Three-dimensional scanning size detection construction method for large prefabricated part

Abstract

The invention discloses a three-dimensional scanning size detection construction method for a large prefabricated part, and relates to the technical field of building construction, a multi-view triangulation measurement technology is used to shoot marking points from different view angles to obtain sparse marker three-dimensional point cloud, a laser three-dimensional scanning technology is used to carry out three-dimensional scanning on the local part of the prefabricated part to be detected, and the three-dimensional scanning size of the prefabricated part to be detected is obtained. The method comprises the following steps: acquiring a high-precision dense three-dimensional point cloud, then performing registration fusion on the dense three-dimensional point cloud and a sparse marker three-dimensional point cloud to obtain high-precision low-volume-error dense prefabricated part three-dimensional point cloud data, thereby realizing high-precision size detection on a large prefabricated part, and simulating an actual assembly process by using a digital virtual pre-assembly technology. The assembly problem is found in advance, and the problems that when a traditional large prefabricated part is assembled, the size detection precision is low, the efficiency is not high, the data comprehensiveness is insufficient, the cost is high when an entity is pre-assembled, and the occupied space is large are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of building construction, and in particular to a construction method for three-dimensional scanning size detection of large prefabricated components. Background Art

[0002] In the process of modern building industrialization, large prefabricated components are widely used in prefabricated buildings, especially in prefabricated subway stations and other projects. The dimensional accuracy of prefabricated components directly affects the stability of the building structure and the overall assembly quality. However, traditional dimensional detection methods such as measurement with a ruler and total station measurement have problems such as low detection accuracy, low efficiency, and insufficient data comprehensiveness when faced with large and complex prefabricated components. At the same time, physical pre-assembly is not only costly and occupies a large area, but it is also difficult to simulate actual vertical assembly conditions and cannot accurately reflect the tolerances of components during actual installation. Therefore, there is an urgent need for a high-precision, efficient, and large-scale prefabricated component dimensional detection construction method that can simulate actual assembly conditions. Summary of the Invention

[0003] Based on this, in response to the above problems, the present invention proposes a three-dimensional scanning size detection construction method for large prefabricated components, which solves the problems of low size detection accuracy, low efficiency, insufficient data comprehensiveness, high cost and large site occupation during the assembly of traditional large prefabricated components.

[0004] The technical solution of the present invention is:

[0005] A large prefabricated component three-dimensional scanning size detection construction method includes the following steps:

[0006] S1: Based on 3D forward design and secondary in-depth design, a BIM model is established using BIM forward design and parametric drive technology. Marking points and coding points are affixed to the surface of prefabricated components, and coordinate poles are set up.

[0007] S2: Using multi-view triangulation technology, the marker points are photographed from different perspectives to obtain a sparse marker 3D point cloud and determine the overall outline of the prefabricated component;

[0008] S3: Use laser 3D scanning technology to perform 3D scanning on the prefabricated component to obtain a high-precision dense 3D point cloud;

[0009] S4: Register and fuse the dense 3D point cloud with the sparse marker 3D point cloud to obtain high-precision and low-volume-error dense 3D point cloud data of prefabricated components, compare it with the BIM model or key design parameters, and perform corresponding processing based on the comparison results;

[0010] S5: Establish a digital component model based on the dense 3D point cloud data of prefabricated components, virtually assemble the digital component model according to the BIM model coding and sequence, inspect and analyze key parts, compare the virtual pre-assembly results with the BIM model or key design parameters, and perform corresponding processing based on the comparison results.

[0011] Preferably, in step S4, when the data comparison result does not meet the requirements, the process returns to step S1 and performs mold adjustment or component trimming; when the data comparison result meets the requirements, the process proceeds to step S5.

[0012] Preferably, in step S5, when the data comparison result does not meet the requirements, the process returns to the virtual assembly step in step S5; when the data comparison result meets the requirements, the interference or gap cloud map of the corresponding position is output to form a visual report.

[0013] Preferably, in step S4, when registering and fusing the dense three-dimensional point cloud with the sparse marker three-dimensional point cloud, each laser scanned point cloud image contains no less than 6 marker points.

[0014] Preferably, in step S4, when registering and fusing the dense 3D point cloud with the sparse marker 3D point cloud, the sparse marker 3D point cloud needs to be scaled according to the scale of the dense 3D point cloud so that the scales of the two are unified.

[0015] Preferably, in step S4, the scale transformation step is as follows:

[0016] Assume that the original sparse marker 3D point cloud to be transformed is P s , the transformed point cloud is P t , at this time there is only rigid transformation between the two, namely translation and rotation, and the relationship between the two is:

[0017] P t =R·P s +T

[0018] Where R is the rotation matrix, as follows:

[0019]

[0020] T is the translation matrix, as follows:

[0021]

[0022] The rotation matrix R and translation matrix T of the sparse marker 3D point cloud can be directly calculated from the coordinate transformation relationship of the sparse marker 3D point cloud.

[0023] Preferably, the global three-dimensional photogrammetry equipment used in the multi-view triangulation technology has a maximum area of ​​not less than 9.4m×6.9m and a volume accuracy of not less than 0.015mm / m.

[0024] Preferably, the three-dimensional laser scanning robot used in the laser three-dimensional scanning technology is equipped with a binocular imaging system with an accuracy of not less than 0.02 mm.

[0025] Compared with the prior art, the present invention has the following beneficial effects:

[0026] This invention combines multi-view triangulation and laser 3D scanning technology to achieve high-precision dimensional detection of large prefabricated components. It also uses digital virtual pre-assembly technology to simulate the actual assembly process, identifying assembly problems in advance, improving construction quality and efficiency, and reducing costs and safety risks. This addresses the problems of low dimensional detection accuracy, low efficiency, insufficient data comprehensiveness, and high costs and large space requirements associated with physical pre-assembly during traditional large prefabricated component assembly. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 It is a schematic diagram of the process structure of a large prefabricated component three-dimensional scanning size detection construction method described in an embodiment of the present invention;

[0028] Figure 2 Schematic diagram of the technical principle of the multi-view triangulation technology described in an embodiment of the present invention;

[0029] Figure 3 Schematic diagram of the technical principle of the laser three-dimensional scanning technology described in an embodiment of the present invention;

[0030] Figure 4 It is the interference or gap cloud map of the digital virtual pre-assembly described in the embodiment of the present invention. DETAILED DESCRIPTION

[0031] The embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0032] Example:

[0033] like Figure 1 As shown, this embodiment discloses a large prefabricated component three-dimensional scanning size detection construction method, including the following steps:

[0034] S1: Based on 3D forward design and secondary in-depth design, a BIM model is established using BIM forward design and parametric drive technology. Marking points and coding points are affixed to the surface of prefabricated components, and coordinate poles are set up.

[0035] S2: Using multi-view triangulation technology, the marker points are photographed from different perspectives to obtain a sparse marker 3D point cloud and determine the overall outline of the prefabricated component;

[0036] S3: Use laser 3D scanning technology to perform 3D scanning on the prefabricated component to obtain a high-precision dense 3D point cloud;

[0037] S4: Register and fuse the dense 3D point cloud with the sparse marker 3D point cloud to obtain high-precision and low-volume-error dense 3D point cloud data of prefabricated components, compare it with the BIM model or key design parameters, and perform corresponding processing based on the comparison results;

[0038] S5: Establish a digital component model based on the dense 3D point cloud data of prefabricated components, virtually assemble the digital component model according to the BIM model coding and sequence, inspect and analyze key parts, compare the virtual pre-assembly results with the BIM model or key design parameters, and perform corresponding processing based on the comparison results.

[0039] In step S1, a BIM model is first established based on 3D forward design and secondary in-depth design, using BIM forward design and parametric-driven technology. Key design parameters are clarified, providing benchmark data for subsequent inspection and virtual pre-assembly. While there are no specific formulas at this stage, the BIM model serves as the foundation for subsequent data comparison and analysis. Marker points and coded points are then affixed to the surface of the prefabricated components for subsequent multi-view triangulation, laser 3D scanning, and point cloud data processing. Coordinate poles are also installed to provide a unified coordinate reference system for scanning measurements.

[0040] In step S2, the multi-view triangulation technique is used to photograph the marker points attached to the prefabricated component from different perspectives. Based on the principle of similar triangles, the surface depth of the object is calculated to obtain a sparse marker 3D point cloud and determine the overall outline of the prefabricated component. The specific formula for calculating the surface depth of the object is:

[0041]

[0042] Wherein, x1 and x2 are the coordinates of the imaging point with the optical center of each camera as the origin, with the right direction as the positive direction, f is the focal length of the camera, T is the camera spacing, and z is the depth of the object to be measured. The global three-dimensional photogrammetry equipment used in the multi-view triangulation technology has a maximum area of ​​not less than 9.4m×6.9m and a volume accuracy of not less than 0.015mm / m.

[0043] like Figure 2 As shown in the figure, the basic principle is: if the test point 1 and the test point 2 are located at different depths, then the coordinates of the imaging points at the two shooting points will be different. Taking the test point 1 as an example, according to the principle of similar triangles, we can get:

[0044]

[0045] Among them, x1 and x2 are the coordinates of the imaging point with the optical center of each camera as the origin, with the right as the positive direction, f is the focal length of the camera, T is the camera spacing, and z is the depth of the object to be measured.

[0046] The coordinate difference of the point to be measured in the images of the two shooting positions is:

[0047]

[0048] As can be seen, when the camera focal length f and the camera spacing T are fixed, the coordinate difference between the image points of the object under test between the two images is only related to its depth. Therefore, in practice, the 3D shape of the object can be calculated by measuring the coordinate difference of the marker points in images taken from different perspectives.

[0049] The global 3D photogrammetry equipment used in this invention has a maximum field size of 9.4m x 6.9m and a volumetric accuracy of 0.015mm / m. By photographing the reflective markers multiple times at different viewing angles, a sparse 3D point cloud of the entire prefabricated component can be generated through fitting and splicing techniques.

[0050] In step S3, laser 3D scanning technology is used to perform high-precision scanning of a portion of the prefabricated component to be measured. This method uses oblique laser triangulation, which is based on the triangle formed by the transmitter, the target, and the receiver. The core idea is to calculate the depth change by measuring the offset of the feature point on the CCD. The calculation formula is:

[0051]

[0052] Among them, x is the offset of the imaging point on the CCD, φ, γ, and θ are related angle parameters, and the three-dimensional laser scanning robot used in the laser three-dimensional scanning technology is equipped with a binocular imaging system with an accuracy of not less than 0.02mm.

[0053] like Figure 3 As shown, its basic principle is:

[0054] As can be seen from the figure, triangle OPN and triangle OPN' are similar triangles, so:

[0055]

[0056] in:

[0057] |N'P'|=xsin(φ)

[0058] |NP|=|AB|sin(γ+θ)

[0059] |M'P'|=xcos(φ)

[0060] |MP|=|MN|cos(γ+θ)

[0061] |MN|=y / cos(γ)

[0062] Therefore, we can find:

[0063]

[0064] Among them, x is the offset of the imaging point on the CCD, φ, γ, and θ are related angle parameters, and the three-dimensional laser scanning robot used in the laser three-dimensional scanning technology is equipped with a binocular imaging system with an accuracy of not less than 0.02mm.

[0065] Among them, the positions of the marking points are irregularly distributed, and at least 4 marking points must be covered in a single laser 3D scan.

[0066] In step S4, when the data comparison result does not meet the requirements, return to step S1 and perform mold adjustment or component trimming; when the data comparison result meets the requirements, enter step S5.

[0067] For example, if the design value of a prefabricated component's BIM model or key design parameter is 10,000 mm, the allowable deviation is ±3 mm, and the dense prefabricated component 3D point cloud data is 10,001.2 mm, then the requirements are met. If the requirements are not met, such as if the dense prefabricated component 3D point cloud data is 10,005 mm, the mold should be adjusted or the component should be trimmed and then remeasured.

[0068] In step S4, when the dense three-dimensional point cloud is registered and fused with the sparse marker three-dimensional point cloud, each laser scanned point cloud image contains no less than 6 marker points.

[0069] In step S4, when registering and fusing the dense 3D point cloud with the sparse marker 3D point cloud, the sparse marker 3D point cloud needs to be scaled according to the scale of the dense 3D point cloud so that the scales of the two are unified.

[0070] The scaling steps are as follows:

[0071] Assume that the original sparse marker 3D point cloud to be transformed is P s , the transformed point cloud is P t , at this time there is only rigid transformation between the two, namely translation and rotation, and the relationship between the two is:

[0072] P t =R·P s +T

[0073] Where R is the rotation matrix, as follows:

[0074]

[0075] T is the translation matrix, as follows:

[0076]

[0077] The rotation matrix R and translation matrix T of the sparse marker 3D point cloud can be directly calculated from the coordinate transformation relationship of the sparse marker 3D point cloud.

[0078] In step S5, when the data comparison result does not meet the requirements, return to the virtual assembly step in step S5; when the data comparison result meets the requirements, output the corresponding position interference or gap cloud map to form a visual report, such as Figure 4 Shown is an interference or gap cloud diagram of digital virtual pre-assembly in a specific project.

[0079] For example, testing mortises, tenons, and key waterproofing areas to analyze assembly clearances and interferences. If a mortise and tenon joint gap is found to be 6mm, exceeding the design tolerance (0-5.0mm), the assembly tolerance is adjusted and virtual pre-assembly is repeated until the design requirements are met.

[0080] This invention combines multi-view triangulation and laser 3D scanning technology to achieve high-precision dimensional detection of large prefabricated components. It also uses digital virtual pre-assembly technology to simulate the actual assembly process, identifying assembly problems in advance, improving construction quality and efficiency, and reducing costs and safety risks. It can achieve a volumetric error of 0.015mm / m, meeting the accuracy requirement of less than 0.5mm when applied to the measurement of prefabricated components in stations. This solves the problems of low dimensional detection accuracy, low efficiency, insufficient data comprehensiveness, and high cost and large space occupied during physical pre-assembly during traditional large prefabricated component assembly.

[0081] The above-described embodiments merely represent specific implementations of the present invention. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that a person skilled in the art would be able to make numerous variations and improvements without departing from the spirit of the present invention, and all such variations and improvements fall within the scope of protection of the present invention.

Claims

1. A construction method for three-dimensional scanning size detection of large prefabricated components, characterized in that: The following steps are involved: S1: Based on 3D forward design and secondary in-depth design, a BIM model is established using BIM forward design and parametric drive technology. Marking points and coding points are affixed to the surface of prefabricated components, and coordinate poles are set up. S2: Using multi-view triangulation technology, the marker points are photographed from different perspectives to obtain a sparse marker 3D point cloud and determine the overall outline of the prefabricated component; S3: Use laser 3D scanning technology to perform 3D scanning on the prefabricated component to obtain a high-precision dense 3D point cloud; S4: Register and fuse the dense 3D point cloud with the sparse marker 3D point cloud to obtain high-precision and low-volume-error dense 3D point cloud data of prefabricated components, compare it with the BIM model or key design parameters, and perform corresponding processing based on the comparison results; S5: Establish a digital component model based on the dense 3D point cloud data of prefabricated components, virtually assemble the digital component model according to the BIM model coding and sequence, inspect and analyze key parts, compare the virtual pre-assembly results with the BIM model or key design parameters, and perform corresponding processing based on the comparison results.

2. A large prefabricated component three-dimensional scanning size detection construction method according to claim 1, characterized in that: In step S4, when the data comparison result does not meet the requirements, return to step S1 and perform mold adjustment or component trimming; when the data comparison result meets the requirements, enter step S5.

3. A large prefabricated component three-dimensional scanning size detection construction method according to claim 2, characterized in that: In step S5, when the data comparison result does not meet the requirements, return to the virtual assembly step in step S5; when the data comparison result meets the requirements, output the corresponding position interference or gap cloud map to form a visual report.

4. A large prefabricated component three-dimensional scanning size detection construction method according to claim 3, characterized in that: In step S4, when the dense three-dimensional point cloud is registered and fused with the sparse marker three-dimensional point cloud, each laser scanned point cloud image contains no less than 6 marker points.

5. The large prefabricated component three-dimensional scanning size detection construction method according to claim 1 is characterized in that: In step S4, when registering and fusing the dense 3D point cloud with the sparse marker 3D point cloud, the sparse marker 3D point cloud needs to be scaled according to the scale of the dense 3D point cloud so that the scales of the two are unified.

6. A large prefabricated component three-dimensional scanning size detection construction method according to claim 5, characterized in that: In step S4, the scale transformation steps are as follows: Assume that the original sparse marker 3D point cloud to be transformed is P S , the transformed point cloud is P t , at this time there is only rigid transformation between the two, namely translation and rotation, and the relationship between the two is: P t =R·P S +T Where R is the rotation matrix, as follows: T is the translation matrix, as follows: The rotation matrix R and translation matrix T of the sparse marker 3D point cloud can be directly calculated from the coordinate transformation relationship of the sparse marker 3D point cloud.

7. A large prefabricated component three-dimensional scanning size detection construction method according to claim 1, characterized in that: The global three-dimensional photogrammetry equipment used in the multi-view triangulation technology has a maximum area of ​​no less than 9.4m×6.9m and a volume accuracy of no less than 0.015mm / m.

8. The large prefabricated component three-dimensional scanning size detection construction method according to claim 1, characterized in that: The three-dimensional laser scanning robot used in the laser three-dimensional scanning technology is equipped with a binocular imaging system with an accuracy of not less than 0.02mm.

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