser radar.

[0164] An application embodiment of the present invention provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, the processor executes the steps of a method for measuring the roundness of a pipe segment based on a two-dimensional laser radar.

[0165] The present invention is used in the subway tunnel of T3 of Guangzhou Baiyun Airport in China, with a tunnel diameter of Φ14.31m. Experiments are carried out in the gap between each ring of segment assembly to ensure the authenticity of the experiment. The laser radar is installed on the segment assembly machine, and the communication between the PC, the laser radar and the splicing machine PLC is completed. A total of 13 scanning experiments were carried out, and 25 sets of data were collected from 14 rings of segments 392-413 as experimental data. The scanning frequency of the laser radar is 50Hz, and the rotation speed of the segment assembly machine is about 0.05rad / s when it rotates at a constant speed. In terms of efficiency, it only takes 2-3 minutes for the laser radar to measure a ring of segments and no station setting and other operations are required, which is much more efficient than the total station.

[0166] The original data scanned by the laser radar is generated in the polar coordinate system. After the coordinates are converted by the method described in step S1, the PCD file is generated and imported into the supporting software ( Figure 5 ), filtering operation is performed.

[0167] After the data is transformed by wavelet, the non-stationary jump degree of the point cloud is significantly reduced. In the figure below, Figure 6 In the figure, it represents the change of the distance value from the center of a certain scan line before and after the wavelet transform. Figure 7 Indicates the change in the degree of jitter of the point cloud scan line before and after filtering.

[0168] For outlier clusters such as bolt holes and pipes contained in the point cloud, the method described in step S3 is used to filter them out. Figure 8The curvature variation calculated in step S3.1, of which 69% of the points have a variation below 0.2. It can be considered that the variation of the information points is around 0.2, and the remaining points are marked as outliers. Fig. 9 This is the outlier filtering effect diagram.

[0169] In order to obtain the true value of the tunnel roundness, a total station is used to measure several pipe wall points around the middle section of each ring to fit the segment cross section.

[0170] The cross-sectional points of the total station and the filtered point cloud are fitted into an ellipse using the method described in the steps. The points on the tunnel ventilation duct and the scaffolding in the cross-sectional points of the total station are manually removed, and the major and minor axes of the remaining parts are calculated by the ellipse fitting method. The difference between the major and minor axes of the ellipse is considered to be the true value of the true roundness of the ring.

[0171] Table 4.1 Calculation results of true roundness of ring 392-413 segments

[0172]

[0173] Among them, the 395th ring data showed an error greater than the required range (Δ≥10mm) due to the uneven speed of the segment splicing machine operated by the workers, and the 413th ring did not collect comparative data, resulting in a large deviation in the calculation results. In general, after removing the problem points, the average roundness error of the 12 ring segments was 3.98mm, which is much smaller than 10mm. It can be seen that this tunnel roundness measurement system meets the engineering requirements in the construction environment of a shield tunnel with a diameter of Φ14.31m and can be further applied in the project.

[0174] 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.

[0175] It should be noted that the embodiments of the present invention can be implemented by hardware, software, or a combination of software and hardware. The hardware part can be implemented using dedicated logic; the software part can be stored in a memory and executed by an appropriate instruction execution system, such as a microprocessor or dedicated design hardware. It can be understood by a person of ordinary skill in the art that the above-mentioned devices and methods can be implemented using computer executable instructions and / or contained in a processor control code, such as a carrier medium such as a disk, CD or DVD-ROM, a programmable memory such as a read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. Such code is provided on the carrier medium. The device and its modules of the present invention can be implemented by hardware circuits such as very large-scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, etc., or programmable hardware devices such as field programmable gate arrays, programmable logic devices, etc., can also be implemented by software executed by various types of processors, and can also be implemented by a combination of the above-mentioned hardware circuits and software, such as firmware.

[0176] The above description is only a specific implementation mode of the present invention, but the protection scope of the present invention is not limited thereto. Any modifications, equivalent substitutions and improvements made by any technician familiar with the technical field within the technical scope disclosed by the present invention and within the spirit and principle of the present invention should be covered by the protection scope of the present invention.

Claims

1. A method for measuring the roundness of a pipe segment based on a two-dimensional laser radar, characterized in that: include: S1: Use laser radar to obtain tunnel wall point cloud data in polar coordinate system; S2: Perform random noise filtering on the point cloud data and convert it to a Cartesian coordinate system; S3: Filter out useless point cloud data generated by pipelines, tracks and other debris on the pipe wall; S4: Project the point cloud data onto a two-dimensional plane, perform ellipse fitting on the filtered point cloud data, fit the ellipse profile of the tunnel cross section, calculate its major and minor axes according to the ellipse curve equation, and then calculate the true roundness of the tunnel.

2. The method for measuring the roundness of a pipe segment based on a two-dimensional laser radar according to claim 1, characterized in that: In step S1, using laser radar to obtain tunnel wall point cloud data refers to: After completing the assembly of a ring of segments, the segment assembly machine is moved to the middle of the segment. The segment assembly machine is used to drive the lidar to rotate at a uniform speed to obtain a number of lidar cross-sectional data points (r, Φ) and the corresponding encoder rotation angle θ, which are combined to obtain the tunnel wall point cloud data in the polar coordinate system.

3. The method for measuring the roundness of a pipe segment based on a two-dimensional laser radar according to claim 1, characterized in that: In step S2, the point cloud data random noise filtering and coordinate space conversion refers to: Step S2.1: In polar coordinate space, perform multi-layer wavelet decomposition on the point cloud data, obtain the corresponding approximate coefficients and detail coefficients, set the detail coefficients to zero and reconstruct the point cloud data to filter out the random noise caused by the uneven roughness of the measured surface and the measurement error of the laser radar itself; Step S2.2: Convert the point cloud data from polar coordinate space to three-dimensional coordinate space; Assume that the polar coordinates of each data point are (r, θ, Φ) and the rectangular coordinates are (x, y, z), where r represents the distance value of the scanning point, θ represents the scanning deflection angle of the laser radar, and Φ represents the rotation angle of the segment assembly machine. The corresponding relationship is as follows:

4. The method for measuring the roundness of a pipe segment based on a two-dimensional laser radar according to claim 1, characterized in that: The specific steps of step S2.1 are as follows: Step S2.1.1: Select db8 wavelet as the mother wavelet and set the decomposition level to 3; Step S2.1.2 initializes the wavelet filters, which are a low-pass filter and a high-pass filter, respectively, and these filters are obtained by the scale function and the wavelet function of the wavelet function; Low-pass filter decomposition formula: High pass filter decomposition formula: Step S2.1.3 decomposes the point cloud data and calculates the approximate coefficient and the detail coefficient respectively; Calculation of the approximate coefficient cA: Calculation of detail coefficient cD: Step S2.1.4 repeats the decomposition process; Step S2.1.5 sets the detail coefficient to 0 and reconstructs the point cloud data; Approximate coefficient reconstruction: Detail coefficient reconstruction:

5. The method for measuring the roundness of a pipe segment based on a two-dimensional laser radar according to claim 1, characterized in that: In step S3, removing outliers in the point cloud data according to the tunnel section includes the following steps: Step S3.1: Calculate the Gaussian curvature of each data point in the point cloud, sort the curvature change rate from large to small, set a threshold and set the larger 20% data points as initial outliers; Step S3.2: Convert the point cloud data into a bool property matrix according to whether it is an initial outlier point, where non-outlier points are set to 0 and outlier points are set to 1; perform several consecutive morphological corrosion and expansion operations on the property matrix, and determine the point cloud data corresponding to the part of the property matrix that is 1 as the final outlier point, and filter it out.

6. The method for measuring the roundness of a pipe segment based on a two-dimensional laser radar according to claim 1, characterized in that: The specific steps of step S3.1 are as follows: Step S3.1.1 For each point p in the point cloud i , find the distance point p i The nearest k points, get the domain point set N(p i ); Step S3.1.2 For point p i The least squares plane is fitted to the neighborhood points of ; Through the domain point set N(p i ) Calculate the covariance matrix C: Perform eigenvalue decomposition on the covariance matrix C to obtain eigenvalues ​​and eigenvectors; the eigenvector of the minimum eigenvalue pair is the point p i The normal vector n i ; Step S3.1.3 calculates the principal curvature using the normal vector and the domain point; projects the domain point onto the fitting plane, and performs quadratic surface fitting on the projected two-dimensional point set: z=ax 2 +by 2 +cxy+dx+ey+f Calculate the principal curvature based on the quadratic surface fitting coefficients a, b, c: Step S3.1.4 obtains the Gaussian curvature of each point based on the principal curvatures k1 and k2; K=k1·k2.

7. The method for measuring the roundness of a pipe segment based on a two-dimensional laser radar according to claim 1, characterized in that: In step S4, performing ellipse fitting on the filtered point cloud data includes the following steps: Step S4.1: Perform eigenvalue decomposition on the covariance matrix of the point cloud data through PCA principal component analysis, cross-multiply the two eigenvectors with larger eigenvalues, and regard the resulting unit vector as the tunnel axis direction vector. Project the three-dimensional point cloud onto the two-dimensional plane according to the tunnel axis direction vector; Step S4.2: Use the least squares method to fit the projected point cloud data into an elliptic curve; the least squares parameter matrix is: To minimize the fitting error, the cost function can be expressed as: min||Da|| 2 where a is a vector [A,B,C,D,E,F] containing the parameters of the ellipse that satisfy the general formula for the ellipse equation: Ax 2 +Bxy+Cy 2 +Dx+Ey+F=0 According to the fitted ellipse equation A * x 2 +B * xy+C * y 2 +D * x+E * y+F * =0, the major axis l and minor axis s of the tunnel cross-section profile are obtained; according to the theoretical radius D of the tunnel cross-section, the true roundness of the tunnel is calculated: γ=2(ls) / D.

8. A segment roundness measurement system based on a two-dimensional laser radar for implementing the segment roundness measurement method based on a two-dimensional laser radar as claimed in any one of claims 1 to 7, comprising a PC, a laser radar, a segment assembly machine and an encoder thereof; The laser radar is fixed on the counterweight block of the segment assembly machine, and its measurement plane coincides with the rotation axis of the segment assembly machine; the PC end realizes TCP communication with the laser radar and the segment assembly machine to ensure that the data points collected by the laser radar and the encoder of the segment assembly machine can be sent to the PC end in real time.

9. A computer device, comprising a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the method for measuring the roundness of a pipe segment based on two-dimensional laser radar as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, which, when executed by a processor, enables the processor to execute the steps of the method for measuring the roundness of a pipe segment based on two-dimensional laser radar as described in any one of claims 1 to 7.

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