CN117190911A - Linear monitoring method for steel truss arch bridge construction based on three-dimensional laser scanning - Google Patents
Linear monitoring method for steel truss arch bridge construction based on three-dimensional laser scanning Download PDFInfo
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Abstract
The application discloses a linear monitoring method for steel truss arch bridge construction based on three-dimensional laser scanning, and relates to the technical field of steel truss arch bridge construction. Comprising the following steps: arranging a plurality of monitoring areas on the steel truss arch bridge; scanning a monitoring area by adopting a structured light scanning technology to generate a first group of point cloud data; scanning the monitoring area by using a TOF scanning technology to generate a second group of point cloud data; carrying out data fusion on the two groups of point cloud data to generate a fusion data set; carrying out data processing on the fusion data set to generate a point cloud image; generating a BIM model based on the point cloud image; and (5) comparing the BIM model with a construction structure diagram to adjust linear change. The application combines the structured light scanning and TOF scanning technology to monitor construction linear change, thereby improving the accuracy and efficiency of linear monitoring; the point cloud data and accurate linear analysis are processed rapidly and efficiently through data fusion; the accuracy, comprehensiveness, instantaneity and high efficiency of linear monitoring are further guaranteed through the BIM model.
Description
Technical Field
The application belongs to the technical field of steel truss arch bridge construction, and particularly relates to a linear monitoring method for steel truss arch bridge construction based on three-dimensional laser scanning.
Background
The bridge construction monitoring technology is one of important components of the bridge construction technology, and can ensure that the bridge structure meets the calculation stress state and reasonable construction linearity in the construction process. The bridge construction line is complex and changeable due to the influence of complex conditions such as construction load, temperature load and wind load, and the bridge construction line monitoring is very important in practical engineering. Therefore, line-shaped monitoring needs to be carried out on bridge structures under different construction working conditions in actual engineering, so that external interference such as temperature, wind power and the like is eliminated, the steel truss arch bridge construction line shape can be monitored rapidly and synchronously, the accuracy and the instantaneity of construction linearity are guaranteed, and the problem to be solved is urgent.
Disclosure of Invention
The application aims to provide a linear monitoring method for steel truss arch bridge construction based on three-dimensional laser scanning, which monitors linear change of a steel truss arch bridge in the construction process by adopting a mode of combining a structured light scanning technology and a TOF scanning technology, fuses and processes generated point cloud data, generates a BIM model by adopting a BIM technology, and corrects and adjusts the linear change by comparing the difference between the BIM model and a construction structure diagram.
The aim of the application can be achieved by the following technical scheme:
the embodiment of the application provides a steel truss arch bridge construction linear monitoring method based on three-dimensional laser scanning, wherein the three-dimensional laser scanning technology comprises a structured light scanning technology and a TOF scanning technology, and the method comprises the following steps of:
preferably, a plurality of monitoring areas are arranged on the steel truss arch bridge; the monitoring area is used for monitoring the linear change of the steel truss arch bridge in the construction process;
scanning the monitoring area by adopting the structured light scanning technology to generate a first group of point cloud data;
scanning the monitoring area by adopting the TOF scanning technology to generate a second group of point cloud data;
performing data fusion on the first group of point cloud data and the second group of point cloud data to generate a fusion data set;
performing data processing on the fusion data set to generate a point cloud image;
generating a BIM model based on the point cloud image by adopting a BIM technology;
comparing the BIM model with a construction structure diagram, and correcting and adjusting the linear change;
the data processing comprises data denoising, data reduction, data filtering, data splicing, data segmentation, surface smoothing and hole filling.
Preferably, the scanning of the monitoring area by using the structured light scanning technology specifically includes the following steps:
a structured light monitoring device is arranged in the monitoring area; the structure light monitoring device comprises a structure light scanning device and a camera;
configuring an ambient light filtering device for the structured light monitoring device;
projecting a structured light pattern onto a monitoring target surface using the structured light scanning apparatus;
acquiring a structured light projection image of the surface of the monitoring target by using the camera;
performing image processing on the structured light projection image, extracting structured light deformation information of the surface of the monitoring target, and generating the first group of point cloud data;
calculating the three-dimensional coordinates of each pixel point on the surface of the monitoring target according to the structured light deformation information to obtain a three-dimensional model;
wherein, ambient light filter device includes band-pass filter and colour filter.
Preferably, a reflectivity compensation technique is used in the process of performing the structured light scanning, and specifically includes the following steps:
image acquisition is carried out on the background scene of the monitoring target, and a background image is obtained;
establishing a reflectivity model according to the material characteristics of the monitoring target;
comparing the structured light projection image with the background image to obtain an image difference;
calculating a reflectivity compensation value by using the reflectivity model according to the image difference;
the reflectivity compensation value is applied to the structured light projection image.
Preferably, the TOF scanning technology is adopted to scan the monitoring area, and the method specifically comprises the following steps:
configuring a TOF monitoring device in the monitoring area; wherein the TOF monitoring device comprises an optical pulse transmitting device and an optical pulse receiving device;
transmitting an optical pulse to a surface of a monitoring target through the optical pulse transmitting device;
the monitoring target surface reflects and scatters the light pulse to form a reflected light pulse and a scattered light pulse;
the light pulse receiving device receives the reflected light pulse;
recording the transmitting time and the receiving time of the light pulse, and calculating the time difference of the transmitting time and the receiving time;
calculating the distance or depth of a monitoring target according to the speed of the light pulse and the time difference;
adjusting the positions of the light pulse transmitting device and the light pulse receiving device, comprehensively scanning a plurality of monitoring targets in the monitoring area, and generating the second group of point cloud data;
wherein, calculate the distance or depth of the said monitoring target, its formula is expressed as:
c is the propagation speed of the light pulse in the medium, delta t is the time difference, and rd is the distance to be measured;
wherein, in the process of scanning by the TOF scanning technology, the time delay generated in the propagation process of the light pulse is corrected by adopting distance compensation.
Preferably, the first set of point cloud data and the second set of point cloud data are subjected to data fusion by adopting a mode of combining an F-ICP algorithm and Gauss Newton method.
Preferably, the data fusion is performed by adopting the F-ICP algorithm, which comprises the following steps:
s61, searching each original characteristic point p in the target point cloud Q i The point with the nearest distance is removed, and the point with the maximum 30% is taken as an off-office point;
s62, calculating a simplified error function by using Frobenius norms to obtain a rotation matrix R k And translation vector t k The reduced error function is expressed as
S is the number of points left by the original point cloud P after the extra points are removed; k is the number of neighborhood points near the feature points; p is p i Represents the original feature points, q i Representing target feature points;
wherein, the original point cloud p= { P i I=1, 2, n, target point cloud q= { Q i }，i＝1，2，...，m；
S63, rotating the matrix R k And translation vector t k Applied to original characteristic point cloud P k Obtaining a transformation characteristic point cloud P k+1 ；
S64, respectively calculatingΔR＝R k -R k-1 And Δt=t k -t k-1 The method comprises the steps of carrying out a first treatment on the surface of the If epsilon k If the value is smaller than the threshold value or the delta R and delta t are smaller than the threshold value, stopping iteration; otherwise, using the transformation characteristic point cloud P k+1 Substitute original feature point cloud P k The steps S61 to S63 are repeated.
Preferably, regarding the step S62, the method further includes the steps of:
s621, calculating center moment
S622, carrying out initial position normalization on the point cloud, wherein P' = { P i -μ p |p i ∈P，i＝1，2，...，S}，Q′＝{q i -μ q |q i ∈Q，i＝1，2，...，S}；
S623, calculate P 'Q' T Singular value decomposition is carried out to obtain U and V;
s624, calculate R k ＝UV T And t k ＝μ q -R k μ p 。
Preferably, the F-error function is obtained using the nature of the Frobenius norm, expressed as:
where tr is the trace of the matrix;
the nature of the Frobenius norm is expressed as:<A，B> F ＝tr(AB T )；
wherein,the Frobenius norm, denoted as matrix a;
the matrix a is expressed as:is an m x n matrix.
Preferably, the minimum error function is obtained according to the F-error function, expressed as:
wherein,and->Is not associated with the rotation matrix R;
when (when)At maximum, the F-error function reaches a minimum, the condition r=uv is satisfied T
Wherein U and V are defined by P 'Q' T Singular value decomposition and acquisition;
wherein t=u T RV is an orthogonal matrix, and T ii ||＝1。
Preferably, generating the BIM model includes the steps of:
s101, carrying out data denoising on the fusion data set;
s102, carrying out data reduction on the fusion data set;
s103, carrying out data registration on the fusion data set;
s104, building a grid based on the fusion data set;
s105, reconstructing a curved surface based on the fusion data set;
the data denoising adopts a Gaussian algorithm, a median algorithm and a filtering algorithm;
the Gaussian algorithm is used for keeping the form of the original data;
the median algorithm is used for eliminating burrs on the surface of the model;
the data reduction adopts one or more of a bounding box method, a geometric image reduction method, a curvature reduction method and a normal precision reduction method.
The beneficial effects of the application are as follows:
(1) According to the application, the linear change of the steel truss arch bridge in the construction process is monitored by adopting a mode of combining a structured light scanning technology and a TOF scanning technology, and the respective advantages of the two scanning technologies can be fully brought into play by adopting the combination mode, so that the accuracy and the efficiency of linear monitoring are improved; by fusing and processing the generated point cloud data, quick and efficient data processing and accurate linear analysis are realized; and then a BIM model is generated by adopting a BIM technology, and the linear change is corrected and regulated by comparing the difference between the BIM model and a construction structure diagram, so that the accuracy, the comprehensiveness, the real-time performance and the high efficiency of linear monitoring are further ensured.
(2) The application adopts the structured light scanning technology to scan the monitoring area, captures and analyzes the light patterns projected on the surface of the monitoring target by projecting specific light patterns or grating structures and using the camera to obtain the three-dimensional shape information of the surface of the monitoring target, provides the bridge surface shape information with high precision and high resolution, and accurately measures the detail part, thereby ensuring the accuracy of the monitoring result.
(3) According to the application, the ambient light filtering device is configured in the scanning process, and the reflectivity compensation technology is adopted to filter out the ambient light which is not matched with the frequency of the structural light, so that the interference of the environment is eliminated to the maximum extent, and the accuracy and the reliability of the structural light scanning are improved.
(4) According to the application, a TOF scanning technology is adopted to scan a monitoring area, and distance information of the surface of a monitoring target is calculated by sending a pulse laser beam and measuring the round-trip time of a laser signal, so that the linear change of the whole bridge is monitored, and the reconstruction of a follow-up three-dimensional model is facilitated; the method has the characteristics of high speed and good instantaneity, greatly improves the efficiency of linear monitoring, and ensures the instantaneity.
(5) The application combines the structure light scanning technology and the TOF scanning technology, firstly obtains high-precision three-dimensional shape information through the structure light scanning, and then utilizes the TOF scanning to carry out rapid large-scale monitoring, and the combination of the structure light scanning technology and the TOF scanning technology can realize comprehensive steel truss arch bridge construction linear monitoring and simultaneously meet the requirements of high precision and high efficiency.
(6) According to the application, the point cloud data are subjected to data fusion in a mode of combining an F-ICP algorithm and a Gauss Newton method, and a more complete and accurate three-dimensional model or point cloud representation is generated by registering and combining a plurality of groups of point cloud data; gaps among different point cloud data sources can be eliminated through data fusion, the coverage rate and the accuracy of point cloud are improved, meanwhile, the fused point cloud data can provide richer information, more accurate and stable linear monitoring results can be obtained, and more accurate analysis, modeling and application are facilitated.
(7) The method utilizes the Frobenius norm to make improvement on the traditional ICP algorithm, deduces the rapid ICP algorithm, can reach the same registration precision as the traditional ICP algorithm in extremely short time, and is more beneficial to real-time target identification and detection of the three-dimensional point cloud; the rotation matrix and the translation matrix between the point cloud data are estimated by obtaining the minimum error function, so that the problems that a large amount of operation and storage resources are needed and the consumed time is long in the traditional ICP algorithm are solved, the targets are processed in real time under the condition that information is not lost completely, the robustness and the stability of the algorithm are enhanced, and the registration efficiency is further improved.
(8) According to the method, the BIM model is generated based on the point cloud image, three-dimensional modeling and information management of the steel truss arch bridge are realized by converting the point cloud data into the BIM model, the construction efficiency, accuracy and visualization degree of the steel truss arch bridge are improved, and optimization of cooperation and decision making processes is promoted.
Drawings
For a better understanding and implementation, the technical solution of the present application is described in detail below with reference to the accompanying drawings.
Fig. 1 is a step flow chart of a method for monitoring the construction alignment of a steel truss arch bridge based on three-dimensional laser scanning, which is provided by the embodiment of the application;
FIG. 2 is a flowchart illustrating steps for scanning a monitoring area using a structured light scanning technique according to an embodiment of the present application;
FIG. 3 is a flowchart illustrating steps for employing a reflectivity compensation technique according to an embodiment of the present application;
FIG. 4 is a flowchart illustrating steps for scanning a monitoring area using TOF scanning according to an embodiment of the present application;
FIG. 5 is a flowchart illustrating steps for generating BIM model based on point cloud image according to an embodiment of the present application;
fig. 6 is a schematic layout diagram of a three-dimensional laser scanner based on linear monitoring of steel truss arch bridge construction according to an embodiment of the present application;
description of the reference numerals
In the figure: 1. a first three-dimensional laser scanner; 2. a second three-dimensional laser scanner; 3. constructing a control point; 4. a first temporary control point; 5. a second temporary control point; 6. a bridge deck; 7. steel truss girder; 8. steel truss arch; 9. a sling tower.
Detailed Description
For further explanation of the technical means and effects adopted by the present application for achieving the intended purpose, exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the accompanying drawings, the same numbers in different drawings refer to the same or similar elements, unless otherwise indicated. The implementations described in the following exemplary examples do not represent all implementations consistent with the application. Rather, they are merely examples of methods and systems that are consistent with aspects of the application as detailed in the accompanying claims.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used in this specification and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It should also be understood that the term "and/or" as used herein refers to any or all possible combinations including one or more of the associated listed items.
The following detailed description of specific embodiments, features and effects according to the present application is provided with reference to the accompanying drawings and preferred embodiments.
Referring to fig. 1, an embodiment of the application provides a method for monitoring a line shape of a steel truss arch bridge construction based on three-dimensional laser scanning, wherein the three-dimensional laser scanning technology comprises a structured light scanning technology and a TOF scanning technology, and specifically comprises the following steps:
s1, arranging a plurality of monitoring areas on a steel truss arch bridge;
the monitoring area is used for monitoring the linear change of the steel truss arch bridge in the construction process;
s2, scanning a monitoring area by adopting a structured light scanning technology to generate a first group of point cloud data;
s3, scanning the monitoring area by adopting a TOF scanning technology to generate a second group of point cloud data;
s4, carrying out data fusion on the first group of point cloud data and the second group of point cloud data to generate a fusion data set;
s5, carrying out data processing on the fusion data set to generate a point cloud picture;
s6, generating a BIM model based on the point cloud image by adopting a BIM technology;
s7, comparing the BIM model with a construction structure diagram, and correcting and adjusting linear changes;
the data processing comprises data denoising, data reduction, data filtering, data splicing, data segmentation, surface smoothing and hole filling.
Specifically, firstly, a plurality of linear monitoring areas are arranged on a steel truss arch bridge; secondly, scanning a monitoring area by adopting a structured light scanning technology and a TOF scanning technology respectively, and generating a first group of point cloud data and a second group of point cloud data respectively; performing data fusion on the first group of point cloud data and the second group of point cloud data to generate a fusion data set; then, carrying out data processing on the fusion data set to generate a point cloud image; then, generating a BIM model based on the point cloud image by adopting a BIM technology; and finally, comparing the BIM model with an actual construction structure diagram, and correcting and adjusting the region or the part in which the linear change occurs.
According to the application, the linear change of the steel truss arch bridge in the construction process is monitored by adopting a mode of combining a structured light scanning technology and a TOF scanning technology, and the respective advantages of the two scanning technologies can be fully brought into play by adopting the combination mode, so that the accuracy and the efficiency of linear monitoring are improved; the generated point cloud data is subjected to data registration fusion, and data processing is performed in a plurality of data processing modes, so that rapid and efficient data processing and accurate linear analysis are realized; and then a BIM model is generated by adopting a BIM technology, and the linear change is corrected and regulated by comparing the difference between the BIM model and a construction structure diagram, so that the accuracy, the comprehensiveness, the real-time performance and the high efficiency of linear monitoring are further ensured.
The above steps S1 to S7 will be described in detail below.
Regarding step S1, a plurality of linear monitoring areas are arranged on a constructed steel truss arch bridge according to actual requirements, and the monitoring areas are used for monitoring the linear change of the steel truss arch bridge in the construction process in real time, including but not limited to arch bridge shape, displacement and deformation, bridge piers and foundations, bridge decks, pavement and other monitoring main bodies; the shape of the arch bridge comprises a bending curve, arch height and the like between the main span and the supporting point, the bridge can be ensured to be correctly built according to the design requirement by monitoring the geometric shape of the arch bridge, and any deviation or non-uniformity can be found in time; the displacement and deformation comprise cantilever sections, soffit, fulcrums and the like, the displacement and deformation conditions of all parts of the bridge are also monitored, and the structural safety of the bridge can be evaluated by monitoring the displacement and the deformation, so that the problem that the structure is possibly unstable or damaged can be found in time; the bridge pier and the foundation are mainly used for monitoring displacement, deformation and settlement of the bridge pier and the foundation, so that the stability of the bridge pier and the foundation can be ensured, and the problem that the safety of a bridge is possibly influenced can be timely identified and solved; and the bridge deck slab is monitored, the paving evenness and the paving deformation condition are monitored, and the smooth traffic and the safety of the bridge deck can be ensured.
Referring to step S2, referring to fig. 2, in an embodiment of the present application, a structured light scanning technique is used to scan a monitoring area, which specifically includes the following steps:
s21, configuring a structured light monitoring device in a monitoring area;
the structure light monitoring device comprises a structure light scanning device and a camera;
s22, configuring an ambient light filtering device for the structural light monitoring device;
s23, projecting a structured light pattern to the surface of the monitoring target by using a structured light scanning device;
the above-described structured light scanning device may be a light source, and the light source may employ a laser; the structured light pattern comprises stripes, lattice points or random textures.
S24, acquiring a structured light projection image of the surface of the monitoring target by using a camera;
s25, performing image processing on the structured light projection image, extracting structured light deformation information of the surface of the monitoring target, and generating a first group of point cloud data;
s26, calculating the three-dimensional coordinates of each pixel point on the surface of the monitoring target according to the structured light deformation information to obtain a three-dimensional model;
the ambient light filtering device comprises a band-pass filter and a color filter.
Further, as shown in fig. 3, the reflectivity compensation technique is adopted in the process of performing the structured light scanning, and specifically includes the following steps:
s31, acquiring images of a background scene of a monitoring target to obtain a background image;
s32, establishing a reflectivity model according to the material characteristics of the monitoring target;
s33, comparing the structured light projection image with the background image to obtain an image difference;
s34, calculating a reflectivity compensation value by using a reflectivity model according to the image difference;
and S35, applying the reflectivity compensation value to the structured light projection image.
With respect to step S3, as shown in fig. 4, in an embodiment provided by the present application, a TOF scanning technology is adopted to scan a monitored area, which specifically includes the following steps:
s41, configuring a TOF monitoring device in a monitoring area;
wherein the TOF monitoring device comprises an optical pulse transmitting device and an optical pulse receiving device;
s42, transmitting light pulses to the surface of the monitoring target through a light pulse transmitting device;
s43, the surface of the monitoring target reflects and scatters the light pulse to form a reflected light pulse and a scattered light pulse;
s44, the light pulse receiving device receives the reflected light pulse;
the light pulse receiving device adopts a photosensitive element such as a photodiode and the like.
S45, recording the transmitting time and the receiving time of the light pulse, and calculating the time difference of the transmitting time and the receiving time;
s46, calculating the distance or depth of the monitoring target according to the speed and the time difference of the light pulse;
s47, adjusting the positions of the light pulse emitting device and the light pulse receiving device, and comprehensively scanning a plurality of monitoring targets in a monitoring area to generate second group of point cloud data;
wherein, calculate the distance or depth of the monitoring target, its formula is expressed as:
c is the propagation speed of the light pulse in the medium, delta t is the time difference, and rd is the distance to be measured;
in the process of scanning by using TOF scanning technology, the time delay generated in the propagation process of the light pulse is corrected by using distance compensation.
Regarding step S4, in one embodiment provided by the present application, the data fusion is performed on the first set of point cloud data and the second set of point cloud data by adopting a combination method of an F-ICP algorithm and a gauss newton method; the F-ICP algorithm is expressed as a rapid ICP algorithm improved based on the traditional ICP algorithm.
Further, the F-ICP algorithm is adopted for data fusion, and the method comprises the following steps:
s61, searching each original characteristic point p in the target point cloud Q i The point with the nearest distance is removed, and the point with the maximum 30% is taken as an off-office point;
s62, calculating a simplified error function by using Frobenius norms to obtain a rotation matrix R k And translation vector t k The reduced error function is expressed as
S is the number of points left by the original point cloud P after the extra points are removed; k is the number of neighborhood points near the feature points; p is p i Represents the original feature points, q i Representing target feature points;
wherein, the original point cloud p= { P i I=1, 2, n, target point cloud q= { Q i }，i＝1，2，...，m；
S63, rotating the matrix R k And translation vector t k Applied to original feature points Cloud P k Obtaining a transformation characteristic point cloud P k+1 ；
S64, respectively calculatingΔR＝R k -R k-1 And Δt=t k -t k-1 The method comprises the steps of carrying out a first treatment on the surface of the If epsilon k If the value is smaller than the threshold value or the delta R and delta t are smaller than the threshold value, stopping iteration; otherwise, using the transformation characteristic point cloud P k+1 Substitute original feature point cloud P k The steps S61 to S63 are repeated.
Further, regarding the step S62, the method further includes the steps of:
s621, calculating center moment
S622, carrying out initial position normalization on the point cloud, wherein P' = { P i -μ p |p i ∈P，i＝1，2，...，S}，Q′＝{q i -μ q |q i ∈Q，i＝1，2，...，S}；
S623, calculate P 'Q' T Singular value decomposition is carried out to obtain U and V;
s624, calculate R k ＝UV T And t k ＝μ q -R k μ p 。
Further, the F-error function is obtained using the nature of the Frobenius norm, expressed as:
where tr is the trace of the matrix;
the nature of the Frobenius norm is expressed as:<A，B> F ＝tr(AB T )；
wherein,the Frobenius norm, denoted as matrix a;
the matrix a is expressed as:is an m x n matrix.
Further, a minimum error function is obtained according to the F-error function, and is expressed as:
wherein,and->Is not associated with the rotation matrix R;
when (when)At maximum, the F-error function reaches a minimum, the condition r=uv is satisfied T
Wherein U and V are defined by P 'Q' T Singular value decomposition and acquisition;
wherein t=u T RV is an orthogonal matrix, and T ii ||＝1。
In particular, the method comprises the steps of,and->Is not associated with the rotation matrix R; then the error function is to be minimized, namely +.>Max, since t=u T RV is an orthogonal matrix, then T ii The value of T is less than or equal to 1, so if the maximum value is to be obtained ii ||＝1。
With respect to step S6, as shown in fig. 5, in one embodiment provided by the present application, a BIM model is generated based on a point cloud image using a BIM technique, and includes the following steps:
s101, data denoising is carried out on the fusion data set;
s102, data reduction is carried out on the fusion data set;
s103, carrying out data registration on the fusion data set;
s104, building a grid based on the fusion data set;
s105, reconstructing a curved surface based on the fusion data set;
the data denoising adopts a Gaussian algorithm, a median algorithm and a filtering algorithm;
the Gaussian algorithm is used for keeping the form of the original data;
the median algorithm is used for eliminating burrs on the surface of the model;
wherein, the data compaction adopts one or more of bounding box method, geometric image compaction method, curvature compaction method and normal precision compaction method.
In another embodiment of the present application, as shown in fig. 6, a method for arranging a three-dimensional laser scanner based on linear monitoring of steel truss arch bridge construction is provided.
In the present embodiment, first, the first three-dimensional laser scanner 1 at the bridge side is arranged on the construction control point 3; then determining a first temporary control point 4 on the deck; then a second three-dimensional laser scanner 2 on the bridge deck is arranged by using the first temporary control point 4; then applying a rear intersection such that the first temporary control point 4 extends longitudinally along the bridge deck 6 to the second temporary control point 5; then the second three-dimensional laser scanner 2 on the bridge deck is utilized to rapidly scan on the bridge deck; and finally, forming a point cloud image from the scanning result to realize real-time monitoring. In this embodiment, the first three-dimensional laser scanner 1 and the second three-dimensional laser scanner 2 also monitor the positions of the steel truss girder 7, the steel truss arch 8, the sling tower 9, and the like in real time in a linear manner.
In summary, the application has the following beneficial effects:
(1) According to the application, the linear change of the steel truss arch bridge in the construction process is monitored by adopting a mode of combining a structured light scanning technology and a TOF scanning technology, and the respective advantages of the two scanning technologies can be fully brought into play by adopting the combination mode, so that the accuracy and the efficiency of linear monitoring are improved; by fusing and processing the generated point cloud data, quick and efficient data processing and accurate linear analysis are realized; and then a BIM model is generated by adopting a BIM technology, and the linear change is corrected and regulated by comparing the difference between the BIM model and a construction structure diagram, so that the accuracy, the comprehensiveness, the real-time performance and the high efficiency of linear monitoring are further ensured.
(2) The application adopts the structured light scanning technology to scan the monitoring area, captures and analyzes the light patterns projected on the surface of the monitoring target by projecting specific light patterns or grating structures and using the camera to obtain the three-dimensional shape information of the surface of the monitoring target, provides the bridge surface shape information with high precision and high resolution, and accurately measures the detail part, thereby ensuring the accuracy of the monitoring result.
(3) According to the application, the ambient light filtering device is configured in the scanning process, and the reflectivity compensation technology is adopted to filter out the ambient light which is not matched with the frequency of the structural light, so that the interference of the environment is eliminated to the maximum extent, and the accuracy and the reliability of the structural light scanning are improved.
(4) According to the application, a TOF scanning technology is adopted to scan a monitoring area, and distance information of the surface of a monitoring target is calculated by sending a pulse laser beam and measuring the round-trip time of a laser signal, so that the linear change of the whole bridge is monitored, and the reconstruction of a follow-up three-dimensional model is facilitated; the method has the characteristics of high speed and good instantaneity, greatly improves the efficiency of linear monitoring, and ensures the instantaneity.
(5) The application combines the structure light scanning technology and the TOF scanning technology, firstly obtains high-precision three-dimensional shape information through the structure light scanning, and then utilizes the TOF scanning to carry out rapid large-scale monitoring, and the combination of the structure light scanning technology and the TOF scanning technology can realize comprehensive steel truss arch bridge construction linear monitoring and simultaneously meet the requirements of high precision and high efficiency.
(6) According to the application, the point cloud data are subjected to data fusion in a mode of combining an F-ICP algorithm and a Gauss Newton method, and a more complete and accurate three-dimensional model or point cloud representation is generated by registering and combining a plurality of groups of point cloud data; gaps among different point cloud data sources can be eliminated through data fusion, the coverage rate and the accuracy of point cloud are improved, meanwhile, the fused point cloud data can provide richer information, more accurate and stable linear monitoring results can be obtained, and more accurate analysis, modeling and application are facilitated.
(7) The method utilizes the Frobenius norm to make improvement on the traditional ICP algorithm, deduces the rapid ICP algorithm, can reach the same registration precision as the traditional ICP algorithm in extremely short time, and is more beneficial to real-time target identification and detection of the three-dimensional point cloud; the rotation matrix and the translation matrix between the point cloud data are estimated by obtaining the minimum error function, so that the problems that a large amount of operation and storage resources are needed and the consumed time is long in the traditional ICP algorithm are solved, the targets are processed in real time under the condition that information is not lost completely, the robustness and the stability of the algorithm are enhanced, and the registration efficiency is further improved.
(8) According to the method, the BIM model is generated based on the point cloud image, three-dimensional modeling and information management of the steel truss arch bridge are realized by converting the point cloud data into the BIM model, the construction efficiency, accuracy and visualization degree of the steel truss arch bridge are improved, and optimization of cooperation and decision making processes is promoted.
Those of ordinary skill in the art will appreciate that the algorithm steps of the examples described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or as a combination of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
The present application is not limited to the above embodiments, but is capable of modification and variation in detail, and other modifications and variations can be made by those skilled in the art without departing from the scope of the present application.
Claims (10)
1. A linear monitoring method for steel truss arch bridge construction based on three-dimensional laser scanning is characterized in that: the three-dimensional laser scanning technology comprises a structured light scanning technology and a TOF scanning technology, and comprises the following steps:
arranging a plurality of monitoring areas on the steel truss arch bridge; the monitoring area is used for monitoring the linear change of the steel truss arch bridge in the construction process;
scanning the monitoring area by adopting the structured light scanning technology to generate a first group of point cloud data;
scanning the monitoring area by adopting the TOF scanning technology to generate a second group of point cloud data;
performing data fusion on the first group of point cloud data and the second group of point cloud data to generate a fusion data set;
performing data processing on the fusion data set to generate a point cloud image;
generating a BIM model based on the point cloud image by adopting a BIM technology;
comparing the BIM model with a construction structure diagram, and correcting and adjusting the linear change;
the data processing comprises data denoising, data reduction, data filtering, data splicing, data segmentation, surface smoothing and hole filling.
2. The linear monitoring method for steel truss arch bridge construction based on three-dimensional laser scanning of claim 1, which is characterized by comprising the following steps: scanning the monitoring area by adopting the structured light scanning technology, and specifically comprises the following steps:
a structured light monitoring device is arranged in the monitoring area; the structure light monitoring device comprises a structure light scanning device and a camera;
configuring an ambient light filtering device for the structured light monitoring device;
projecting a structured light pattern onto a monitoring target surface using the structured light scanning apparatus;
acquiring a structured light projection image of the surface of the monitoring target by using the camera;
performing image processing on the structured light projection image, extracting structured light deformation information of the surface of the monitoring target, and generating the first group of point cloud data;
calculating the three-dimensional coordinates of each pixel point on the surface of the monitoring target according to the structured light deformation information to obtain a three-dimensional model;
wherein, ambient light filter device includes band-pass filter and colour filter.
3. The method for monitoring the construction linearity of the steel truss arch bridge based on three-dimensional laser scanning as set forth in claim 2, wherein the method comprises the following steps: the reflectivity compensation technology is adopted in the process of carrying out the structured light scanning, and specifically comprises the following steps:
image acquisition is carried out on the background scene of the monitoring target, and a background image is obtained;
establishing a reflectivity model according to the material characteristics of the monitoring target;
comparing the structured light projection image with the background image to obtain an image difference;
calculating a reflectivity compensation value by using the reflectivity model according to the image difference;
the reflectivity compensation value is applied to the structured light projection image.
4. The linear monitoring method for steel truss arch bridge construction based on three-dimensional laser scanning of claim 1, which is characterized by comprising the following steps: scanning the monitoring area by adopting the TOF scanning technology, and specifically comprises the following steps:
configuring a TOF monitoring device in the monitoring area; wherein the TOF monitoring device comprises an optical pulse transmitting device and an optical pulse receiving device;
transmitting an optical pulse to a surface of a monitoring target through the optical pulse transmitting device;
the monitoring target surface reflects and scatters the light pulse to form a reflected light pulse and a scattered light pulse;
the light pulse receiving device receives the reflected light pulse;
recording the transmitting time and the receiving time of the light pulse, and calculating the time difference of the transmitting time and the receiving time;
calculating the distance or depth of a monitoring target according to the speed of the light pulse and the time difference;
adjusting the positions of the light pulse transmitting device and the light pulse receiving device, comprehensively scanning a plurality of monitoring targets in the monitoring area, and generating the second group of point cloud data;
wherein, calculate the distance or depth of the said monitoring target, its formula is expressed as:c is the propagation speed of the light pulse in the medium, delta t is the time difference, and rd is the distance to be measured;
wherein, in the process of scanning by the TOF scanning technology, the time delay generated in the propagation process of the light pulse is corrected by adopting distance compensation.
5. The linear monitoring method for steel truss arch bridge construction based on three-dimensional laser scanning of claim 1, which is characterized by comprising the following steps: and carrying out data fusion on the first group of point cloud data and the second group of point cloud data by adopting a mode of combining an F-ICP algorithm and a Gauss Newton method.
6. The linear monitoring method for steel truss arch bridge construction based on three-dimensional laser scanning of claim 5, which is characterized by comprising the following steps: the F-ICP algorithm is adopted for data fusion, and the method comprises the following steps:
s61, searching each original characteristic point p in the target point cloud Q i The point with the nearest distance is removed, and the point with the maximum 30% is taken as an off-office point;
s62, calculating a simplified error function by using Frobenius norms to obtain a rotation matrix R k And translation vector t k The reduced error function is expressed as
S is the number of points left by the original point cloud P after the extra points are removed; k is the number of neighborhood points near the feature points; p is p i Represents the original feature points, q i Representing target feature points;
wherein, the original point cloud p= { P i I=1, 2, n, target point cloud q= { Q i }，i＝1，2，...，m；
S63, rotating the matrix R k And translation vector t k Applied to original characteristic point cloud P k Obtaining a transformation characteristic point cloud P k+1 ；
S64, respectively calculatingΔR＝R k -R k-1 And Δt=t k -t k-1 The method comprises the steps of carrying out a first treatment on the surface of the If epsilon k If the value is smaller than the threshold value or the delta R and delta t are smaller than the threshold value, stopping iteration; otherwise, using the transformation characteristic point cloud P k+1 Substitute original feature point cloud P k The steps S61 to S63 are repeated.
7. The method for monitoring the construction linearity of the steel truss arch bridge based on three-dimensional laser scanning of claim 6, wherein the method comprises the following steps: regarding the step S62, the method further includes the steps of:
s621, calculating center moment
S622, carrying out initial position normalization on the point cloud, wherein P' = { P i -μ p |p i ∈P，i＝1，2，...，S}，Q′＝{q i -μ q |q i ∈Q，i＝1，2，...，S}；
S623, calculate P 'Q' T Singular value decomposition is carried out to obtain U and V;
s624, calculate R k ＝UV T And t k ＝μ q -R k μ p 。
8. The method for monitoring the construction linearity of the steel truss arch bridge based on three-dimensional laser scanning of claim 7, wherein the method comprises the following steps: the F-error function is obtained using the nature of the Frobenius norm, expressed as:
where tr is the trace of the matrix;
the nature of the Frobenius norm is expressed as:
wherein,the Frobenius norm, denoted as matrix a;
the matrix a is expressed as:is an m x n matrix.
9. The method for monitoring the construction linearity of the steel truss arch bridge based on three-dimensional laser scanning of claim 8, which is characterized by comprising the following steps: obtaining a minimum error function according to the F-error function, wherein the minimum error function is expressed as:
wherein,and->Is not associated with the rotation matrix R;
when (when)At maximum, the F-error function reaches a minimum, and the condition r=uv is satisfied T
Wherein U and V are defined by P 'Q' T Singular value decomposition and acquisition;
wherein t=u T RV is an orthogonal matrix, and T ii ||＝1。
10. The linear monitoring method for steel truss arch bridge construction based on three-dimensional laser scanning of claim 1, which is characterized by comprising the following steps: generating the BIM model comprises the following steps:
s101, carrying out data denoising on the fusion data set;
s102, carrying out data reduction on the fusion data set;
s103, carrying out data registration on the fusion data set;
s104, building a grid based on the fusion data set;
s105, reconstructing a curved surface based on the fusion data set;
the data denoising adopts a Gaussian algorithm, a median algorithm and a filtering algorithm;
the Gaussian algorithm is used for keeping the form of the original data;
the median algorithm is used for eliminating burrs on the surface of the model;
the data reduction adopts one or more of a bounding box method, a geometric image reduction method, a curvature reduction method and a normal precision reduction method.
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