CN115014198B - Reinforcing steel bar installation detection method based on three-dimensional laser scanning - Google Patents
Reinforcing steel bar installation detection method based on three-dimensional laser scanning Download PDFInfo
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- 229910001294 Reinforcing steel Inorganic materials 0.000 title claims abstract description 55
- 238000001514 detection method Methods 0.000 title claims abstract description 33
- 238000009434 installation Methods 0.000 title claims abstract description 30
- 229910000831 Steel Inorganic materials 0.000 claims abstract description 74
- 239000010959 steel Substances 0.000 claims abstract description 74
- 238000000034 method Methods 0.000 claims abstract description 29
- 230000003014 reinforcing effect Effects 0.000 claims abstract description 24
- 239000011241 protective layer Substances 0.000 claims abstract description 10
- 238000006243 chemical reaction Methods 0.000 claims abstract description 3
- 238000004458 analytical method Methods 0.000 claims description 21
- 230000002787 reinforcement Effects 0.000 claims description 15
- 230000011218 segmentation Effects 0.000 claims description 9
- 239000011159 matrix material Substances 0.000 claims description 5
- 238000012549 training Methods 0.000 claims description 3
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- 238000002372 labelling Methods 0.000 claims description 2
- 238000000513 principal component analysis Methods 0.000 claims description 2
- 230000008569 process Effects 0.000 claims description 2
- 230000009466 transformation Effects 0.000 claims description 2
- 238000004141 dimensional analysis Methods 0.000 claims 2
- 238000012216 screening Methods 0.000 claims 1
- 238000013461 design Methods 0.000 abstract description 15
- 238000010276 construction Methods 0.000 abstract description 11
- 239000004567 concrete Substances 0.000 abstract description 6
- 239000010410 layer Substances 0.000 description 8
- 238000005259 measurement Methods 0.000 description 7
- 238000005516 engineering process Methods 0.000 description 4
- 239000011150 reinforced concrete Substances 0.000 description 4
- 230000009286 beneficial effect Effects 0.000 description 3
- 238000009415 formwork Methods 0.000 description 3
- 238000002156 mixing Methods 0.000 description 3
- 238000010586 diagram Methods 0.000 description 2
- 238000007689 inspection Methods 0.000 description 2
- 238000005070 sampling Methods 0.000 description 2
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- 208000004350 Strabismus Diseases 0.000 description 1
- 238000005452 bending Methods 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 230000007797 corrosion Effects 0.000 description 1
- 238000005260 corrosion Methods 0.000 description 1
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Classifications
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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
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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
- G01B11/02—Measuring arrangements characterised by the use of optical techniques for measuring length, width or thickness
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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
- G01B11/02—Measuring arrangements characterised by the use of optical techniques for measuring length, width or thickness
- G01B11/06—Measuring arrangements characterised by the use of optical techniques for measuring length, width or thickness for measuring thickness ; e.g. of sheet material
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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
- G01B11/08—Measuring arrangements characterised by the use of optical techniques for measuring diameters
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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
- G01B11/14—Measuring arrangements characterised by the use of optical techniques for measuring distance or clearance between spaced objects or spaced apertures
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/30—Computing systems specially adapted for manufacturing
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Abstract
The application relates to a method for detecting the installation of reinforcing steel bars based on three-dimensional laser scanning, which comprises the steps of firstly planning a measuring station by means of a sight line detection algorithm based on a BIM design model; then scanning by using a ground three-dimensional laser scanner; the data acquired by each measuring station are subjected to coordinate conversion and spliced into a complete reinforcing mesh, and the complete reinforcing mesh is divided into a plurality of areas according to the effective data range; separately analyzing the point cloud data of the steel bars in each area, reconstructing the steel bar models in each area according to the diameter, the position, the distance and the thickness of the protective layer, and splicing and integrating the steel bar models in each area to form a complete steel bar net model; and finally, comparing the real reinforcing steel bar model with the reinforcing steel bar BIM design model. According to the method, the point cloud data of the steel bars in each area are analyzed independently, so that the accuracy of steel bar diameter estimation is guaranteed; through a large amount of point cloud data that gather, the true form of quick construction object, through with BIM design model contrast find out the installation deviation, in time make the adjustment to the deviation, do benefit to and accomplish effective control in advance before concrete placement.
Description
Technical Field
The invention relates to the technical field of steel bar detection, in particular to a steel bar installation detection method based on three-dimensional laser scanning.
Background
The reinforced concrete structure is widely applied to industrial and civil buildings, and the size and the installation position of the reinforced concrete structure are critical to the overall structural performance of the building. Incorrect installation locations can lead to cracking and corrosion of the rebar, causing serious consequences such as structural collapse. Therefore, before concrete is poured, a field engineer checks whether the installation size and position of the reinforcing steel bar are correct or not, and the requirements of the design drawing are met. The project of reinforcing bar detection in the building mainly relates to reinforcing bar mounted position, reinforcing bar diameter, interval between the adjacent reinforcing bars, interval between two-layer reinforcing bar net, protective layer thickness etc.. At present, the method for detecting the steel bars is to manually detect by using a steel rule. However, for large complex structures, manual detection is inefficient and takes a lot of time. In addition, the result of manual detection depends on the expertise level and the operation proficiency of the detection personnel, so the detection result has a certain subjectivity.
For decades, geological radar, ultrasonic, thermal imager, radiographic, etc. technologies have emerged, which, while providing faster, more accurate measurements than manual inspection, do not have a sufficiently high sampling rate for high density measurements of building elements, and these inspection technologies detect after concrete placement and only track the state of the building elements after construction.
In order to realize automatic detection of the reinforcing steel bars, various researches using intelligent sensing technology have been conducted in the art. In recent years, the ground three-dimensional laser scanning technology is increasingly popular in the industries of building measurement, slope deformation monitoring, ancient building repair and the like due to the advantages of high density, rapidness, comprehensiveness, non-contact acquisition of three-dimensional data of a target object and the like. It measures distance by emitting a laser beam and detecting a reflected signal of the target. At present, three-dimensional laser scanning data are usually spliced and then analyzed integrally, but the processing mode is not applicable to steel bar scanning data. The diameter of the steel bar is measured in millimeters, and the instrument has an error of 1-2cm when in scanning operation, so that larger errors can be generated in data splicing, and if the steel bar is spliced first and then analyzed, the influence on the estimation of the diameter of the steel bar is large, so that the analysis result is inaccurate.
Disclosure of Invention
The application provides a method for detecting the installation of reinforcing steel bars based on three-dimensional laser scanning, which aims to solve the technical problems, and specifically comprises the following steps: firstly, carrying out station measurement planning by means of a line-of-sight detection algorithm, then carrying out field scanning on a steel bar construction site by utilizing a ground three-dimensional laser scanner, analyzing data acquired by each station measurement independently, reconstructing a steel bar model of a live-action restoration construction site by utilizing a three-dimensional model according to analysis results of the diameter, the position, the distance and the thickness of a protection layer of the steel bar of each station, and finally comparing a real steel bar model with a steel bar design BIM model to find out installation deviation and timely adjusting. The application divides the reinforcing mesh into a plurality of areas, each area is effectively covered by a single measuring station, and then the analysis results of the measuring stations are spliced. And the independent analysis of the data of each measuring station can reduce the splicing error, and is beneficial to ensuring the accuracy of the diameter estimation of the steel bars.
It is worth to say that the steel bar installation detection method is carried out before concrete is poured, and the steel bar installation deviation can be found by comparing and analyzing the scanning steel bar model and the BIM design model so as to be convenient for timely adjustment, thereby being beneficial to subsequent evaluation of construction quality.
Compared with the prior art, the application has the following beneficial effects:
the application of the three-dimensional laser scanner overcomes the defects of low manual measurement efficiency and few detection samples, reduces the time for field data acquisition, improves the working efficiency and realizes digital steel bar detection;
2, firstly dividing the reinforcing mesh into each area, effectively covering each area by an independent measuring station, and independently estimating the diameter of the reinforcing steel bar by using the cloud data of each area, and splicing after the analysis of each area, so that the splicing error of multi-station scanning data can be avoided, and the estimation accuracy of the diameter of the reinforcing steel bar is ensured;
According to the application, the real form of the object can be quickly constructed through a large amount of collected point cloud data, the position, the diameter, the distance and the thickness information of the protective layer of the steel bar can be truly, accurately and clearly reflected, and a comprehensive quality detection report can be obtained by comparing the data with a BIM design model, so that timely adjustment can be made for the report, and the operation of a construction site can be better guided;
3, the application not only can realize the example segmentation of the main reinforcement and the distributed reinforcement, but also can help to improve the modeling precision of the reinforcement;
4, the invention extracts the linear planeness characteristics of the steel bars and the mixed pixels based on a two-stage algorithm, and can quickly and automatically remove the mixed pixels by adopting linear planeness analysis, and retain the point cloud data of the steel bars;
Compared with a geological radar, the method can realize the prior control, is not limited to sampling detection, has a more comprehensive detection range, and can timely find and comprehensively analyze unreasonable places in the steel bar installation process and make adjustment; meanwhile, the invention can provide more accurate analysis results than that of a geological radar.
Drawings
The accompanying drawings, which are included to provide a further understanding of embodiments of the application and are incorporated in and constitute a part of this specification, illustrate embodiments of the application and together with the description serve to explain the principles of the application.
FIG. 1 is a flow chart of an embodiment of the present application;
FIG. 2 is a flow chart of data analysis of each zone in an embodiment of the application;
FIG. 3 is a schematic illustration of the regions of and overlapping areas in an embodiment;
fig. 4a is a schematic diagram before the mixed pixels are removed, and b is a schematic diagram after the mixed pixels are removed;
Fig. 5 is a partial schematic view of a rebar model in one of the zones in an embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the technical solutions in the embodiments of the present application will be clearly and completely described with reference to the accompanying drawings in the embodiments. It will be apparent that the described embodiments are some, but not all, of the embodiments of the application. The components of the embodiments of the present application generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations.
Thus, the following detailed description of the embodiments of the invention, as presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, based on the embodiments of the invention, which are apparent to those of ordinary skill in the art without inventive faculty, are intended to be within the scope of the invention.
In addition, the embodiments of the present invention and the features of the embodiments may be combined with each other without collision. It should be noted that, in the present specification, each embodiment is described in a progressive manner, and each embodiment is mainly described as different from other embodiments, and identical and similar parts between the embodiments are all enough to be referred to each other.
As shown in fig. 1, the method for detecting the installation of the reinforcing steel bar based on the three-dimensional laser scanning disclosed in the embodiment comprises the following steps:
s1, station measurement planning.
While scanning the rebar grid calls for a station to cover the rebar surface, acquiring all surface data of the rebar tends to be challenging. So in some embodiments, when scanning large scene reinforced concrete structures or denser reinforcing bars, divide the whole reinforcing bar net into a plurality of small areas, effectively reduce the reinforcing bar shielding through increasing the station and rationally planning scanner position. The specific method comprises the following steps:
before the three-dimensional laser scanner works in the field, the scanning distance is calculated by the angular resolution of the instrument and the density of the reinforcing steel bar points, and then the effective data range of each measuring station is calculated by using a line-of-sight detection algorithm. And determining the position of the measuring station based on the scanning distance and the effective data range, completing the planning of the measuring station, and then carrying out the steel bar scanning work.
The position where the complete scanning data coverage can be obtained can be calculated through a sight detection algorithm, and then the position of the measuring station is scientifically determined. Specifically, a BIM model is designed by inputting reinforcing steel bars in a sight line detection algorithm, a scanning target is definitely scanned, and the range of an effective sight line is calculated according to factors such as scanning distance, visibility, coverage rate, accuracy and the like.
It is worth to say that the position of the measuring station is required to ensure that two layers of reinforcing steel bar meshes can be scanned, and the scanning visual angle can squint the reinforcing steel bar meshes, so that the bottom reinforcing steel bar meshes are positioned in gaps of the surface reinforcing steel bar meshes. One measuring station is only responsible for the point cloud of the area, so that the point cloud integrity of the area is ensured.
The sight line detection algorithm has the advantages that all scanning positions are planned before data are acquired, so that the detection efficiency is greatly improved; meanwhile, the station measuring position is calculated based on the field reinforcing steel bar design model, so that the problem of shielding of the lower layer of sight on the reinforcing steel bar net can be reduced.
In particular, each zone has a scan overlap. The overlapping portion refers to an area where scan data in one area is also captured by another area scan acquired from another location. The scan overlap is to facilitate subsequent model stitching, and the overlap range is recommended to be set to 10-20cm.
As shown in fig. 3, the reinforcing mesh is divided into a plurality of areas S1, S2, S3, S4, etc., and in order to improve accuracy, the effective data range of a station is determined as an A1A2A3A4 area, that is, the coverage area of S1, and the coverage areas of S2, S3, S4 can be obtained in the same way. The overlap between the S1 and S2 regions may be denoted as B1A2A3B4, i.e., the area covered by I12, and the relationship of S1, S2, and I12 may be expressed as: i12 Other overlapping ranges and zone relationships are similarly available. Because each measuring station has a large scanning range, but has a small effective data range, the scanning distance is calculated simply by the angular resolution of the instrument and the density of the reinforcing steel bar points, and the specific position of the effective measuring station cannot be determined. According to the application, whether the sight condition exists between two points is judged through sight detection, and then the station measuring position is determined, so that the planned station measuring position not only meets a larger sight range, but also can improve the point cloud precision.
S2, scanning the steel bars by each measuring station, and performing field scanning on the steel bar construction site by using a ground three-dimensional laser scanner.
The ground three-dimensional laser scanner can actively emit a scanning light source, and can rapidly construct the real form of an object by collecting a large amount of point cloud data, so that the information of the steel bars can be truly, accurately and clearly reflected.
And S3, after scanning of each measuring station, carrying out coordinate conversion and splicing on the measuring station data to form a complete reinforcing mesh for finding out an effective data range corresponding to the designed BIM model.
In some embodiments, the station measurement results are spliced into a complete rebar grid by coordinate transformation using a rotational translation matrix. The rotation and translation matrix is
S4, dividing the reinforcing mesh into each area according to the effective data range calculated by the line-of-sight detection algorithm.
S5, independently analyzing the data of each area to obtain the reinforcing steel bar model of each area.
As described above, in the line-of-sight detection algorithm, the complete rebar grid is divided into different zones, and each station is responsible for the point cloud coverage and integrity of its respective zone.
And S6, splicing all the area models, and reconstructing the complete reinforcing steel bar model.
And after the data of each measuring station are analyzed independently, combining the reinforcing steel bars with the same diameter at the overlapping part of each measuring area, and realizing splicing of reinforcing steel bar models by using a weighted average method. And (3) finding the circle centers of the same steel bar in different areas, and calculating the final position of the steel bar by using the average distance between the two circle centers. Once the diameter and the position of the reinforcing steel bars are determined, the distance between the reinforcing steel bars and the thickness of the reinforcing steel bar protection layer can be obtained.
S7, comparing the complete steel bar model with the design model to find out deviation.
And after parameter estimation is completed, the point cloud data is imported into BIM software, and the reinforcing steel bar model is compared with the design model, so that the detection of reinforcing steel bar installation is realized. The comparison items comprise a steel bar installation position, a steel bar diameter, a steel bar distance and a protective layer thickness, and if the installation deviation exceeds the allowable range, the adjustment is required to be made in time.
The mounting positions of the steel bars are directly related to the stress performance of the components, and for the densely arranged steel bar meshes, reasonable mounting positions are important guarantees for ensuring the stress stability of the components. The proper arrangement of the reinforcement spacing is required to ensure the correct installation position of the reinforcement. In order to ensure reasonable stress of the components, the detection of the spacing between the steel bars comprises the spacing between single steel bars and the spacing between two layers of steel bar meshes.
It should be noted that there are three stages for evaluating the thickness of the protective layer, before, during and after the form is supported. Specifically, before the template is supported, the scanned reinforcing mesh is compared with the designed BIM model, the position of the theoretical template can be calculated, and therefore the thickness of the pre-estimated protective layer is obtained, and whether the thickness of the pre-estimated protective layer meets the design specification requirement is judged. After the formwork is supported and before concrete is poured, the reinforced bar formwork can be scanned again, so that the thickness of a reinforced bar protection layer on a construction site is calculated, and if the thickness of the protection layer does not meet the design specification requirements, the formwork position adjustment or the reinforced bar position adjustment is timely carried out. After the form is removed, the surface of the reinforced concrete can be scanned to calculate the actual thickness of the protective layer. Compared with the geological radar which can only detect the state of the steel bar after concrete is poured, the application provides three steel bar detection modes of different construction stages by using the ground three-dimensional laser, and the steel bar installation state can be detected from a plurality of stages, thereby ensuring that the steel bar installation accords with the design specification.
The diameters of the steel bars are calculated in millimeter level, so that the requirement on the accuracy of point cloud is high, and the traditional method of splicing and then analyzing is not applicable to steel bar scanning engineering. According to the embodiment, the point cloud data of each measuring station are analyzed independently, and finally, the results of each measuring station are subjected to superposition comprehensive analysis, so that the accuracy and the integrity can be ensured.
It should be noted that, the coordinate system of the scanned reinforcing mesh is different from the coordinate system of the ground three-dimensional laser, so as to facilitate subsequent calculation. As shown in fig. 2, in some embodiments, the analysis of each zone data includes the steps of:
S5.1, preprocessing data, including the following steps:
and 5.1.1, converting the coordinate system of the point cloud into the coordinate system of the engineering.
If the steel bars are horizontally arranged, the stressed steel bars are parallel to the x axis, and the distributing bars are parallel to the y axis; if the steel bars are in bending arrangement, the steel bars are directly unfolded according to a steel bar design model, so that the main bars are parallel to the x axis, and the distributed bars are parallel to the y axis.
5.1.2, The blended pixels generated during the scan are removed by linear planarity analysis, as shown in fig. 4.
In the point cloud data, since the linear value of the rebar is higher and the plane value of the mixed pixel is higher, this embodiment uses this feature to distinguish the rebar point cloud from the mixed pixel.
Based on the geometric characteristics of the mixed pixels of the steel bars, dimension analysis is performed first. The geometry features include linearity a 1D and planarity a 2D. The mixed pixels can be manually screened out first to train the geometric features of the mixed pixels of the reinforcing steel bars; and the training results are used to identify all points in the point cloud data associated with the rebar to determine a threshold of linearity T 1D and a threshold of planarity T 2D. For each point P i in the point cloud data, principal component analysis is performed based on the neighboring points of P i, three eigenvectors lambda 1,λ2,λ3 are obtained from the covariance matrix, and lambda 1≥λ2≥λ3 is equal to or greater than 0, and then the linearity and the planarity can be expressed as:
If the linear value is greater than the trained threshold a 1D＞T1D, or the plane value satisfies a 2D＜T2D, then the point is marked as linear; and vice versa.
In some embodiments, points within a 3-fold radius neighborhood of the rebar are taken as the neighborhood points of P i.
It is worth noting that dimension analysis can only remove most of the blended pixels, but also a small number of edge blended pixels. For edge blending pixels, densitometric removal may be employed. The density analysis is to remove edge blending pixels using rebar and blending pixel density features. The principle is as follows: reinforcing steel bars exist in places with large point cloud density, the density of mixed pixels is smaller than that of the reinforcing steel bars, and residual edge mixed pixels in the previous stage can be removed by utilizing the characteristic to perform density analysis, so that a complete reinforcing steel bar net is extracted.
S5.2, point cloud segmentation, comprising the following steps of:
5.2.1, semantic segmentation is carried out to distinguish the types of the steel bars;
In theory, the main rib direction is parallel to the x-axis direction, and the distribution ribs are parallel to the y-axis. In the actual reinforcement process, there is often a certain angle error, and in some embodiments, the angle error value is calculated by using the direction cosine. The first principal component is equal to the direction cosine because they are unit vectors, then the first principal component Can be expressed as:
Wherein, alpha i,βi,γi is the included angle between the first main component and the x, y and z axes respectively.
To avoid 180,Should be defined in the first quadrant, then/>The angles alpha i,βi and gamma i can be calculated by the above formula, and the coordinate axis corresponding to the smallest calculated angle is most likely the installation direction of the steel bar.
Meanwhile, the angle error value T mis should be equal to or less than 54.736 ° when a=β=γ, depending on the construction site. When T mis determines, the reinforcement semantic segmentation can be performed: main reinforcement is alpha less than T mis, distribution reinforcement is beta less than T mis, stirrup is alpha more than or equal to T mis and beta more than or equal to T mis.
5.2.2, Example labeling, evaluating the spacing of the reinforcing bars, and distinguishing each reinforcing bar.
Instance marking of rebar points is critical to assessing the spacing of each rebar, so instance IDs should be marked for each rebar point to distinguish between different rebar in the same semantic. In general, the points of rebar belonging to the same instance should be close and the points belonging to different instances should be further apart.
Example segmentation algorithms include rebar segment clustering and rebar segment growth. If the distance between two rebar points is less than a threshold T dist, they belong to the same cluster, the threshold should be small enough to ensure that different rebar cannot belong to the same cluster, the magnitude of the threshold depends on the actual spacing at which the rebar is placed. The reinforcing steel bar points of the same cluster are marked as a segment, one segment is randomly selected as a growing segment, one end point of the growing segment is connected with the nearest point of the candidate reinforcing steel bar segment, the angle formed by the vector and the growing segment is smaller than a given threshold T grow, and otherwise the two segments cannot be connected as a new growing segment. The connected rebar segment will be assigned an instance ID if no connection points are found, and then another rebar segment is randomly selected as a new growth segment until no rebar segments can be connected.
S5.3, parameter estimation is carried out, and a single steel bar scanning model is obtained.
The circular RANSAC is used to estimate the coordinate position and diameter of the rebar. Several cross sections of one steel bar are cut, and circular fitting is carried out on the cross sections to estimate the circle center. To fit p data points into a circle, three points can determine a circle, so n is more than or equal to 3, the iteration number k is set, n points are randomly selected from p to fit a model, M1 is marked, the allowable error epsilon is set as the allowable deviation of the reinforcing steel bar, when the distance between one point and the model is smaller than epsilon, the point is judged to be an inner point, otherwise, the point is an outer point, when the number of the inner points is larger than the set threshold t, the model is reasonable, and the model of the maximum point set of the inner points is adopted. After the circle fitting is completed, the circle center and the radius of each cross section can be determined, and finally, the estimated circle centers are connected to form the scanned single steel bar model.
And S5.4, combining the single reinforcement models in each area to obtain the reinforcement model of the area, as shown in figure 5.
According to the application, the reinforcing mesh is divided into each area, the single measuring station scans, and the scanning data of each area are analyzed independently, so that the multi-station splicing error is reduced, and the accuracy of reinforcing diameter estimation is ensured. According to the application, the real form of the object can be quickly constructed through a large amount of collected point cloud data, and a comprehensive quality detection report can be obtained through comparison with the BIM design model, so that timely adjustment can be made for the report, and the operation of a construction site can be better guided.
The foregoing detailed description of the application has been presented for purposes of illustration and description, and it should be understood that the application is not limited to the particular embodiments disclosed, but is intended to cover all modifications, equivalents, alternatives, and improvements within the spirit and principles of the application.
Claims (8)
1. A rebar installation detection method based on three-dimensional laser scanning is characterized by comprising the following steps of: the method comprises the following steps:
s1, calculating a scanning distance according to the angle resolution of a three-dimensional laser scanner and the density of steel bar points, designing a BIM model based on steel bars, determining the effective data range of each measuring station by using a sight detection algorithm, and determining the position of each measuring station based on the scanning distance and the effective data range;
s2, scanning the steel bars by each measuring station;
s3, carrying out coordinate conversion and splicing on the data of each measuring station to form a complete reinforcing mesh;
S4, dividing the reinforcing mesh in the S3 into a plurality of areas according to the effective data range calculated by the sight line detection algorithm in the S1, wherein each area is effectively covered by a single measuring station;
S5, independently analyzing the data acquired by each measuring station, and acquiring a reinforcing steel bar model of each area according to the analysis results of the diameter, the position, the spacing and the thickness of the protective layer of the reinforcing steel bars of each measuring station;
s6, splicing the reinforcement models in each area to reconstruct a real reinforcement model;
S7, comparing the real reinforcing bar model obtained in the S6 with a designed BIM model to obtain deviation; the comparison items comprise a reinforcing steel bar installation position, a reinforcing steel bar diameter, a reinforcing steel bar spacing and a protective layer thickness;
the step S5 comprises the following steps:
s5.1, converting a coordinate system of the point cloud into an engineering coordinate system, and removing mixed pixels generated in the scanning process through linear planeness analysis;
s5.2, point cloud segmentation, including:
Semantic segmentation of the steel bar type;
example labeling, namely evaluating the intervals of the steel bars and distinguishing each steel bar;
s5.3, parameter estimation is carried out to obtain a scanning model of a single steel bar;
s5.4, combining the single reinforcement models in each area to obtain a reinforcement model of the area;
the semantic segmentation method comprises the following steps:
Calculating an angle error value using a direction cosine Size, when/>Determining, namely carrying out semantic segmentation of the steel bars:
The first principal component is equal to the direction cosine, the first principal component Can be expressed as:
In the above-mentioned method, the step of, ，/>，/>For the first principal component and/>, respectively,/>And/>An included angle of the shaft;
Depending on the actual circumstances of the situation, Should be limited in the first quadrant, then/>Calculating/> by the formula (1)，/>And/>The coordinate axis corresponding to the smallest calculated angle is the installation direction of the steel bar;
If it is The main rib; if/>Distributing ribs; if/>The stirrup is used.
2. The method for detecting the installation of the reinforcing steel bar based on three-dimensional laser scanning as set forth in claim 1, wherein the method comprises the following steps: in the step S3, the coordinate transformation is performed using a rotation translation matrix.
3. The method for detecting the installation of the reinforcing steel bar based on three-dimensional laser scanning as set forth in claim 1, wherein the method comprises the following steps: each area has scanning overlapping parts, and each area is spliced together through the overlapping areas in the step S6;
The overlapping portion refers to an area where scan data in one area is also captured by another area scan acquired from another location.
4. A method for detecting the installation of reinforcing steel bars based on three-dimensional laser scanning as set forth in claim 3, wherein: and combining the reinforcing steel bars with the same diameter at the overlapping parts of all the areas, and realizing splicing of reinforcing steel bar models by using a weighted average method.
5. The method for detecting the installation of the reinforcing steel bar based on three-dimensional laser scanning as set forth in claim 1, wherein the method comprises the following steps: when (when)，/>54.736°。
6. The method for detecting the installation of the reinforcing steel bar based on three-dimensional laser scanning as set forth in claim 1, wherein the method comprises the following steps: the specific method of S5.3 is as follows: estimating the coordinate position and the diameter of the steel bar by using a circular RANSAC, intercepting a plurality of cross sections of one steel bar, and respectively carrying out circular fitting on the cross sections to estimate the circle center;
Fitting p data points into a circle, determining a circle by three points, setting the iteration number k, randomly selecting n points from p to fit a model, marking M1, and allowing error Is set as the allowable deviation of the steel bar, when the distance between a point and the model is less than/>, the distance between the point and the model is less thanJudging the point as an inner point, otherwise, judging the point as an outer point, and when the number of the inner points is larger than a set threshold t, reasonably adopting a model of the maximum point set of the inner points; after the circle fitting is completed, determining the circle center and the radius of each cross section, and finally connecting the estimated circle centers to obtain the scanned single steel bar model.
7. The method for detecting the installation of the reinforcing steel bar based on three-dimensional laser scanning as set forth in claim 1, wherein the method comprises the following steps: in S5.1, most of the mixed pixels are removed by dimension analysis, and then the edge mixed pixels are removed by density analysis.
8. The method for detecting the installation of the reinforcing steel bar based on three-dimensional laser scanning as set forth in claim 7, wherein the method comprises the following steps: dimensional analysis is performed based on geometric features of mixed pixels of reinforcing steel bars, wherein the geometric features comprise linearityAnd planarity/>The dimension analysis method comprises the following steps: firstly, screening out the mixed pixels manually to train the geometric characteristics of the mixed pixels of the reinforcing steel bars; and using the training result to identify all points associated with the steel bar in the point cloud data to determine a linear threshold/>And threshold of planarity/>；
For each point in the point cloud dataBased on/>Principal component analysis is performed on neighboring points of (2) to obtain three eigenvectors/>, from the covariance matrix,/>,/>And/>Linearity and planarity can be expressed as:
if the linear value is greater than the training threshold Or the plane value satisfies/>The point is marked as linear; vice versa;
The density analysis utilizes the density characteristics of the reinforcing steel bars and the mixed pixels to remove the edge mixed pixels, the reinforcing steel bars exist in the places with large point cloud density, the density of the mixed pixels is smaller than that of the reinforcing steel bars, the density analysis is performed by utilizing the density characteristics, and then the edge mixed pixels remained in the dimensional analysis are removed.
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