CN111442736B - Railway tunnel deformation detection method and device based on laser scanner - Google Patents
Railway tunnel deformation detection method and device based on laser scanner Download PDFInfo
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Abstract
The invention discloses a railway tunnel deformation detection method and a railway tunnel deformation detection device based on a laser scanner. The method comprises the following steps: calculating a travel distance of the laser scanner; firstly, converting a local coordinate system and a tunnel coordinate system, and then generating three-dimensional coordinates to form point cloud; firstly, determining a contour curve, then drawing a circle, and finally determining a support area; firstly, respectively calculating the direction angles of the two vectors, then calculating the curvature angle, and finally judging whether the curvature angle is within the threshold range; deleting all non-tunnel face data points; smoothing point cloud data of point clouds formed by the three-dimensional coordinates; substituting the tunnel section data into the ellipse conversion matrix to obtain five ellipse equation parameters of a preset ellipse, and determining the preset ellipse according to the five ellipse equation parameters; and calculating the ellipticity of the preset ellipse, and judging the convergence and expansion conditions and the deformation conditions of the railway tunnel. The invention can improve the detection efficiency and precision of tunnel deformation, reduce the labor intensity and realize the effect of automatic detection.
Description
Technical Field
The invention relates to a railway tunnel deformation detection method in the technical field of railway tunnel detection, in particular to a railway tunnel deformation detection method based on a laser scanner and a railway tunnel deformation detection device based on the laser scanner and applying the method.
Background
Along with the rapid development of economy in China, the population density of cities is increased, and the pressure on urban ground traffic is increased, so that tunnels are built underground in each large city, and subways are opened to solve the problems of shortage of urban ground resources and large occupied space of road traffic. However, as the operation task of urban subways is increasingly heavy and the passenger flow is large, the safety and stability of subway tunnel lines become new concerns of various metro operation companies. The tunnel deformation is large, and the normal operation of the subway is influenced. The safety and stability of the tunnel are closely related to the normal operation of the subway and the safety of passengers, so that the regular detection of the structural state of the subway tunnel is necessary.
In the conventional tunnel maintenance and detection process, a railway tunnel (including a subway tunnel, a high-speed railway tunnel, a express railway tunnel and the like) has large deformation, and the deformation of the railway tunnel is periodically detected in order to detect the deformation of the tunnel. However, the existing railway tunnel deformation detection method is generally performed by a common manual detection mode or by means of manual instruments, which causes the following problems: 1. the detection efficiency is low, and because manual detection or detection through an instrument needs manual operation, a large amount of time is consumed for sampling and calculating on the spot, and the detection efficiency cannot meet the requirement; 2. the detection and measurement precision is low, because the measurement error exists in manual detection or measurement through an instrument, the final measurement result has a larger error value; 3. the detection quantity is large, so that the detection work is slow, and the maintenance quality of the tunnel is influenced.
Disclosure of Invention
The invention provides a railway tunnel deformation detection method and device based on a laser scanner, and aims to solve the technical problems that the existing railway tunnel deformation detection is poor in detection efficiency and accuracy, large in detection amount and incapable of timely detection.
The invention is realized by adopting the following technical scheme: a railway tunnel deformation detection method based on a laser scanner comprises the following steps:
scanning a railway tunnel through a laser scanner, and calculating the traveling distance of the laser scanner;
firstly, converting a local coordinate system of the laser scanner and a tunnel coordinate system of the railway tunnel to determine coordinates of scanning points of the laser scanner in the tunnel coordinate system, and then combining the coordinates and the travelling distance to generate three-dimensional coordinates to form a point cloud;
firstly, determining a contour curve of boundary characteristic points of the point cloud formed by the three-dimensional coordinates, then selecting a preset point as a circle center on the contour curve, drawing a circle to compare the contour curve with two intersection points, and finally taking an area formed by the preset point and the two intersection points as a support area;
firstly, respectively calculating direction angles of two vectors formed by the preset point and two intersection points, then calculating curvature angles of the two direction angles in the supporting area, and finally judging whether the curvature angles are located in a preset threshold range;
when the curvature angle is within the threshold value range, firstly, using points forming the corresponding support area as non-tunnel surface data points, then deleting all the non-tunnel surface data points, and obtaining tunnel surface data;
smoothing point cloud data of the point cloud formed by the three-dimensional coordinates through a preset Gaussian filter to remove protruding non-tunnel face data in the tunnel face data and obtain tunnel section data;
substituting the tunnel section data into a preset ellipse conversion matrix to obtain five ellipse equation parameters of a preset ellipse, and determining the geometric center, the long and short half shafts and the long axis inclination angle of the preset ellipse according to the five ellipse equation parameters;
and calculating the ellipticity of the preset ellipse, judging the convergence and expansion condition of the railway tunnel according to the change condition of the long half shaft and the short half shaft, and evaluating the deformation condition of the railway tunnel according to the ellipticity.
The invention calculates the travel distance, converts the local coordinate system of the laser scanner and the tunnel coordinate system to determine the coordinate of the scanning point in the tunnel coordinate system, combines the coordinate and the travel distance to obtain the three-dimensional coordinate to form the point cloud, then determines the outline curve of the point cloud, and selects the point on the outline curve to draw a circle to obtain the support area, then determines the curvature angle by calculating two direction angles, and judges whether the curvature angle is in the threshold range, when the curvature angle is in the threshold range, the point of the support area is deleted as the non-tunnel surface data point, and the rest points form the tunnel surface data to realize the point cloud segmentation, then carries out the smooth processing to the point cloud data by the Gauss filter to eliminate the obvious convex non-tunnel surface data, and then substitutes the obtained tunnel section data into the ellipse conversion matrix to obtain the ellipse equation parameter, and determining the geometric center, the major and minor semi-axes and the major axis inclination angle of the ellipse according to the parameters, finally calculating the ellipticity, and judging the convergence expansion and deformation conditions of the railway tunnel according to the related information of the preset ellipse, thereby realizing the detection of the deformation of the railway tunnel, solving the technical problems that the existing railway tunnel deformation detection has poor detection efficiency and precision, large detection amount and cannot detect in time, improving the detection efficiency and precision, carrying out detection in a large amount and reducing the manual labor intensity.
As a further improvement of the above aspect, the laser scanner is mounted on a dynamic detection railcar capable of traveling on the railway tunnel, and the dynamic detection railcar is further mounted with an odometer; the method for calculating the travel distance comprises the following steps:
calibrating the time of data acquisition of the laser scanner and the odometer, and synchronizing the odometer time and the scanning time;
and calculating the number of wheel rotation turns N of the dynamic detection rail car, wherein the calculation formula is as follows:
in the formula, a1For counting the progress of the odometer, a2Counting the backward movement of the odometer, wherein n is the number of counts obtained by one turn of a measuring wheel of the odometer;
calculating the travel distance s, wherein the calculation formula is as follows:
s＝2πr1N
in the formula, r1And dynamically detecting the wheel radius of the rail car.
As a further improvement of the above solution, an x-axis of the local coordinate system is arranged parallel to a z-axis of the tunnel coordinate system; the conversion method of the local coordinate system and the tunnel coordinate system comprises the following steps:
calibrating the relative position of the laser scanner;
calculating a distance c1 between the scan point and the x-axis of the tunnel coordinate system;
calculating a distance d1 between the scanning point and the central axis of the railway tunnel;
calculating the inclination angle beta of the laser scanner relative to the tunnel coordinate system;
firstly, the local coordinate system OXZ is subjected to rotation transformation, and then the local coordinate system is transformed into the tunnel coordinate system OX 'Z' through translation, and the transformation formula is as follows:
further, defining the preset point as a point P, Pf、PbTwo intersection points are respectively formed; the calculation formulas of the two direction angles are respectively as follows:
in the formula, thetaf、θbTwo direction angles are respectively;
the calculation formula of the curvature angle is as follows:
θ(i)＝θb-θf
in the formula, theta(i)For the angle of curvature, (z, x) is the point p coordinate, (z)f，xf) Is a point PfCoordinate (z)b，xb) Is a point PbAnd (4) coordinates.
As a further improvement of the above scheme, the point cloud data and the gaussian filter are subjected to piecewise convolution calculation to realize piecewise smoothing processing; the expression of the gaussian filter is:
in the formula, σ2Is the variance.
As a further improvement of the above scheme, the elliptic transformation matrix is:
therein, ax2+bxy+cy2+ dx + ey +1 ═ 0, and a, b, c, d, e are five elliptic equation parameters, (x)1,y1)、(x2,y2)、……、(xn,yn) The coordinates corresponding to the scanning points 1, 2, … …, n, respectively.
Further, five ellipse equation parameters are solved by a least square method, and the solving formula is as follows:
X＝(BTB)-1BTL
in the formula (I), the compound is shown in the specification,
still further, the calculation formula of the geometric center is as follows:
in the formula, x0、y0Respectively are the horizontal and vertical coordinates of the geometric center;
the calculation formula of the long half shaft and the short half shaft is as follows:
in the formula, the larger one of m and n is a long half shaft, and the smaller one is a short half shaft;
the long axis inclination angle has the calculation formula as follows:
wherein α is the inclination angle of the major axis.
Still further, the formula for calculating the ellipticity is as follows:
wherein θ is the ellipticity, r2Is the nominal outer diameter.
The invention also provides a railway tunnel deformation detection device based on the laser scanner, which applies any of the above railway tunnel deformation detection methods based on the laser scanner, and comprises the following steps:
a scanning module for scanning a railway tunnel by a laser scanner and calculating a travel distance of the laser scanner;
the point cloud generating module is used for firstly converting a local coordinate system of the laser scanner and a tunnel coordinate system of the railway tunnel to determine coordinates of scanning points of the laser scanner in the tunnel coordinate system, and then combining the coordinates and the travelling distance to generate three-dimensional coordinates to form a point cloud;
the tunnel surface point cloud segmentation module comprises a circle drawing unit, a curvature angle calculation and judgment unit and a non-tunnel surface data point rejection unit; the circle drawing unit is used for firstly determining a contour curve of boundary characteristic points of the point cloud formed by the three-dimensional coordinates, then selecting a preset point as a circle center on the contour curve and drawing a circle to compare the contour curve with two intersection points, and finally taking an area formed by the preset point and the two intersection points as a support area; the curvature angle calculation and judgment unit is used for calculating direction angles of two vectors formed by the preset point and the two intersection points respectively, calculating curvature angles of the two direction angles in the supporting area, and finally judging whether the curvature angles are within a preset threshold range; the non-tunnel face data point eliminating unit is used for taking the points forming the corresponding support area as non-tunnel face data points, then deleting all the non-tunnel face data points and obtaining tunnel face data when the curvature angle is within the threshold range;
the smoothing module is used for smoothing point cloud data of the point cloud formed by the three-dimensional coordinates through a preset Gaussian filter so as to remove convex non-tunnel face data in the tunnel face data and obtain tunnel section data;
the ellipse equation parameter determining module is used for substituting the tunnel section data into a preset ellipse conversion matrix to obtain five ellipse equation parameters of a preset ellipse, and determining a geometric center, a major-minor axis and a major axis inclination angle of the preset ellipse according to the five ellipse equation parameters; and
and the calculation and evaluation module is used for calculating the ellipticity of the preset ellipse, judging the convergence and expansion conditions of the railway tunnel according to the change conditions of the long half shaft and the short half shaft, and evaluating the deformation condition of the railway tunnel according to the ellipticity.
Compared with the existing railway tunnel deformation detection technology, the railway tunnel deformation detection method and the device based on the laser scanner have the following beneficial effects:
1. the railway tunnel deformation detection method based on the laser scanner comprises the steps of firstly calculating a traveling distance, then converting a local coordinate system of the laser scanner with a tunnel coordinate system, determining coordinates of scanning points in the tunnel coordinate system, combining the coordinates with the traveling distance to obtain three-dimensional coordinates to form a point cloud, then determining a contour curve of the point cloud, selecting points on the contour curve to draw a circle to obtain a support area, then determining a curvature angle by calculating two direction angles, judging whether the curvature angle is located in a threshold range, deleting points of the support area as non-tunnel surface data points when the curvature angle is located in the threshold range, and deleting the rest points to form tunnel surface data to realize point cloud segmentation, then smoothing the point cloud data through a Gaussian filter to eliminate obvious convex non-tunnel surface data, and then substituting the acquired tunnel section data into an ellipse conversion matrix to acquire ellipse equation parameters, determining the geometric center, the major and minor semi-axes and the major axis inclination angle of the ellipse according to the parameters, finally calculating the ellipticity, and judging the convergence expansion and deformation conditions of the railway tunnel according to the related information of the preset ellipse to realize the detection of the railway tunnel deformation. Because can automated processing at the in-process that detects, need not to adopt the mode of artifical detection or artificial instrument to detect the tunnel deformation condition, can improve the detection efficiency and the precision that the tunnel warp, simultaneously because adopt automated means to handle, can detect and measure the deflection in tunnel in large batch, can practice thrift a large amount of hand labor power, alleviate artifical intensity of labour, realized automated inspection's effect.
2. According to the railway tunnel deformation detection method based on the laser scanner, the railway tunnel is scanned through the laser scanner carried by the dynamic detection rail car, meanwhile, the mileage meter is used for counting, the data acquisition time of the mileage meter and the data acquisition time of the laser scanner are calibrated, the mileage time and the scanning time are synchronized, the follow-up calculated traveling distance can be matched with the coordinate of a scanning point in a tunnel coordinate system, and the formed three-dimensional coordinate composition point cloud is more accurate.
3. According to the railway tunnel deformation detection method based on the laser scanner, the local coordinate system and the tunnel coordinate system are converted, and the coordinates of the scanning points in the tunnel coordinate system are obtained, so that the tunnel form can be correctly reflected, meanwhile, the conversion among different coordinate systems is met, the detection method is high in adaptability, and the deformation detection effect is good.
4. According to the railway tunnel deformation detection method based on the laser scanner, after point cloud data are obtained, the point cloud data comprise tunnel surface point cloud data and non-tunnel surface point cloud data, and the non-tunnel surface point cloud data can affect the detection accuracy. Therefore, the detection method carries out point cloud segmentation through a segmentation algorithm based on boundary characteristic points, determines whether the data points are non-tunnel section data points or not through comparison of curvature angles and a threshold range, if the data points are non-tunnel section data points, deletes the data points, further smoothes the scanning points through a Gaussian filter, removes noise generated by dust in tunnel air by utilizing the fact that the noise source distribution of the dust in the air conforms to the Gaussian noise distribution, and carries out noise reduction treatment in a segmented mode, so that finally obtained data are closer to the actual situation, and the detection accuracy is improved. For example, the filter parameter size is selected according to different characteristics of the data, some unobvious convex non-tunnel surface data are not completely removed after the point cloud data is segmented, a gaussian function with a large variance can be selected for smoothing, and a gaussian function with a small variance can be selected for filtering at other scanning points, so that the detail can be well preserved, and a good noise suppression effect can be achieved.
5. This railway tunnel deformation detection device based on laser scanner, its beneficial effect is the same with the beneficial effect of the above-mentioned railway tunnel deformation detection method based on laser scanner, does not need here to describe again.
Drawings
Fig. 1 is a flowchart of a method for detecting deformation of a railway tunnel based on a laser scanner according to embodiment 1 of the present invention.
Fig. 2 is a schematic view of a laser scanner-based railway tunnel deformation detection method in a point cloud segmentation process according to embodiment 1 of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Example 1
Referring to fig. 1 and 2, the present embodiment provides a method for detecting deformation of a railway tunnel based on a laser scanner, which can be applied to tunnels such as a high-speed railway tunnel, a subway tunnel, and a common railway tunnel, and detects deformation of the tunnels. The tunnel deformation information is an important index for measuring the quality of the tunnel, and plays an important role in railway tunnel maintenance. When the tunnel deformation is too large, the relevant maintenance personnel are required to perform maintenance treatment on the section of tunnel, and the use safety of the railway tunnel is ensured. In the embodiment, the detection method is based on the detection of the deformation of the tunnel by the laser scanner, so that data support is provided for tunnel maintenance. The railway tunnel deformation detection method comprises the following steps of (1) - (8).
(1) The railway tunnel is scanned by the laser scanner, and a travel distance of the laser scanner is calculated. In the present embodiment, the laser scanner is mounted on a dynamic detection railcar capable of traveling on a railway tunnel, and the dynamic detection railcar is further mounted with a speedometer. The calculation method of the travel distance includes the following steps, i.e., steps (1.1) - (1.3).
And (1.1) calibrating the data acquisition time of the laser scanner and the odometer, and synchronizing the odometer time and the scanning time. Therefore, the travelling distance calculated subsequently can be matched with the coordinate of the scanning point in the tunnel coordinate system after time synchronization, and the formed three-dimensional coordinate composition point cloud is more accurate.
(1.2) calculating the number of wheel rotation turns N of the dynamic detection rail car, wherein the calculation formula is as follows:
in the formula, a1For the advance counting of the odometer, a2The back-off counting of the odometer is realized, and n is the counting number obtained by one-turn rotation of the odometer measuring wheel.
(1.3) calculating the travel distance s, and the calculation formula is:
s＝2πr1N
in the formula, r1For dynamic detectionWheel radius of the rail car.
(2) Firstly, a local coordinate system of the laser scanner and a tunnel coordinate system of a railway tunnel are converted to determine coordinates of scanning points of the laser scanner in the tunnel coordinate system, and then the coordinates and a traveling distance are combined to generate three-dimensional coordinates to form a point cloud. In the practical application of tunnel detection, a local coordinate system based on a laser scanner and two sets of coordinate systems based on a central axis of a tunnel are used for detection. The origin of coordinates of the local coordinate system is at the midpoint of the scanning prism, and the point cloud data obtained by the scanner is based on the local coordinate system. The tunnel coordinate system is a coordinate system used in the tunnel construction process, which is a coordinate system actually used. The shape of the tunnel can be correctly reflected only by calculating the coordinate value of the laser scanning point under the tunnel coordinate system, and the coordinate of the laser scanning point under the tunnel coordinate system is called as an absolute coordinate. Therefore, coordinate transformation between two coordinate systems is required, so that the form of the tunnel can be correctly reflected, conversion between different coordinate systems is met, the detection method is stronger in adaptability, and the deformation detection effect is better. Because the laser scanner and the odometer are synchronized in time after time calibration, the mileage measured by the odometer at the same time is used as the y-axis coordinate of the three-dimensional coordinate, and the x-axis coordinate and the z-axis coordinate which are measured by the laser scanner at the same time and are subjected to coordinate conversion jointly form the point cloud formed by the three-dimensional coordinates. In this embodiment, the x-axis of the local coordinate system is arranged parallel to the z-axis of the tunnel coordinate system. The conversion method of the local coordinate system and the tunnel coordinate system comprises the following steps of (2.1) - (2.5).
And (2.1) calibrating the relative position of the laser scanner.
(2.2) calculating the distance 1c between the scanning point and the x-axis of the tunnel coordinate system.
(2.3) calculating the distance d1 between the scanning point and the central axis of the railway tunnel.
And (2.4) calculating the inclination angle beta of the laser scanner relative to the tunnel coordinate system.
(2.5) firstly, the local coordinate system OXZ is subjected to rotation transformation, and then the local coordinate system is transformed into the tunnel coordinate system OX 'Z' through translation, and the transformation formula is as follows:
(3) firstly, determining a contour curve of boundary characteristic points of a point cloud formed by three-dimensional coordinates, then selecting a preset point as a circle center on the contour curve, drawing a circle to compare the contour curve with two intersection points, and finally taking an area formed by the preset point and the two intersection points as a support area.
(4) The method comprises the steps of firstly respectively calculating direction angles of two vectors formed by a preset point and two intersection points, then calculating curvature angles of the two direction angles in a supporting area, and finally judging whether the curvature angles are located in a preset threshold range. Defining the preset point as point P, Pf、PbTwo intersection points are respectively provided, and the calculation formulas of the two direction angles are respectively as follows:
in the formula, thetaf、θbTwo direction angles are respectively;
the formula for the curvature angle is:
θ(i)＝θb-θfin the formula, theta(i)Is the angle of curvature, (z, x) is the point p coordinate, (z)f，xf) Is a point PfCoordinate (z)b，xb) Is a point PbAnd (4) coordinates.
In this embodiment, a suitable R value (radius of a drawn circle) is selected to perform feature region selection, and the suitable R value is determined according to several sets of experiments to achieve an effect of avoiding noise without increasing the calculation amount of the algorithm to a large extent.
(5) And when the curvature angle is within the threshold range, firstly, using the points forming the corresponding support area as non-tunnel surface data points, then deleting all the non-tunnel surface data points, and obtaining tunnel surface data. After the R value is determined, the support area of each scanning point can be obtained andcalculating a corresponding curvature angle theta(i)The curvature angle is statistically analyzed, and a threshold range [ c ] is set1,c2]If c is1≤θ(i)≤c2Then these points are taken as non-tunnel section data points and eliminated. If the data points of the non-tunnel face are still obviously not removed after the primary removal, the data after the primary removal can be retained to continue the steps, and the appropriate R value and the threshold value are selected again to be removed until all the data points of the non-tunnel face are removed. Therefore, the finally obtained data is closer to the actual situation, and the detection accuracy is improved.
(6) And smoothing the point cloud data of the point cloud formed by the three-dimensional coordinates through a preset Gaussian filter to remove the convex non-tunnel face data in the tunnel face data and obtain tunnel section data. The point cloud data is subjected to piecewise convolution calculation with a Gaussian filter to achieve piecewise smoothing processing. The dust in the tunnel air is a main noise source, the distribution of the dust accords with Gaussian noise distribution, and the smoothing is carried out on the scanning point by utilizing a Gaussian filter, so that the noise can be removed to the maximum extent, and finally obtained data can accord with the reality better. In this embodiment, the expression of the gaussian filter is:
in the formula, σ2Is the variance. The noise reduction effect is determined by parameter selection of the Gaussian filter, the size of the filter parameters is selected according to different characteristics of data, some unobvious protruding non-tunnel surface data are not completely eliminated after point cloud data are segmented, a Gaussian function with a large variance can be selected for smoothing, and Gaussian functions with a small variance are selected for filtering at other scanning points. Therefore, the noise reduction processing is carried out in a segmented mode, so that the details can be well reserved, and a good noise suppression effect can be achieved.
(7) And substituting the tunnel section data into a preset ellipse conversion matrix to obtain five ellipse equation parameters of a preset ellipse, and determining the geometric center, the major and minor semi-axes and the major axis inclination angle of the preset ellipse according to the five ellipse equation parameters. The deformation mechanism of the tunnel is that the section of the tunnel changes from a circle into an ellipse with smaller eccentricity along with the time, so that the fitting of the section of the tunnel into the ellipse has certain rationality in analysis. And (3) carrying out segmentation filtering and noise reduction on the acquired tunnel section data through the non-tunnel data in the front. Wherein the elliptic transformation matrix is:
wherein, the parameters of the elliptic equation satisfy:
ax2+bxy+cy2+dx+ey+1＝0
in the formula, a, b, c, d and e are five ellipse equation parameters, (x)1,y1)、(x2,y2)、……、(xn,yn) The coordinates corresponding to the scanning points 1, 2, … …, n, respectively.
Then: BX ═ L
Solving the five elliptic equation parameters by a least square method, wherein the solving formula is as follows:
X＝(BTB)-1BTL
wherein each variable satisfies:
after five ellipse equation parameters are calculated, the calculation formula of the geometric center of the preset ellipse is as follows:
in the formula, x0、y0Respectively are the horizontal and vertical coordinates of the geometric center;
the calculation formula of the long half shaft and the short half shaft is as follows:
in the formula, the larger one of m and n is a long half shaft, and the smaller one is a short half shaft;
the long axis inclination angle is calculated by the formula:
wherein α is the inclination angle of the major axis.
(8) And calculating the ellipticity of the preset ellipse, judging the convergence and expansion conditions of the railway tunnel according to the change conditions of the long half shaft and the short half shaft, and evaluating the deformation condition of the railway tunnel according to the ellipticity. In this way, the detection method adopts an automatic means to process, can detect and measure the deformation of the tunnel in large batch, can save a large amount of manual labor, reduces the manual labor intensity and realizes the effect of automatic detection. In this embodiment, the formula for calculating the ellipticity is:
in the formulaTheta is ovality, r2Is the nominal outer diameter.
In summary, compared with the existing railway tunnel deformation detection method, the railway tunnel deformation detection method based on the laser scanner of the embodiment has the following advantages:
1. the railway tunnel deformation detection method based on the laser scanner comprises the steps of firstly calculating a traveling distance, then converting a local coordinate system of the laser scanner with a tunnel coordinate system, determining coordinates of scanning points in the tunnel coordinate system, combining the coordinates with the traveling distance to obtain three-dimensional coordinates to form a point cloud, then determining a contour curve of the point cloud, selecting points on the contour curve to draw a circle to obtain a support area, then determining a curvature angle by calculating two direction angles, judging whether the curvature angle is located in a threshold range, deleting points of the support area as non-tunnel surface data points when the curvature angle is located in the threshold range, and deleting the rest points to form tunnel surface data to realize point cloud segmentation, then smoothing the point cloud data through a Gaussian filter to eliminate obvious convex non-tunnel surface data, and then substituting the acquired tunnel section data into an ellipse conversion matrix to acquire ellipse equation parameters, determining the geometric center, the major and minor semi-axes and the major axis inclination angle of the ellipse according to the parameters, finally calculating the ellipticity, and judging the convergence expansion and deformation conditions of the railway tunnel according to the related information of the preset ellipse to realize the detection of the railway tunnel deformation. Because can automated processing at the in-process that detects, need not to adopt the mode of artifical detection or artificial instrument to detect the tunnel deformation condition, can improve the detection efficiency and the precision that the tunnel warp, simultaneously because adopt automated means to handle, can detect and measure the deflection in tunnel in large batch, can practice thrift a large amount of hand labor power, alleviate artifical intensity of labour, realized automated inspection's effect.
2. According to the railway tunnel deformation detection method based on the laser scanner, the railway tunnel is scanned through the laser scanner carried by the dynamic detection rail car, meanwhile, the mileage meter is used for counting, the data acquisition time of the mileage meter and the data acquisition time of the laser scanner are calibrated, the mileage time and the scanning time are synchronized, the follow-up calculated traveling distance can be matched with the coordinate of a scanning point in a tunnel coordinate system, and the formed three-dimensional coordinate composition point cloud is more accurate.
3. According to the railway tunnel deformation detection method based on the laser scanner, the local coordinate system and the tunnel coordinate system are converted, and the coordinates of the scanning points in the tunnel coordinate system are obtained, so that the tunnel form can be correctly reflected, meanwhile, the conversion among different coordinate systems is met, the detection method is high in adaptability, and the deformation detection effect is good.
4. According to the railway tunnel deformation detection method based on the laser scanner, after point cloud data are obtained, the point cloud data comprise tunnel surface point cloud data and non-tunnel surface point cloud data, and the non-tunnel surface point cloud data can affect the detection accuracy. Therefore, the detection method carries out point cloud segmentation through a segmentation algorithm based on boundary characteristic points, determines whether the data points are non-tunnel section data points or not through comparison of curvature angles and a threshold range, if the data points are non-tunnel section data points, deletes the data points, further smoothes the scanning points through a Gaussian filter, removes noise generated by dust in tunnel air by utilizing the fact that the noise source distribution of the dust in the air conforms to the Gaussian noise distribution, and carries out noise reduction treatment in a segmented mode, so that finally obtained data are closer to the actual situation, and the detection accuracy is improved. For example, the filter parameter size is selected according to different characteristics of the data, some unobvious convex non-tunnel surface data are not completely removed after the point cloud data is segmented, a gaussian function with a large variance can be selected for smoothing, and a gaussian function with a small variance can be selected for filtering at other scanning points, so that the detail can be well preserved, and a good noise suppression effect can be achieved.
Example 2
The embodiment provides a railway tunnel deformation detection method based on a laser scanner, which is added with the following steps, namely step (11) and step (12), on the basis of the embodiment 1.
(11) And storing the detection data of five ellipse equation parameters, geometric centers, long and short half shafts, major axis inclination angles and ellipticity of the preset ellipse into the cloud platform. The cloud platform is provided with storage positions of all sections of railway tunnels, and the measurement data of the linear cracks correspond to all sections of railway tunnels on the cloud platform, so that a tunnel crack big data system is generated. The big data system can accumulate tunnel detection and measurement data, and can check the tunnel detection and measurement data in time through equipment such as a mobile phone and a PC (personal computer) terminal when personnel need to check the tunnel detection and measurement data, so that railway maintenance is more convenient.
(12) And judging whether the convergence and expansion degree is within a preset convergence and expansion range in the convergence and expansion condition, and meanwhile, judging whether the ellipticity is within a preset ellipticity value range. When at least one of the two exceeds the range, the cloud platform sends out alarm information (the information comprises the position and the standard exceeding condition of the tunnel section), so that railway maintenance personnel can find and process the tunnel section, the railway tunnel is maintained more conveniently, and the railway maintenance efficiency and maintenance quality are improved.
Example 3
The present embodiment provides a laser scanner based railway tunnel deformation detection apparatus, which applies the laser scanner based railway tunnel deformation detection method in embodiment 1 or embodiment 2. The device comprises a scanning module, a point cloud generating module, a circle drawing module, a curvature angle calculating and judging module, a non-tunnel face data point eliminating module, a smoothing module, an ellipse equation parameter determining module and a calculating and evaluating module.
The scanning module is used for scanning the railway tunnel through the laser scanner and calculating the traveling distance of the laser scanner. In the present embodiment, the scanning module is provided in a dynamic detection rail vehicle, and the dynamic detection rail vehicle is mounted with a laser scanner and a odometer. When calculating the travel distance, the scanning module calculates the travel distance according to the counting information of the odometer, and the specific calculation method may be the calculation method of the travel distance in step (1) in embodiment 1.
The point cloud generating module is used for firstly converting a local coordinate system of the laser scanner and a tunnel coordinate system of the railway tunnel so as to determine the coordinates of scanning points of the laser scanner in the tunnel coordinate system, and then combining the coordinates with the traveling distance and generating three-dimensional coordinates to form a point cloud. The point cloud generating module can correctly reflect the form of the tunnel, meanwhile, the conversion among different coordinate systems is met, the detection method is stronger in adaptability, and the deformation detection effect is better. In this embodiment, the point cloud generating module may implement step (2) in embodiment 1.
The circle drawing module is used for determining a contour curve of boundary characteristic points of the point cloud formed by the three-dimensional coordinates, selecting a preset point as a circle center on the contour curve, drawing a circle to compare the contour curve with two intersection points, and finally taking an area formed by the preset point and the two intersection points as a support area. In this embodiment, the circle drawing module may implement step (3) in embodiment 1.
The curvature angle calculation and judgment module is used for calculating direction angles of two vectors formed by the preset points and the two intersection points respectively, calculating curvature angles of the two direction angles in the supporting area, and finally judging whether the curvature angles are located in a preset threshold range. The curvature angle calculation determination module, when performing the calculation determination, may perform the calculation and processing using the formula in step (4) in embodiment 1.
And the non-tunnel face data point eliminating module is used for taking the points forming the corresponding support area as non-tunnel face data points when the curvature angle is within the threshold range, then deleting all the non-tunnel face data points and obtaining tunnel face data. The non-tunnel surface data point eliminating module can obtain the support area of each scanning point after determining the R value and calculate the corresponding curvature angle theta(i)The curvature angle is statistically analyzed, and a threshold range [ c ] is set1,c2]If c is1≤θ(i)≤c2Then these points are taken as non-tunnel section data points and eliminated. If the data points of the non-tunnel face are still obviously not removed after the primary removal, the non-tunnel face data point removing module can retain the data after the primary removal and continue the steps, and selects a proper R value and a proper threshold value again to remove until all the data points of the non-tunnel face are removed. Therefore, the finally obtained data is closer to the actual situation, and the detection accuracy is improved.
The smoothing module is used for smoothing the point cloud data of the point cloud formed by the three-dimensional coordinates through a preset Gaussian filter so as to eliminate the convex non-tunnel face data in the tunnel face data and obtain tunnel section data. The parameter selection of the Gaussian filter determines the noise reduction effect, the smoothing module selects the parameter size of the filter according to different characteristics of data, some unobvious protruding non-tunnel surface data are not completely removed after the point cloud data are segmented, a Gaussian function with a large variance can be selected for smoothing, and Gaussian functions with a small variance are selected for filtering at other scanning points. Therefore, the noise reduction processing is carried out in a segmented mode, so that the details can be well reserved, and a good noise suppression effect can be achieved.
The ellipse equation parameter determining module is used for substituting the tunnel section data into a preset ellipse conversion matrix to obtain five ellipse equation parameters of a preset ellipse, and determining the geometric center, the major and minor semi-axes and the major axis inclination angle of the preset ellipse according to the five ellipse equation parameters. The ellipse equation parameter determination module can calculate by using each calculation formula of step (7) in embodiment 1 when determining these parameters.
The calculation and evaluation module is used for calculating the ellipticity of the preset ellipse, judging the convergence and expansion condition of the railway tunnel according to the change condition of the long half shaft and the short half shaft, and evaluating the deformation condition of the railway tunnel according to the ellipticity. The calculation and evaluation module can adopt the calculation formula in the embodiment 1 when calculating the ovality, and can judge according to the change curves of the major-minor axis and the ovality when judging or evaluating.
Example 4
The present embodiment provides a railway tunnel detection chip having a computer program embedded therein, the computer program being capable of executing the laser scanner-based railway tunnel deformation detection method of embodiment 1 or 2. The tunnel detection chip of the embodiment can be directly embedded in the railway maintenance equipment, can be produced and manufactured independently, and can also be manufactured into a detection and measurement module for application.
Example 5
The present embodiments provide a computer terminal comprising a memory, a processor, and a computer program stored on the memory and executable on the processor. The processor implements the steps of the laser scanner based railway tunnel deformation detection method of embodiment 1 or 2 when executing the program.
When the laser scanner-based railway tunnel deformation detection method in embodiment 1 or 2 is applied, the method may be applied in a software form, for example, a program designed to run independently is installed on a computer terminal, and the computer terminal may be a computer, a smart phone, a control system, other internet of things devices, and the like. The method for detecting deformation of a railway tunnel based on a laser scanner according to embodiment 1 or 2 may also be designed as an embedded running program, and installed on a computer terminal, such as a single chip microcomputer.
Example 6
The present embodiment provides a computer-readable storage medium having a computer program stored thereon. The program, when executed by a processor, implements the steps of the laser scanner based railway tunnel deformation detection method of embodiment 1 or 2.
The laser scanner based railway tunnel deformation detection method of embodiment 1 or 2 may be applied in the form of software, such as a program designed to be independently run by a computer readable storage medium, which may be a usb flash disk designed as a usb shield, and the usb flash disk is designed to be a program for starting the whole method by external triggering.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents and improvements made within the spirit and principle of the present invention are intended to be included within the scope of the present invention.
Claims (10)
1. A railway tunnel deformation detection method based on a laser scanner is characterized by comprising the following steps:
scanning a railway tunnel through a laser scanner, and calculating the traveling distance of the laser scanner;
firstly, converting a local coordinate system of the laser scanner and a tunnel coordinate system of the railway tunnel to determine coordinates of scanning points of the laser scanner in the tunnel coordinate system, and then combining the coordinates and the travelling distance to generate three-dimensional coordinates to form a point cloud;
firstly, determining a contour curve of boundary characteristic points of the point cloud formed by the three-dimensional coordinates, then selecting a preset point as a circle center on the contour curve, drawing a circle to compare the contour curve with two intersection points, and finally taking an area formed by the preset point and the two intersection points as a support area;
firstly, respectively calculating direction angles of two vectors formed by the preset point and two intersection points, then calculating curvature angles of the two direction angles in the supporting area, and finally judging whether the curvature angles are located in a preset threshold range;
when the curvature angle is within the threshold value range, firstly, using points forming the corresponding support area as non-tunnel surface data points, then deleting all the non-tunnel surface data points, and obtaining tunnel surface data;
smoothing point cloud data of the point cloud formed by the three-dimensional coordinates through a preset Gaussian filter to remove protruding non-tunnel face data in the tunnel face data and obtain tunnel section data;
substituting the tunnel section data into a preset ellipse conversion matrix to obtain five ellipse equation parameters of a preset ellipse, and determining the geometric center, the long and short half shafts and the long axis inclination angle of the preset ellipse according to the five ellipse equation parameters;
and calculating the ellipticity of the preset ellipse, judging the convergence and expansion condition of the railway tunnel according to the change condition of the long half shaft and the short half shaft, and evaluating the deformation condition of the railway tunnel according to the ellipticity.
2. The method for detecting deformation of a railway tunnel by using a laser scanner according to claim 1, wherein the laser scanner is mounted on a dynamic detection railcar capable of traveling on the railway tunnel, and the dynamic detection railcar is further mounted with a speedometer; the method for calculating the travel distance comprises the following steps:
calibrating the time of data acquisition of the laser scanner and the odometer, and synchronizing the odometer time and the scanning time;
and calculating the number of wheel rotation turns N of the dynamic detection rail car, wherein the calculation formula is as follows:
in the formula, a1For counting the progress of the odometer, a2Counting the backward movement of the odometer, wherein n is the number of counts obtained by one turn of a measuring wheel of the odometer;
calculating the travel distance s, wherein the calculation formula is as follows:
s＝2πr1N
in the formula, r1And dynamically detecting the wheel radius of the rail car.
3. The laser scanner based railway tunnel deformation detection method of claim 1, wherein an x-axis of the local coordinate system is disposed in parallel with a z-axis of the tunnel coordinate system; the conversion method of the local coordinate system and the tunnel coordinate system comprises the following steps:
calibrating the relative position of the laser scanner;
calculating a distance c1 between the scan point and the x-axis of the tunnel coordinate system;
calculating a distance d1 between the scanning point and the central axis of the railway tunnel;
calculating the inclination angle beta of the laser scanner relative to the tunnel coordinate system;
firstly, the local coordinate system OXZ is subjected to rotation transformation, and then the local coordinate system is transformed into the tunnel coordinate system OX 'Z' through translation, and the transformation formula is as follows:
4. the laser scanner based railway tunnel deformation detection method of claim 3, wherein the preset point is defined as point P, Pf、PbTwo intersection points are respectively formed; the calculation formulas of the two direction angles are respectively as follows:
in the formula, thetaf、θbTwo direction angles are respectively;
the calculation formula of the curvature angle is as follows:
θ(i)＝θb-θf
in the formula, theta(i)For the angle of curvature, (z, x) is the point p coordinate, (z)f，xf) Is a point PfCoordinate (z)b，xb) Is a point PbAnd (4) coordinates.
5. The laser scanner-based railway tunnel deformation detection method of claim 1, wherein a piecewise smoothing process is implemented by piecewise convolution calculation of the point cloud data with the gaussian filter; the expression of the gaussian filter is:
in the formula, σ2Is the variance.
6. The laser scanner based railway tunnel deformation detection method of claim 1, wherein the ellipse transformation matrix is:
therein, ax2+bxy+cy2+ dx + ey +1 ═ 0, and a, b, c, d, e are five elliptic equation parameters, (x)1，y1)、(x2，y2)、……、(xn，yn) The coordinates corresponding to the scanning points 1, 2, … …, n, respectively.
7. The laser scanner-based railway tunnel deformation detection method of claim 6, wherein five elliptic equation parameters are solved by a least square method, and the solving formula is as follows:
X＝(BTB)-1BTL
in the formula (I), the compound is shown in the specification,
8. the laser scanner-based railway tunnel deformation detection method of claim 7, wherein the geometric center is calculated by the formula:
in the formula, x0、y0Respectively are the horizontal and vertical coordinates of the geometric center;
the calculation formula of the long half shaft and the short half shaft is as follows:
in the formula, the larger one of m and n is a long half shaft, and the smaller one is a short half shaft;
the long axis inclination angle has the calculation formula as follows:
wherein α is the inclination angle of the major axis.
9. The laser scanner-based railway tunnel deformation detection method according to claim 8, wherein the ovality is calculated by the formula:
wherein θ is the ellipticity, r2Is the nominal outer diameter.
10. A laser scanner based railway tunnel deformation detecting apparatus to which the laser scanner based railway tunnel deformation detecting method according to any one of claims 1 to 9 is applied, comprising:
a scanning module for scanning a railway tunnel by a laser scanner and calculating a travel distance of the laser scanner;
the point cloud generating module is used for firstly converting a local coordinate system of the laser scanner and a tunnel coordinate system of the railway tunnel to determine coordinates of scanning points of the laser scanner in the tunnel coordinate system, and then combining the coordinates and the travelling distance to generate three-dimensional coordinates to form a point cloud;
the tunnel surface point cloud segmentation module comprises a circle drawing unit, a curvature angle calculation and judgment unit and a non-tunnel surface data point rejection unit; the circle drawing unit is used for firstly determining a contour curve of boundary characteristic points of the point cloud formed by the three-dimensional coordinates, then selecting a preset point as a circle center on the contour curve and drawing a circle to compare the contour curve with two intersection points, and finally taking an area formed by the preset point and the two intersection points as a support area; the curvature angle calculation and judgment unit is used for calculating direction angles of two vectors formed by the preset point and the two intersection points respectively, calculating curvature angles of the two direction angles in the supporting area, and finally judging whether the curvature angles are within a preset threshold range; the non-tunnel face data point eliminating unit is used for taking the points forming the corresponding support area as non-tunnel face data points, then deleting all the non-tunnel face data points and obtaining tunnel face data when the curvature angle is within the threshold range;
the smoothing module is used for smoothing point cloud data of the point cloud formed by the three-dimensional coordinates through a preset Gaussian filter so as to remove convex non-tunnel face data in the tunnel face data and obtain tunnel section data;
the ellipse equation parameter determining module is used for substituting the tunnel section data into a preset ellipse conversion matrix to obtain five ellipse equation parameters of a preset ellipse, and determining a geometric center, a major-minor axis and a major axis inclination angle of the preset ellipse according to the five ellipse equation parameters; and
and the calculation and evaluation module is used for calculating the ellipticity of the preset ellipse, judging the convergence and expansion conditions of the railway tunnel according to the change conditions of the long half shaft and the short half shaft, and evaluating the deformation condition of the railway tunnel according to the ellipticity.
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| CN112697064B (en) * | 2021-03-24 | 2021-06-18 | 北京大成国测科技有限公司 | Intelligent track deformation identification system based on vision and laser radar |
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