CN108253925B - Tunnel deformation monitoring method and device based on point cloud profile and storage device - Google Patents
Tunnel deformation monitoring method and device based on point cloud profile and storage device Download PDFInfo
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- CN108253925B CN108253925B CN201810013186.2A CN201810013186A CN108253925B CN 108253925 B CN108253925 B CN 108253925B CN 201810013186 A CN201810013186 A CN 201810013186A CN 108253925 B CN108253925 B CN 108253925B
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- G01—MEASURING; TESTING
- G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
- G01B21/00—Measuring arrangements or details thereof, where the measuring technique is not covered by the other groups of this subclass, unspecified or not relevant
- G01B21/32—Measuring arrangements or details thereof, where the measuring technique is not covered by the other groups of this subclass, unspecified or not relevant for measuring the deformation in a solid
Abstract
The invention designs a tunnel deformation monitoring method, a device and a storage device based on a point cloud profile, which are characterized in that point cloud data of different stages of a tunnel are obtained through a laser scanner, the data of each stage are subjected to denoising and splicing, the data of different stages are subjected to registration, then the point cloud data of the profile are extracted according to a certain interval, and on the basis, the tunnel is subjected to deformation monitoring by using a point-to-model method. A tunnel deformation monitoring device and a storage device based on a point cloud profile are used for realizing a tunnel deformation monitoring method based on the point cloud profile. The method has the advantages of less number of the extracted profile point cloud data, high data processing efficiency, avoidance of fitting errors caused by fitting of the point cloud profile, strong practicability and high precision.
Description
Technical Field
The invention relates to the field of exploration rock-soil buildings, in particular to a tunnel deformation monitoring method and device based on a point cloud profile and a storage device.
Background
The traditional tunnel deformation monitoring generally adopts monitoring means such as a total station and a convergence meter, and has the defects of low working efficiency and incapability of quickly monitoring the deformation of the whole tunnel in real time although the precision is higher. With the continuous development of laser technology, the laser precision is also continuously improved, so that the use of a laser scanner in the field of high-precision deformation monitoring becomes a new method. At present, two methods exist for detecting changes by using point cloud data, one method is used for monitoring changes by acquiring central axis and fitting cross section data of tunnel point cloud, and the other method is used for directly monitoring changes of the whole tunnel point cloud. The first method selects a section with a certain interval, so that the monitoring efficiency is improved, but an error exists in the process of fitting the section; the second method compares the whole point cloud, so that the data volume is large and the efficiency is low.
Disclosure of Invention
In order to solve the problems, the invention provides a tunnel deformation monitoring method and device based on a point cloud profile and a storage device. A tunnel deformation monitoring method based on a point cloud profile is characterized by comprising the following steps: the method comprises the following steps:
s1: scanning by using a scanner and acquiring point cloud data of different stages of the tunnel;
s2: according to different types of point cloud data in different periods, filtering and denoising the point cloud data by adopting different methods;
s3: splicing point cloud data between adjacent measuring stations in the same period according to control targets distributed at the connection part between the adjacent measuring stations of the tunnel;
s4: registering the spliced point cloud data of different phases according to a registration model by using the control target, and converting the point cloud data into the same coordinate system;
s5: according to point cloud data in the same coordinate system, performing global fitting on the tunnel, and determining an extracted profile base line parallel to the axis of the tunnel;
s6: according to the extracted profile base line, performing cross section interception in the orthogonal direction on the point cloud data of the tunnel trend to obtain a point cloud profile;
s7: processing the point cloud profile using a point-to-grid method;
s8: and comparing the distances from the points in the point cloud data to a reference surface, namely the monitored tunnel deformation, according to a point-to-grid method.
Further, in step S1, the scanner is a laser scanner, and the point cloud data is acquired by a substation scanning manner.
Further, in step S2, filtering and denoising according to different types of point cloud data by using different methods includes: filtering and denoising the ordered point cloud data by adopting a method comprising standard Gaussian and an average value method; scattered point cloud data mainly comprises bulk noise and isolated point noise, and filtering and denoising are carried out by adopting a K neighborhood method of discrete points on the basis of grid network topology reconstruction.
Further, in step S3, the number of the control targets uniformly arranged is 4-5; and taking the first measuring station as a reference station, and splicing the point cloud data of the next measuring station with the adjacent previous measuring station respectively.
Further, in step S4, the step of registering includes: obtaining a conversion relation between different space coordinate systems based on the point cloud coordinates and the engineering coordinates of the control target; and converting point cloud data under different coordinate systems into the engineering coordinate system according to the conversion relation among the coordinate systems by taking the engineering coordinate system as a reference.
Further, in step S5, the determining the extracted profile baseline parallel to the tunnel axis refers to: performing attitude adjustment on the base line through the rotation matrix based on the corresponding angle component, and converting the base line into an extracted profile base line in a local coordinate system; the local coordinate system is: and establishing a coordinate system by taking the advancing direction of the tunnel as an axis Y, the direction of the section of the tunnel as an axis X and the vertical direction as an axis Z.
Further, in step S6, the height of the extracted cross section and the distance between adjacent cross sections are fixed values.
Further, in step S7, the method of point-to-grid refers to: and taking the point cloud data provided by the point cloud profile at one stage as comparison point cloud data, fitting the point cloud data provided by the point cloud profile at the other stage into a curved surface as a reference surface, and calculating the distance between the point in the comparison point cloud data and the reference surface.
A storage device, characterized by: the storage device stores instructions and data for realizing the tunnel deformation monitoring method based on the point cloud profile.
The utility model provides a tunnel deformation monitoring facilities based on point cloud profile which characterized in that: the method comprises the following steps: a processor and the storage device; the processor loads and executes instructions and data in the storage device to realize the tunnel deformation monitoring method based on the point cloud profile.
Drawings
The invention will be further described with reference to the accompanying drawings and examples, in which:
fig. 1 is a flowchart of a tunnel deformation monitoring method based on a point cloud profile in an embodiment of the present invention;
FIG. 2 is a schematic diagram of point cloud data stitching according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of a baseline local coordinate system transformation in an embodiment of the invention;
FIG. 4 is a schematic diagram of a point cloud profile extraction according to an embodiment of the present invention;
FIG. 5 is a schematic cross-sectional view of an embodiment of the invention;
FIG. 6 is a model diagram of a point-to-grid method in an embodiment of the invention;
fig. 7 is a schematic diagram of the operation of the hardware device in the embodiment of the present invention.
Detailed Description
For a more clear understanding of the technical features, objects and effects of the present invention, embodiments of the present invention will now be described in detail with reference to the accompanying drawings.
The embodiment of the invention provides a tunnel deformation monitoring method, equipment and storage equipment based on a point cloud profile, wherein point cloud data of different periods of a tunnel are obtained through a laser scanner, the data of each period are subjected to denoising and splicing, the data of different periods are subjected to registration, then the point cloud data of the profile are extracted according to a certain distance, and on the basis, the tunnel is subjected to deformation monitoring by using a point-to-model method to obtain the deformation of the tunnel; a tunnel deformation monitoring device and a storage device based on a point cloud profile are used for realizing a tunnel deformation monitoring method based on the point cloud profile.
Referring to fig. 1, fig. 1 is a flowchart of a tunnel deformation monitoring method based on a point cloud profile in an embodiment of the present invention, which mainly includes:
s1: scanning by using a scanner and acquiring point cloud data of different stages of the tunnel; the scanner is a laser scanner and collects point cloud data in a substation scanning mode;
s2: according to different types of point cloud data in different periods, filtering and denoising the point cloud data by adopting different methods; the filtering and denoising by adopting different methods according to different types of point cloud data comprises the following steps: filtering and denoising the ordered point cloud data by adopting a method comprising standard Gaussian and an average value method; scattered point cloud data mainly comprises cluster noise and isolated point noise, and is filtered and denoised by adopting a K neighborhood method of discrete points on the basis of grid network topology reconstruction;
s3: splicing point cloud data between adjacent measuring stations in the same period according to control targets distributed at the connection part between the adjacent measuring stations of the tunnel; the number of the control targets which are uniformly distributed is 4-5; taking a first survey station as a reference station, and splicing point cloud data of the next survey station with the adjacent previous survey station respectively;
s4: registering the spliced point cloud data of different phases according to a registration model by using the control target, and converting the point cloud data into the same coordinate system; the step of registering comprises: obtaining a conversion relation between different space coordinate systems based on the point cloud coordinates and the engineering coordinates of the control target; converting point cloud data under different coordinate systems into an engineering coordinate system according to a conversion relation among the coordinate systems by taking the engineering coordinate system as a reference; the registration model is shown in equation (1):
wherein, Pc＝(xc,yc,zc) For obtaining independent coordinates of the control target using the scanner, Pa＝(xa,ya,za) In order to obtain the engineering coordinates of the control target by using a total station, Δ x, Δ y, Δ z are translation parameters, phi, omega, and kappa are included angles between two point cloud coordinate vectors and coordinate axes, and are also coordinate rotation parameters, R (phi, omega, kappa) is a rotation matrix, and the formula of R (phi, omega, kappa) is shown in formula (2):
7 parameters in the coordinate transformation relationship are solved: coordinate scaling factor, translation three-parameter and coordinate rotation three-parameter, because the length ratio in two different coordinate systems is the same by default, the value of the coordinate scaling factor is 1, and 6 parameters to be solved exist in the registration model
Three parameters of translation Δ x, Δ y, Δ z and three parameters of coordinate rotation φ, ω, κ, the relationship shown in equations (3), (4) exists through orthogonal matrices:
according to the registration model, combining the formulas (1) to (4), expanding the parameters to be solved according to the series to obtain an error equation as shown in the formula (5):
wherein V is a matrix of corrected numbers after the engineering coordinate adjustment, B is a coefficient matrix,
l is a constant value as a parameter to be solved, an
Wherein the content of the first and second substances,
and
respectively representing the corrected numbers of the point cloud i after coordinate adjustment in X, Y and Z directions;
and
initial values representing translation parameters;
representing a rotation matrix
An initial value of (1);
according to the least-squares principle, in equation (5)
Satisfy VTThe requirement of minimum PV value obtains a formula (8):
wherein V is a matrix of corrected numbers after the engineering coordinate adjustment, B is a coefficient matrix,
p is a weight matrix for the parameter to be solved.
The equation (8) is transposed, resulting in equation (9):
BTPV＝0(9)
substituting equation (5) into equation (9) yields equation (10):
according to the formula (10), the parameter to be determined is obtained
As shown in formula (11):
as can be seen from equation (12):
wherein l is a constant, P is a weight matrix,
and B is a coefficient array, n is the number of observed values, and t is the necessary observed number.
According to the precision evaluation formula (13), an estimation value of the variance of the parameter to be solved can be calculated to measure the precision of the parameter to be solved:
wherein the content of the first and second substances,
and r is a redundant observation number, P is a weight matrix, and V is a corrected number matrix after the mean difference of the engineering coordinates.
S5: according to point cloud data in the same coordinate system, performing global fitting on the tunnel, and determining an extracted profile base line parallel to the axis of the tunnel; the step of determining the extraction profile base line parallel to the tunnel axis refers to the following steps: performing attitude adjustment on the base line through the rotation matrix based on the corresponding angle component, and converting the base line into an extracted profile base line in a local coordinate system; the local coordinate system is: a coordinate system is established by taking the advancing direction of the tunnel as an axis Y, the direction of the section of the tunnel as an axis X and the vertical direction as an axis Z;
s6: according to the extracted profile base line, performing cross section interception in the orthogonal direction on the point cloud data of the tunnel trend to obtain a point cloud profile; the height of the extracted section and the distance between adjacent sections are fixed values;
s7: processing the point cloud profile using a point-to-grid method; the point-to-grid approach refers to: using the point cloud data provided by the point cloud profile of one stage as comparison point cloud data, fitting the point cloud data provided by the point cloud profile of the other stage into a curved surface as a reference surface, and calculating the distance between a point in the comparison point cloud data and the reference surface;
s8: and obtaining the distance from the point to the reference surface in the comparison data obtained according to the method from the point to the grid, namely the monitored tunnel deformation.
Referring to fig. 2, fig. 2 is a schematic diagram of point cloud data splicing in an embodiment of the present invention, where 4 control targets are uniformly distributed according to a connection portion between a survey station 1 and a survey station 2, and the point cloud data between adjacent survey stations are spliced to ensure that enough point cloud data is provided for extracting tunnel deformation information.
Referring to fig. 3, fig. 3 is a schematic diagram of transformation of a baseline local coordinate system in an embodiment of the present invention, a local coordinate system is established with a tunnel advancing direction as a Y-axis, a tunnel profile direction as an X-axis, and a vertical direction as a Z-axis, and a direction of a tunnel point cloud axis is parallel to the Y-axis. In order to intercept the orthogonal direction of the tunnel trend, the posture of the base line needs to be adjusted based on the corresponding angle component through the rotation matrix, and the base line 2 is respectively rotated around the Z coordinate axis and the X coordinate axis by the angle value
The base line 3 is rotated so that the normal direction of the cut position coincides with the orthogonal direction of the Y coordinate axis.
Referring to fig. 4, fig. 4 is a schematic diagram of point cloud profile extraction in the embodiment of the invention, 5 is a distance between adjacent profiles, 6 is a height of extracted profile, both the height of extracted profile and the distance between adjacent profiles are fixed values, and tunnel point cloud data [ x ]iyizi]TAnd intercept point [ x0y0z0]TAfter transformation, the corresponding coordinate value [ x ] is obtained0' y0' z0']TCutting a section:
[xiyizi]T＝AB[xiyizi]T(8)
[x0' y0' z0']T＝AB[x0y0z0]T(9)
wherein the content of the first and second substances,
and obtaining a section coordinate value [ x ' y ' z ']TAs shown in equation (10):
[x' y' z']T＝[x0' y0' z0']T+R[cosθ 0 sinθ]T(10)
wherein, theta belongs to [0,2 pi ], and is an angle value corresponding to a connecting line of the section point and the interception point on the axis.
Referring to fig. 5, fig. 5 is a schematic cross-sectional view of an embodiment of the invention, and fig. 7 is a point cloud cross-section extracted.
Referring to fig. 6, fig. 6 is a model diagram of a point-to-grid method in an embodiment of the present invention, where 8 is reference point cloud data, 9 is comparison point cloud data, 10 is a distance along a local ridge of the grid, 11 is a curved surface, i.e., a reference surface, to be synthesized by the reference point cloud data 8, and 12 is a distance from any point in the comparison point cloud data 9 to the reference surface 11, i.e., a deformation amount of the tunnel.
Referring to fig. 7, fig. 7 is a schematic diagram of a hardware device according to an embodiment of the present invention, where the hardware device specifically includes: a tunnel deformation monitoring device 701 based on a point cloud profile, a processor 702 and a storage device 703 are provided.
A tunnel deformation monitoring device 701 based on a point cloud profile: the tunnel deformation monitoring equipment 701 based on the point cloud profile realizes the tunnel deformation monitoring method based on the point cloud profile.
The processor 702: the processor 702 loads and executes instructions and data in the storage device 703 for implementing the tunnel deformation monitoring method based on the point cloud profile.
The storage device 703: the storage device 703 stores instructions and data; the storage device 703 is used to implement the tunnel deformation monitoring method based on the point cloud profile.
The invention has the beneficial effects that: the method provided by the invention has the advantages that the number of the profile point clouds extracted is small, the data processing efficiency is improved, meanwhile, the fitting error caused by the fitting of the profile point clouds is avoided, the practicability is high, and the precision is high.
While the present invention has been described with reference to the embodiments shown in the drawings, the present invention is not limited to the embodiments, which are illustrative and not restrictive, and it will be apparent to those skilled in the art that various changes and modifications can be made therein without departing from the spirit and scope of the invention as defined in the appended claims.
Claims (9)
1. A tunnel deformation monitoring method based on a point cloud profile is characterized by comprising the following steps: the method comprises the following steps:
s1: scanning by using a scanner and acquiring point cloud data of different stages of the tunnel;
s2: according to different types of point cloud data in different periods, filtering and denoising the point cloud data by adopting different methods; different types of the point cloud data adopt different methods for filtering and denoising, and the method comprises the following steps: filtering and denoising the ordered point cloud data by adopting a method comprising standard Gaussian and an average value method; scattered point cloud data mainly comprise cluster noise and isolated point noise, and filtering and denoising are carried out by adopting a K neighborhood method of discrete points on the basis of grid network topology reconstruction;
s3: splicing point cloud data between adjacent measuring stations in the same period according to control targets distributed at the connection part between the adjacent measuring stations of the tunnel;
s4: registering the spliced point cloud data of different phases according to a registration model by using the control target, and converting the point cloud data into the same coordinate system;
the step of registering comprises: obtaining a conversion relation between different space coordinate systems based on the point cloud coordinates and the engineering coordinates of the control target; converting point cloud data under different coordinate systems into an engineering coordinate system according to a conversion relation among the coordinate systems by taking the engineering coordinate system as a reference; the registration model is shown in equation (1):
wherein, Pc＝(xc,yc,zc) For obtaining independent coordinates of the control target using the scanner, Pa＝(xa,ya,za) In order to obtain engineering coordinates of the control target by using a total station, delta x, delta y and delta z are translation parameters, phi, omega and kappa are included angles between two point cloud coordinate vectors and coordinate axes and coordinate rotation parameters, R (phi, omega and kappa) is a rotation matrix, and R (R), (Y, omega and kappa) isPhi, omega, kappa) is shown in equation (2):
7 parameters in the coordinate transformation relationship are solved: coordinate scaling factor, translation three-parameter and coordinate rotation three-parameter, because the length ratio in two different coordinate systems is the same by default, the value of the coordinate scaling factor is 1, and 6 parameters to be solved exist in the registration model
Three parameters of translation Δ x, Δ y, Δ z and three parameters of coordinate rotation φ, ω, κ, the relationship shown in equations (3), (4) exists through orthogonal matrices:
according to the registration model, combining the formulas (1) to (4), expanding the parameters to be solved according to the series to obtain an error equation as shown in the formula (5):
wherein V is a matrix of corrected numbers after the engineering coordinate adjustment, B is a coefficient matrix,
for the parameter to be solved, l is a constant, and
wherein the content of the first and second substances,
and
respectively representing the corrected numbers of the point cloud i after coordinate adjustment in X, Y and Z directions;
and
initial values representing translation parameters;
representing a rotation matrix
An initial value of (1);
according to the least-squares principle, in equation (5)
Satisfy VTThe requirement of minimum PV value obtains a formula (8):
wherein V is a matrix of corrected numbers after the engineering coordinate adjustment, B is a coefficient matrix,
p is a weight matrix for the parameter to be solved;
the equation (8) is transposed, resulting in equation (9):
BTPV＝0 (9)
substituting equation (5) into equation (9) yields equation (10):
according to the formula (10), the parameter to be determined is obtained
As shown in formula (11):
as can be seen from equation (12):
wherein l is a constant, P is a weight matrix,
b is a coefficient array, n is the number of observed values, and t is a necessary observed number;
according to the precision evaluation formula (13), an estimation value of the variance of the parameter to be solved can be calculated to measure the precision of the parameter to be solved:
wherein the content of the first and second substances,
the variance of the parameter to be evaluated is evaluated, r is a redundant observation number, P is a weight matrix, and V is a correction matrix after the mean difference of the engineering coordinates;
s5: according to point cloud data in the same coordinate system, performing global fitting on the tunnel, and determining an extracted profile base line parallel to the axis of the tunnel;
s6: according to the extracted profile base line, performing cross section interception in the orthogonal direction on the point cloud data of the tunnel trend to obtain a point cloud profile;
s7: processing the point cloud profile using a point-to-grid method;
s8: and comparing the distances from the points in the point cloud data to a reference surface, namely the monitored tunnel deformation, according to a point-to-grid method.
2. The tunnel deformation monitoring method based on the point cloud profile as claimed in claim 1, wherein: in step S1, the scanner is a laser scanner, and the point cloud data is acquired by a substation scanning method.
3. The tunnel deformation monitoring method based on the point cloud profile as claimed in claim 1, wherein: in step S3, the number of the control targets uniformly arranged is 4 to 5; and taking the first measuring station as a reference station, and splicing the point cloud data of the next measuring station with the adjacent previous measuring station respectively.
4. The tunnel deformation monitoring method based on the point cloud profile as claimed in claim 1, wherein: in step S4, the step of registering includes: obtaining a conversion relation between different space coordinate systems based on the point cloud coordinates and the engineering coordinates of the control target; and converting point cloud data under different coordinate systems into the engineering coordinate system according to the conversion relation among the coordinate systems by taking the engineering coordinate system as a reference.
5. The tunnel deformation monitoring method based on the point cloud profile as claimed in claim 1, wherein: in step S5, the determining the extracted profile baseline parallel to the tunnel axis is: performing attitude adjustment on the base line through the rotation matrix based on the corresponding angle component, and converting the base line into an extracted profile base line in a local coordinate system; the local coordinate system is: and establishing a coordinate system by taking the advancing direction of the tunnel as an axis Y, the direction of the section of the tunnel as an axis X and the vertical direction as an axis Z.
6. The tunnel deformation monitoring method based on the point cloud profile as claimed in claim 1, wherein: in step S6, the height of the extracted cross section and the distance between adjacent cross sections are fixed values.
7. The tunnel deformation monitoring method based on the point cloud profile as claimed in claim 1, wherein: in step S7, the method of point-to-grid refers to: and taking the point cloud data provided by the point cloud profile at one stage as comparison point cloud data, fitting the point cloud data provided by the point cloud profile at the other stage into a curved surface as a reference surface, and calculating the distance between the point in the comparison point cloud data and the reference surface.
8. A storage device, characterized by: the storage device stores instructions and data for implementing the tunnel deformation monitoring method based on the point cloud profile as claimed in any one of claims 1 to 7.
9. The utility model provides an equipment of tunnel deformation monitoring based on point cloud profile which characterized in that: the method comprises the following steps: a processor and a storage device; the processor loads and executes instructions and data in the storage device to realize the tunnel deformation monitoring method based on the point cloud profile as claimed in any one of claims 1 to 7.
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| CN109900211A (en) * | 2019-03-22 | 2019-06-18 | 贵州大学 | A kind of monitoring of underground engineering wall rock displacement and data processing system and method |
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| CN112989453B (en) * | 2021-01-28 | 2022-12-06 | 徐洋洋 | BIM-based holographic deformation information extraction method |
| CN113157005B (en) * | 2021-04-22 | 2022-11-11 | 中煤科工集团重庆研究院有限公司 | Rotating holder for monitoring deformation of tunnel section and control system thereof |
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