CN114396871B - Prefabricated pier column installation position posture monitoring method based on three-dimensional laser scanning - Google Patents

Prefabricated pier column installation position posture monitoring method based on three-dimensional laser scanning Download PDF

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CN114396871B
CN114396871B CN202111633836.1A CN202111633836A CN114396871B CN 114396871 B CN114396871 B CN 114396871B CN 202111633836 A CN202111633836 A CN 202111633836A CN 114396871 B CN114396871 B CN 114396871B
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coordinate system
point cloud
point
scanning
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CN114396871A (en
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徐燕
骆义
张建
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Southeast University
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Southeast University
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
    • G01B11/00—Measuring arrangements characterised by the use of optical techniques
    • G01B11/002—Measuring arrangements characterised by the use of optical techniques for measuring two or more coordinates
    • E—FIXED CONSTRUCTIONS
    • E01—CONSTRUCTION OF ROADS, RAILWAYS, OR BRIDGES
    • E01D—CONSTRUCTION OF BRIDGES, ELEVATED ROADWAYS OR VIADUCTS; ASSEMBLY OF BRIDGES
    • E01D19/00—Structural or constructional details of bridges
    • E01D19/02—Piers; Abutments ; Protecting same against drifting ice
    • E—FIXED CONSTRUCTIONS
    • E01—CONSTRUCTION OF ROADS, RAILWAYS, OR BRIDGES
    • E01D—CONSTRUCTION OF BRIDGES, ELEVATED ROADWAYS OR VIADUCTS; ASSEMBLY OF BRIDGES
    • E01D21/00—Methods or apparatus specially adapted for erecting or assembling bridges
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
    • G01B11/00—Measuring arrangements characterised by the use of optical techniques
    • G01B11/24—Measuring arrangements characterised by the use of optical techniques for measuring contours or curvatures
    • E—FIXED CONSTRUCTIONS
    • E01—CONSTRUCTION OF ROADS, RAILWAYS, OR BRIDGES
    • E01D—CONSTRUCTION OF BRIDGES, ELEVATED ROADWAYS OR VIADUCTS; ASSEMBLY OF BRIDGES
    • E01D2101/00—Material constitution of bridges
    • E01D2101/20—Concrete, stone or stone-like material
    • E01D2101/24—Concrete
    • E01D2101/26—Concrete reinforced

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  • Engineering & Computer Science (AREA)
  • Architecture (AREA)
  • Civil Engineering (AREA)
  • Structural Engineering (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Length Measuring Devices By Optical Means (AREA)
  • Length Measuring Devices With Unspecified Measuring Means (AREA)

Abstract

The invention belongs to the field of construction monitoring, and provides a prefabricated pier column installation position posture monitoring method based on three-dimensional laser scanning. The method comprises the steps of using a three-dimensional surface model of embedded bars on a bridge pier column and a bearing platform to be installed as a reference of a site scanning model, and establishing a pier column local coordinate system and a design coordinate system; carrying out 360-degree surrounding scanning on a construction site by using a three-dimensional scanner to obtain scanning point cloud data of the three-dimensional scanner; calculating to obtain a three-dimensional transformation matrix; a design coordinate system defined by a three-dimensional transformation matrix; taking the allowable installation deviation of the prefabricated pier stud given in the specification as a reference, and feeding back the measured value to site constructors for installation adjustment; and judging that the hoisting is completed until the measured value is smaller than the allowable installation deviation. The three-dimensional scanner can be erected at any position on site, does not need to be lofted and positioned by a reference line, and does not need to be stuck with a marker on a pier column to be used as a measurement reference, so that the three-dimensional scanner is convenient to use and high in efficiency.

Description

Prefabricated pier column installation position posture monitoring method based on three-dimensional laser scanning
Technical Field
The invention belongs to the field of construction monitoring, and provides a prefabricated pier column installation position posture monitoring method based on three-dimensional laser scanning.
Background
The assembled bridge has the advantages of improving construction speed, ensuring construction quality, reducing traffic influence and the like, and is greatly developed and applied in recent years. The connection part of the prefabricated pier column and the bearing platform is often the position with the largest bending moment and is also the position where plastic hinge is easy to appear under the action of earthquake, and the connection part has important influence on the overall safety and durability of the bridge. Therefore, when the prefabricated pier stud is installed in the construction process, the installation deviation is required to be accurately measured, and the assembly quality is strictly controlled.
At present, measuring tools such as a level gauge, a theodolite and a total station are mostly adopted for assisting in accurately assembling a control structure, but the measuring mode is low in automation degree, complex in preparation work before measurement, and needs to input more manpower and material resources, so that the measuring mode of the construction site needs to be improved. In recent years, three-dimensional laser scanning technology has been rapidly developed, and has attracted attention because of its advantages of high precision, non-contact, wide measurement range, and the like. However, there is no application of three-dimensional scanning in pier stud installation monitoring at present, and therefore, there is a need for an automatic data processing method for applying a three-dimensional scanning technology to pier stud installation monitoring, and in particular, converting point cloud data obtained from scanning into installation deviation values.
Disclosure of Invention
The invention aims to provide a prefabricated pier column installation position posture monitoring method based on three-dimensional laser scanning, which is used for automatically extracting six-degree-of-freedom position posture information of a prefabricated pier column from a scanned point cloud model and detecting verticality and horizontal offset of an installation state.
The invention adopts the following technical scheme:
the invention discloses a three-dimensional laser scanning-based prefabricated pier column installation position posture monitoring method, which comprises the following steps of:
step 1, taking the cross section size, the length and the construction information of embedded bars on a bridge pier column and a bearing platform to be installed in a design drawing as measurement preparation;
a three-dimensional surface model of the bridge pier column and the bearing platform reinforcing steel bar is established in CAD and is used as a reference of an on-site scanning model,
wherein the coordinate system of the bridge pier column model is recorded as a pier column local coordinate system CS P The coordinate system of the bearing platform reinforcing bar model is marked as a design coordinate system CS R ；
Step 2, placing a three-dimensional scanner on site, and keeping the position of the three-dimensional scanner unchanged in the process of installing and monitoring the pier stud, so that the three-dimensional scanner can monitor connecting steel bars pre-embedded on a bridge bearing platform before the prefabricated pier stud is installed, and can monitor two adjacent end faces of the bridge pier stud in the process of installing the prefabricated bridge pier stud;
step 3, before pier stud installation, carrying out 360-degree surrounding scanning on a construction site by using a three-dimensional scanner; obtaining scanning point cloud data of a three-dimensional scanner;
step 4, registering the scanning point cloud data obtained by the three-dimensional scanner in step 3 and the design model of the bearing platform embedded bars in step 1, calibrating the position and posture of the three-dimensional scanner, and solving a scanning coordinate system CS M To a design coordinate system CS R Is a three-dimensional transformation matrix Rt of (a) M->R ；
Step 5, calculating a scanning range required by completely covering the bridge pier column design position by taking the bridge pier column design model as a reference, namely, the initial value and the final value of the elevation angle of the three-dimensional scanner along the vertical direction and the azimuth angle along the horizontal direction;
step 6, after the bridge pier column is lifted, setting a three-dimensional scanner according to the scanning range calculated in the step 5, and starting scanning to obtain scanning point cloud data of the current state of the pier column;
step 7, utilizing the three-dimensional transformation matrix Rt in the step 4 M->R The scanning point cloud data of the pier stud in the step 6 are obtained through a scanning coordinate system CS M Conversion to the design coordinate System CS defined in step 1 R ；
Step 8, registering the scanning point cloud data transformed in the step 7 with the bridge pier column design model in the step 1, calculating the local position and posture of the pier column, and solving a design coordinate system CS R To pier column local coordinate system CS P Is a three-dimensional transformation matrix Rt of (a) R->P ；
Step 9, designing a coordinate system CS R To pier column local coordinate system CS P Is a three-dimensional transformation matrix Rt of (a) R->P Decomposing the offset values of the bottom of the pier column along the directions of three coordinate axes of a design coordinate system and the vertical inclination angle;
step 10, taking the allowable installation deviation of the prefabricated pier column given in the specification as a reference, and feeding back the measured value to field constructors for installation adjustment when the pier column deviation value and the inclination angle solved in the step 8 are larger than the allowable installation deviation;
and step 11, repeating the steps 6-10 after installation and adjustment until the measured value is smaller than the allowable installation deviation, and judging that the hoisting is finished.
According to the three-dimensional laser scanning-based prefabricated pier column installation position posture monitoring method, the scanning point cloud obtained by the three-dimensional scanner in the step 4 and the step 8 is registered with the design model of the bridge pier column or the bearing platform reinforcing steel bar by adopting a coarse-to-fine registration method;
the scanning point cloud is the source point cloud S, and the corresponding coordinate system is marked as cs S . The design model is a three-dimensional surface model established by CAD, the three-dimensional surface model is converted into a design point cloud model by using Poisson disk sampling, the design point cloud model is recorded as a target point cloud T, and a corresponding coordinate system is cs T The method comprises the steps of carrying out a first treatment on the surface of the Coarse registration based on the neighborhood two-dimensional point cloud smooth density is carried out, and the steps are as follows:
(4) Extracting respective three-dimensional characteristic points of a source point cloud S and a target point cloud T by utilizing a voxelized grid filter;
(5) Respectively solving a local coordinate system of the feature points for each three-dimensional feature point; for the characteristic point p, selecting a radius r L Neighbor point set { p } within local sphere Q of (2) i :||p i -p||≤r L Solving covariance matrix M of the neighborhood point set,
where N is the neighborhood point set p i Points within e Q; a point in the set of neighborhood points,
the method comprises the steps of utilizing feature decomposition to solve a local coordinate system LCS of a feature point p, wherein a normal vector with the Z-axis direction as a minimum feature value in the local coordinate system LCS, defining a plane vertical to the Z-axis as a plane L, and an x-axis as a projection vector weighted sum of all neighborhood points projected to the plane L, wherein a y-axis is obtained by cross product of the Z-axis and the x-axis;
(6) For each characteristic point, respectively solving a two-dimensional point cloud smooth density matrix of the characteristic point by the three-dimensional characteristic points of the source point cloud S and the target point cloud T; the neighborhood point set p of the feature point p i E, Q is transformed to a local coordinate system, projected into an XY plane of the local coordinate system and converted into a neighborhood point set p 'of a two-dimensional feature point p' i E Q'; establishing a two-dimensional grid G around a two-dimensional feature point p', wherein the grid numbers along the X axis and the Y axis are n v The total size of the grid is d v . For a single grid center point c 'in the two-dimensional grid G' jk Selecting a neighborhood point set G jk Point p 'in the field Point set' jk Meet to the grid center point c' jk Is less than 2d v /n v Solving the two-dimensional point cloud density after Gaussian smoothing,
wherein N is jk For the neighborhood point set p' jk ∈G jk σ is the bandwidth of Gaussian smoothing; the two-dimensional point cloud density matrix has the size of n v ×n v All elements D jk Normalizing according to the sum of 1 to adapt to the non-uniformity of the point cloud density; two-dimensional smooth point cloud density matrix for characteristic pointsConverted into a length n v ×n v Is a feature point column vector of (1);
(4) Respectively extracting two-dimensional smooth point cloud density vectors of characteristic points from a source point cloud S and a target point cloud T; solving a three-dimensional transformation matrix rt of a source point cloud S and a target point cloud T by utilizing RANSAC global registration 1 Source point cloud S passing rt 1 The coordinate system after three-dimensional transformation is marked as an estimated target coordinate systemTarget point cloud T passing->The coordinate system after the inverse matrix three-dimensional transformation of (a) is marked as a pre-estimated source coordinate system +.>
(5) Transformation matrix rt for coarsely registering design surface models 1 Is transformed by the inverse matrix of the target coordinate system cs T Transforming to a predicted source coordinate systemEstimated source coordinate system +.>Converting the corner points of the face from a three-dimensional Cartesian coordinate system (x, y, z) into a spherical coordinate system +.>Counting elevation angle theta and azimuth angle +.>Is set to simulate the elevation angle, azimuth angle start and end angles of the scanner. The scanning angle resolution of the analog scanner is set to be the same as that of the actual scanning, and an analog point cloud model T 'of the object to be detected is generated based on a ray tracing method' s The corresponding coordinate system is->Using a three-dimensional transformation matrix rt 1 The point cloud model T 'will be simulated' s From the estimated source coordinate system->Transforming to the target coordinate system cs T The simulated point cloud model is the corrected target point cloud T';
(6) To estimate the source coordinate systemUnder simulated point cloud T s ' as a reference point set, searching by utilizing a nearest point to acquire the nearest point and a corresponding distance of each point in the source point cloud S in the reference point set; setting a near point distance threshold value eta=1 cm, marking points with a distance lower than a specified threshold value eta as objects to be detected, and marking the rest points as background objects; removing background objects, and storing source point clouds' only containing objects to be detected;
(7) Registering the corrected source point cloud S 'and target point cloud T' by utilizing a trimmed ICP algorithm, wherein an initial transformation matrix is set as rt 1 The point cloud coincidence ratio is set to 0.8 in consideration of the real point cloud measurement error. Solving an accurate three-dimensional transformation matrix rt from the source point cloud S 'to the target point cloud T';
the invention discloses a three-dimensional laser scanning-based prefabricated pier column installation position posture monitoring method, which is characterized in that the scanning range calculation method in the step 5 is as follows:
designing a pier column surface model according to a three-dimensional transformation matrix Rt which is solved in advance M->R Is the inverse matrix Rt of (1) R->M Transforming the coordinates by designing a reference coordinate system CS R Transformed to scanner own coordinate system CS M The method comprises the steps of carrying out a first treatment on the surface of the Converting corner points in the pier cylindrical model from a three-dimensional Cartesian coordinate system (x, y, z) to a spherical coordinate systemCounting elevation angle theta and azimuth angle +.>Minimum and maximum values [ theta ] min ,θ max ],/>Given a redundancy of 5 °, the elevation angle start and end angle values in the scan range are [ θ ] min -5°,θ max +5°]The azimuth angle start and end angle values are +.>
Advantageous effects
According to the three-dimensional laser scanning-based prefabricated pier column installation position posture monitoring method, the three-dimensional scanner can be erected at any position on site, lofting and positioning of a reference line are not needed, and a marker is not needed to be stuck on the pier column to serve as a measurement reference, so that the three-dimensional laser scanning-based prefabricated pier column installation position posture monitoring method is convenient to use and efficient.
According to the three-dimensional laser scanning-based prefabricated pier column installation position posture monitoring method, the horizontal offset and the vertical inclination of the pier column relative to the design position in the current installation state can be automatically extracted from the point cloud model of the field scanning by adopting the data processing method, and data guidance is provided for pier column installation adjustment.
According to the rough registration method based on the neighborhood two-dimensional point cloud smooth density, the fact that the projections of the corner points and the side characteristic points in the embedded bar point cloud to the local coordinate system of the embedded bar point cloud are obviously different is considered, the three-dimensional description of the characteristic points is reduced to two-dimensional description, the calculation efficiency is improved, and the method is also robust to sparse point clouds; the fine registration method based on the point cloud generator and the trimmed ICP ensures that the designed point cloud and the real point cloud used in registration have high overlap ratio through the scanning process simulation and the real point cloud target object mark, and can realize the fine registration.
Drawings
FIG. 1 is a flow chart of the method of the present invention;
FIG. 2 is a schematic illustration of a job site of the present invention;
FIG. 3 is a photograph of pre-buried bars on the surface of a bearing platform before installation and a field diagram during the installation process;
FIG. 4 is a design reference frame CS of the present invention R Scanner self coordinate system CS M Pier column local coordinate system CS P Is illustrated by three coordinate systems;
FIG. 5 is a two-dimensional smooth point cloud density matrix illustration of two characteristic points extracted from corner points and side edges in a scanning point cloud of embedded bars on the surface of a bearing platform;
FIG. 6 is a schematic illustration of the accurate registration process of the point cloud model of the embedded bar of the present invention;
FIG. 7 is a trajectory of the motion of the local coordinate system of the pier measured during the stepwise measurement adjustment of the pier stud installation of the present invention in the design coordinate system.
Detailed Description
In order to make the purpose and technical solutions of the embodiments of the present invention more clear, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. It will be apparent that the described embodiments are some, but not all, embodiments of the invention. All other embodiments, which can be made by a person skilled in the art without creative efforts, based on the described embodiments of the present invention fall within the protection scope of the present invention.
As shown in the figure: a prefabricated pier column installation position posture monitoring method based on three-dimensional laser scanning comprises the following steps:
step 1, taking the cross section size, the length and the construction information of embedded bars on a bridge pier column and a bearing platform to be installed in a design drawing as measurement preparation;
a three-dimensional surface model of the bridge pier column and the bearing platform reinforcing steel bar is established in CAD and is used as a reference of an on-site scanning model,
wherein the coordinate system of the bridge pier column model is recorded as a pier column local coordinate system CS P The coordinate system of the bearing platform reinforcing bar model is marked as a design coordinate system CS R ；
Step 2, placing a three-dimensional scanner on site, and keeping the position of the three-dimensional scanner unchanged in the process of installing and monitoring the pier stud, so that the three-dimensional scanner can monitor connecting steel bars pre-embedded on a bridge bearing platform before the prefabricated pier stud is installed, and can monitor two adjacent end faces of the bridge pier stud in the process of installing the prefabricated bridge pier stud;
step 3, before pier stud installation, carrying out 360-degree surrounding scanning on a construction site by using a three-dimensional scanner; obtaining scanning point cloud data of a three-dimensional scanner;
step 4, registering the scanning point cloud data obtained by the three-dimensional scanner in step 3 and the design model of the bearing platform embedded bars in step 1, calibrating the position and posture of the three-dimensional scanner, and solving a scanning coordinate system CS M To a design coordinate system CS R Is a three-dimensional transformation matrix Rt of (a) M->R ；
Step 5, calculating a scanning range required by completely covering the bridge pier column design position by taking the bridge pier column design model as a reference, namely, the initial value and the final value of the elevation angle of the three-dimensional scanner along the vertical direction and the azimuth angle along the horizontal direction;
the scanning range calculating method comprises designing pier column surface model according to three-dimensional transformation matrix Rt obtained in advance M->R Is the inverse matrix Rt of (1) R->M Coordinate transformation is carried out, and the design is carried outReference coordinate system CS R Transformed to scanner own coordinate system CS M The method comprises the steps of carrying out a first treatment on the surface of the Converting corner points in the pier cylindrical model from a three-dimensional Cartesian coordinate system (x, y, z) to a spherical coordinate systemCounting elevation angle theta and azimuth angle +.>Minimum and maximum values [ theta ] min ,θ max ],/>Given a redundancy of 5 °, the elevation angle start and end angle values in the scan range are [ θ ] min -5°,θ max +5°]The azimuth angle start and end angle values are +.>
Step 6, after the bridge pier column is lifted, setting a three-dimensional scanner according to the scanning range calculated in the step 5, and starting scanning to obtain a point cloud model of the current state of the pier column;
step 7, utilizing the three-dimensional transformation matrix Rt in the step 4 M->R The scanning point cloud data in the step 6 (pier column) is processed by a scanning coordinate system CS M Conversion to the design coordinate System CS defined in step 1 R ；
Step 8, registering the scanning point cloud data transformed in the step 7 with the bridge pier column design model in the step 1, calculating the local position and posture of the pier column, and solving a design coordinate system CS R To pier column local coordinate system CS P Is a three-dimensional transformation matrix Rt of (a) R->P ；
Step 9, designing a coordinate system CS R To pier column local coordinate system CS P Is a three-dimensional transformation matrix Rt of (a) R->P Is decomposed into offset values of the bottom of the pier column along the directions of three coordinate axes of a design coordinate system and a CS along the design coordinate system R A vertical tilt angle of (2);
step 10, taking the allowable installation deviation of the prefabricated pier column given in the specification as a reference, and feeding back the measured value to field constructors for installation adjustment when the pier column deviation value and the inclination angle solved in the step 8 are larger than the allowable installation deviation;
and step 11, repeating the steps 6-10 after installation and adjustment until the measured value is smaller than the allowable installation deviation, and judging that the hoisting is finished.
According to the invention, the scanning point cloud obtained by the three-dimensional scanner in the step 4 and the step 8 is registered with the design model of the bridge pier or bearing platform reinforcing steel bar by adopting a coarse-to-fine registration method;
the scanning point cloud is the source point cloud S, and the corresponding coordinate system is marked as cs S . The design model is a three-dimensional surface model established by CAD, the three-dimensional surface model is converted into a design point cloud model by using Poisson disk sampling, the design point cloud model is recorded as a target point cloud T, and a corresponding coordinate system is cs T The method comprises the steps of carrying out a first treatment on the surface of the Coarse registration based on the neighborhood two-dimensional point cloud smooth density is carried out, and the steps are as follows:
(1) Extracting respective three-dimensional characteristic points of a source point cloud S and a target point cloud T by utilizing a voxelized grid filter;
(2) Respectively solving a local coordinate system of the feature points for each three-dimensional feature point; for any feature point p, selecting a radius r L Neighbor point set { p } within local sphere Q of (2) i :||p i -p||≤r L Solving covariance matrix M of the neighborhood point set,
where N is the neighborhood point set p i Points within e Q; a certain point in the neighborhood point set is utilized to solve a local coordinate system LCS of a feature point p by utilizing feature decomposition, a normal vector with the Z-axis direction as a minimum feature value in the local coordinate system LCS, a plane vertical to the Z-axis is defined as a plane L, an x-axis is a projection vector weighted sum of projections of all neighborhood points onto the plane L, and a y-axis is obtained by a cross product of the Z-axis and the x-axis;
(3) For each characteristic point, the three-dimensional characteristic points of the source point cloud S and the target point cloud T are used for respectively solving the two-dimensional point cloud smooth density of the characteristic pointsA matrix; the neighborhood point set p of the feature point p i E, Q is transformed to a local coordinate system, projected into an XY plane of the local coordinate system and converted into a neighborhood point set p 'of a two-dimensional feature point p' i E Q'; establishing a two-dimensional grid G around a two-dimensional feature point p', wherein the grid numbers along the X axis and the Y axis are n v The total size of the grid is d v . For a single grid center point c 'in the two-dimensional grid G' jk Selecting a neighborhood point set G jk Point p 'in the field Point set' jk Meet to the grid center point c' jk Is less than 2d v /n v Solving the two-dimensional point cloud density after Gaussian smoothing,
wherein N is jk For the neighborhood point set p' jk ∈G jk σ is the bandwidth of Gaussian smoothing; the two-dimensional point cloud density matrix has the size of n v ×n v All elements D jk Normalizing according to the sum of 1 to adapt to the non-uniformity of the point cloud density; two-dimensional smooth point cloud density matrix for characteristic pointsConverted into a length n v ×n v Is a feature point column vector of (1);
(4) Respectively extracting two-dimensional smooth point cloud density vectors of characteristic points from a source point cloud S and a target point cloud T; solving a three-dimensional transformation matrix rt of a source point cloud S and a target point cloud T by utilizing RANSAC global registration 1 Source point cloud S passing rt 1 The coordinate system after three-dimensional transformation is marked as an estimated target coordinate systemTarget point cloud T passing->The coordinate system after the inverse matrix three-dimensional transformation of (a) is marked as a pre-estimated source coordinate system +.>
Fine registration based on the point cloud generator and the trimmed ICP is performed. The target point cloud T is the point cloud of all the surfaces of the object to be measured after sampling in the design state, and the situation that the part of the object to be measured is blocked and the measurement is incomplete in the measuring process of the scanner is not considered. The source point cloud S is a field scanning model and comprises a plurality of background objects besides an object to be detected. Therefore, the source point cloud S and the target point cloud T have a low degree of coincidence, which may cause inaccurate registration. Because, a simulated point cloud generator is adopted, the target point cloud T' is regenerated; extracting a point cloud S' corresponding to an object to be detected in the source point cloud S by adopting nearest point searching; the corrected source point cloud S 'and target point cloud T' are accurately registered by utilizing a trimmed ICP algorithm, and the specific steps are as follows:
(5) Transformation matrix rt for coarsely registering design surface models 1 Is transformed by the inverse matrix of the target coordinate system cs T Transforming to a predicted source coordinate systemEstimated source coordinate system +.>Converting corner points of the face into a spherical coordinate system from a three-dimensional Cartesian coordinate system (x, y, z)>Counting elevation angle theta and azimuth angle +.>Is set to simulate the elevation angle, azimuth angle start and end angles of the scanner. The scanning angle resolution of the analog scanner is set to be the same as that of the actual scanning, and a simulated point cloud model T 'of the object to be detected is generated based on a ray tracing method without considering measurement errors' s The corresponding coordinate system is->Using three-dimensional transformationMatrix change rt 1 The point cloud model T 'will be simulated' s From the estimated source coordinate system->Transforming to the target coordinate system cs T The simulated point cloud model is the corrected target point cloud T';
(6) To estimate the source coordinate systemUnder simulated point cloud T s ' as a reference point set, searching by utilizing a nearest point to acquire the nearest point and a corresponding distance of each point in the source point cloud S in the reference point set; setting a near point distance threshold value eta=1 cm, marking points with a distance lower than a specified threshold value eta as objects to be detected, and marking other points as background objects; removing background objects, and storing source point clouds' only containing objects to be detected;
(7) Registering the corrected source point cloud S 'and target point cloud T' by utilizing a trimmed ICP algorithm, wherein an initial transformation matrix is set as rt 1 The point cloud coincidence ratio is set to 0.8 in consideration of the real point cloud measurement error. And solving an accurate three-dimensional transformation matrix rt from the source point cloud S 'to the target point cloud T'.
The present invention is not limited to the above-mentioned embodiments, and any changes or substitutions that can be easily understood by those skilled in the art within the technical scope of the present invention are intended to be included in the scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.

Claims (3)

1. A prefabricated pier column installation position posture monitoring method based on three-dimensional laser scanning is characterized in that: the method comprises the following steps:
step 1, as measurement preparation, according to the section size, length and construction information of embedded bars on a bridge pier column and a bearing platform to be installed in a design drawing, a three-dimensional surface model of the bridge pier column and the bearing platform bars is built in CAD and used as a reference of an on-site scanning model;
wherein the coordinate system of the bridge pier column model is recorded as a pier column local coordinate system CS P The coordinate system of the bearing platform reinforcing bar model is marked as a design reference coordinate system CS R ；
Step 2, placing a three-dimensional scanner on site, and keeping the position of the three-dimensional scanner unchanged in the process of installing and monitoring the pier stud, so that the three-dimensional scanner can monitor connecting steel bars pre-embedded on a bridge bearing platform before the prefabricated pier stud is installed, and can monitor two adjacent end faces of the bridge pier stud in the process of installing the prefabricated bridge pier stud;
step 3, before pier stud installation, carrying out 360-degree surrounding scanning on a construction site by using a three-dimensional scanner to obtain scanning point cloud data of the three-dimensional scanner;
step 4, registering the scanning point cloud data obtained by the three-dimensional scanner in step 3 and the design model of the bearing platform embedded bars in step 1, calibrating the position and posture of the three-dimensional scanner, and solving a scanning coordinate system CS M To the design reference frame CS R Is a three-dimensional transformation matrix Rt of (a) M->R ；
Step 5, calculating a scanning range required by completely covering the bridge pier column design position by taking the bridge pier column design model as a reference, namely, the initial value and the final value of the elevation angle of the three-dimensional scanner along the vertical direction and the azimuth angle along the horizontal direction;
step 6, after the bridge pier column is lifted, setting a three-dimensional scanner according to the scanning range calculated in the step 5, and starting scanning to obtain scanning point cloud data of the current state of the pier column;
step 7, utilizing the three-dimensional transformation matrix Rt in the step 4 M->R The pier column scanning point cloud data in the step 6 is processed by a scanning coordinate system CS M Transformation to the design reference frame CS defined in step 1 R ；
Step 8, registering the scanning point cloud data transformed in the step 7 with the bridge pier column design model in the step 1, calculating the local position and posture of the pier column, and solving a design reference coordinate system CS R To pier column local coordinate system CS P Is a three-dimensional transformation matrix Rt of (a) R->P ；
Step 9, designing a reference coordinate system CS R To pier column local coordinate system CS P Is a three-dimensional transformation matrix Rt of (a) R->P Decomposing the offset values of the bottom of the pier column along the directions of three coordinate axes of a design coordinate system and the vertical inclination angle;
step 10, taking the allowable installation deviation of the prefabricated pier column given in the specification as a reference, and feeding back the measured value to field constructors for installation adjustment when the pier column deviation value and the inclination angle solved in the step 8 are larger than the allowable installation deviation;
and step 11, repeating the steps 6-10 after installation and adjustment until the measured value is smaller than the allowable installation deviation, and judging that the hoisting is finished.
2. The method for monitoring the mounting position and the posture of the prefabricated pier stud based on the three-dimensional laser scanning according to claim 1, wherein the scanning point cloud obtained by the three-dimensional scanner in the step 4 and the step 8 is registered with the design model of the bridge pier stud or the bearing platform reinforcing steel bar by adopting a coarse-to-fine registration method;
the scanning point cloud is the source point cloud S, and the corresponding coordinate system is marked as cs S The method comprises the steps of carrying out a first treatment on the surface of the The design model is a three-dimensional surface model established by CAD, the three-dimensional surface model is converted into a design point cloud model by using Poisson disk sampling, the design point cloud model is recorded as a target point cloud T, and a corresponding coordinate system is cs T The method comprises the steps of carrying out a first treatment on the surface of the Coarse registration based on the neighborhood two-dimensional point cloud smooth density is carried out, and the steps are as follows:
(1) Extracting respective three-dimensional characteristic points of a source point cloud S and a target point cloud T by utilizing a voxelized grid filter;
(2) Respectively solving a local coordinate system of the feature points for each three-dimensional feature point; for the characteristic point p, selecting a radius r L Neighbor point set { p } within local sphere Q of (2) i :||p i -p||≤r L Solving covariance matrix M of the neighborhood point set,
where N is the neighborhood point set p i Points within e Q; a point in the neighborhood point set，
The method comprises the steps of utilizing feature decomposition to solve a local coordinate system LCS of a feature point p, wherein a normal vector with the Z axis direction as a minimum feature value in the local coordinate system LCS, defining a plane vertical to the Z axis as a plane L, and an X axis as a projection vector weighted sum of all neighborhood points projected to the plane L, wherein a Y axis is obtained by cross product of the Z axis and the X axis;
(3) For each characteristic point, respectively solving a two-dimensional point cloud smooth density matrix of the characteristic point by the three-dimensional characteristic points of the source point cloud S and the target point cloud T; the neighborhood point set p of the feature point p i E, Q is transformed to a local coordinate system, projected into an XY plane of the local coordinate system and converted into a neighborhood point set p 'of a two-dimensional feature point p' i E Q'; establishing a two-dimensional grid G around a two-dimensional feature point p', wherein the grid numbers along the X axis and the Y axis are n v The total size of the grid is d v For a single grid center point c 'in the two-dimensional grid G' jk Selecting a neighborhood point set G jk Point p 'in the field Point set' jk Meet to the grid center point c' jk Is less than 2d v /n v Solving the two-dimensional point cloud density after Gaussian smoothing,
wherein N is jk For the neighborhood point set p' jk ∈G jk σ is the bandwidth of Gaussian smoothing; the two-dimensional point cloud density matrix has the size of n v ×n v All elements D jk Normalizing according to the sum of 1 to adapt to the non-uniformity of the point cloud density; two-dimensional smooth point cloud density matrix for characteristic pointsConverted into a length n v ×n v Is a feature point column vector of (1);
(4) Respectively extracting two-dimensional smooth point cloud density vectors of characteristic points from a source point cloud S and a target point cloud T; solving a three-dimensional transformation matrix rt of a source point cloud S and a target point cloud T by utilizing RANSAC global registration 1 Source point cloud S passing rt 1 The coordinate system after three-dimensional transformation is marked as an estimated target coordinate systemTarget point cloud T passing->The coordinate system after the inverse matrix three-dimensional transformation of (a) is marked as a pre-estimated source coordinate system +.>
(5) Transformation matrix rt for coarsely registering design surface models 1 Is transformed by the inverse matrix of the target coordinate system cs T Transforming to a predicted source coordinate systemEstimated source coordinate system +.>Converting the corner points of the face from a three-dimensional Cartesian coordinate system (x, y, z) into a spherical coordinate system +.>Counting elevation angle theta and azimuth angle +.>The minimum value and the maximum value of the (B) are set as the elevation angle, the azimuth angle starting angle and the azimuth angle ending angle of the analog scanner, the scanning angle resolution of the analog scanner is set to be the same as that of the actual scanning, and the analog point cloud model T 'of the object to be detected is generated based on a ray tracing method' s The corresponding coordinate system is->Using a three-dimensional transformation matrix rt 1 The point cloud model T 'will be simulated' s From the estimated source coordinate system->Transforming to the target coordinate system cs T The simulated point cloud model is the corrected target point cloud T';
(6) To estimate the source coordinate systemUnder simulated point cloud T s ' as a reference point set, searching by utilizing a nearest point to acquire the nearest point and a corresponding distance of each point in the source point cloud S in the reference point set; setting a near point distance threshold value eta=1 cm, marking points with a distance lower than a specified threshold value eta as objects to be detected, and marking the rest points as background objects; removing background objects, and storing source point clouds' only containing objects to be detected;
(7) Registering the corrected source point cloud S 'and target point cloud T' by utilizing a trimmed ICP algorithm, wherein an initial transformation matrix is set as rt 1 Considering the measurement error of the real point cloud, setting the point cloud coincidence rate to 0.8, and solving an accurate three-dimensional transformation matrix rt from the source point cloud S 'to the target point cloud T'.
3. The method for monitoring the mounting position and the posture of the prefabricated pier stud based on the three-dimensional laser scanning according to claim 1, wherein the scanning range calculation method in the step 5 is as follows:
designing pier column surface model, and transforming matrix Rt according to the model M->R Is the inverse matrix Rt of (1) R->M Transforming the coordinates by designing a reference coordinate system CS R Transformed to scanner own coordinate system CS M The method comprises the steps of carrying out a first treatment on the surface of the Converting corner points in the pier cylindrical model from a three-dimensional Cartesian coordinate system (x, y, z) to a spherical coordinate systemCounting elevation angle theta and azimuth angle +.>Minimum and maximum values [ theta ] min ,θ max ],/>
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