CN114841965A - Steel structure deformation detection method and device, computer equipment and storage medium - Google Patents
Steel structure deformation detection method and device, computer equipment and storage medium Download PDFInfo
- Publication number
- CN114841965A CN114841965A CN202210491706.7A CN202210491706A CN114841965A CN 114841965 A CN114841965 A CN 114841965A CN 202210491706 A CN202210491706 A CN 202210491706A CN 114841965 A CN114841965 A CN 114841965A
- Authority
- CN
- China
- Prior art keywords
- steel structure
- data
- dimensional
- point cloud
- contour
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/0002—Inspection of images, e.g. flaw detection
- G06T7/0004—Industrial image inspection
- G06T7/001—Industrial image inspection using an image reference approach
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
- G01B21/00—Measuring arrangements or details thereof, where the measuring technique is not covered by the other groups of this subclass, unspecified or not relevant
- G01B21/32—Measuring arrangements or details thereof, where the measuring technique is not covered by the other groups of this subclass, unspecified or not relevant for measuring the deformation in a solid
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/13—Edge detection
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/30—Determination of transform parameters for the alignment of images, i.e. image registration
- G06T7/33—Determination of transform parameters for the alignment of images, i.e. image registration using feature-based methods
- G06T7/337—Determination of transform parameters for the alignment of images, i.e. image registration using feature-based methods involving reference images or patches
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/60—Analysis of geometric attributes
- G06T7/66—Analysis of geometric attributes of image moments or centre of gravity
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10028—Range image; Depth image; 3D point clouds
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20016—Hierarchical, coarse-to-fine, multiscale or multiresolution image processing; Pyramid transform
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20112—Image segmentation details
- G06T2207/20164—Salient point detection; Corner detection
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30108—Industrial image inspection
- G06T2207/30136—Metal
-
- 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
Abstract
The application discloses a steel structure deformation detection method, a device, computer equipment and a storage medium, which relate to the technical field of computer application, the method comprises the steps of obtaining three-dimensional point cloud data of a steel structure, extracting two-dimensional outline data of the steel structure according to the three-dimensional point cloud data, roughly registering the two-dimensional outline data with a preset steel structure outline template to obtain first measurement data, finely registering the first measurement data according to an iterative closest point algorithm to obtain second measurement quantity, and calculating the minimum distance error from the second measurement data to the steel structure outline template to determine the deformation quantity of the steel structure, wherein on one hand, the two-dimensional outline data is beneficial to reducing unnecessary interference data and reducing the calculation quantity of steel structure deformation, on the other hand, the two-dimensional outline data of the steel structure is roughly registered and finely registered, namely, a double registration mode is beneficial to improving the accuracy of the steel structure variable, and then realize comprehensive, accurate, quick steel construction deformation analysis effect.
Description
Technical Field
The application relates to the technical field of engineering structure deformation detection, in particular to a steel structure deformation detection method and device, computer equipment and a storage medium.
Background
The steel structure is widely applied to large-scale engineering construction sites due to the excellent properties of large strength, large span, good shaping, high temperature resistance and the like. However, the steel structure is affected by the external environment after being assembled and formed, that is, the steel structure is affected by the change of the surrounding load, such as the change of weather, e.g. wind, temperature, etc., so that the internal stress of the steel structure can be changed, resulting in the local non-uniform deformation of the steel structure. Deformation of a steel structure is a common problem in the engineering and industrial fields, and has adverse effects on normal construction of engineering and normal operation of industry. If the deformation of the steel structure exceeds the alarm value, safety accidents can occur, and therefore the safety of people is threatened. Therefore, the deformation of the steel structure needs to be detected in time, the safety state of the steel structure needs to be analyzed, and the subsequent deformation trend needs to be predicted.
For deformation detection of a steel structure, a total station instrument measurement mode is mainly adopted in a traditional detection means, namely three-dimensional coordinates of spatial feature points are collected one by one in a steel structure deformation area, and the geometric relation between the feature points of the deformation area and a spatial reference plane and line is analyzed according to point location coordinate values, so that a deformation value is calculated. The detection means has low field operation efficiency and large calculation amount, and the deformation value of the spatial discrete point is difficult to comprehensively and accurately reflect the spatial integral deformation condition of the steel structure.
Aiming at the problems that the processing and the whole deformation analysis of the steel structure point cloud data are difficult to realize comprehensively, accurately and efficiently in the prior art, an effective solution is not provided at present.
Disclosure of Invention
The embodiment of the application aims to provide a steel structure deformation detection method to solve the problems of low efficiency and low accuracy of the existing steel structure deformation measurement.
In order to solve the technical problem, an embodiment of the present application provides a steel structure deformation detection method, including the following steps:
acquiring three-dimensional point cloud data of a steel structure;
extracting two-dimensional contour data of the steel structure according to the three-dimensional point cloud data;
roughly registering the two-dimensional contour data with a preset steel structure contour template to obtain first measurement data;
performing fine registration on the first measurement data according to an iterative closest point algorithm to obtain a second measurement quantity;
and calculating the minimum distance error of the second measurement data to the steel structure contour template so as to determine the deformation amount of the steel structure.
In some embodiments, extracting two-dimensional profile data of the steel structure from the three-dimensional point cloud data comprises:
determining the extension direction of the steel structure according to the three-dimensional point cloud data of the steel structure;
determining bounding box data of the steel structure from the three-dimensional point cloud data of the steel structure;
two-dimensional profile data of the steel structure is extracted from the bounding box data according to the extension direction.
In some embodiments, determining the extension direction of the steel structure from the three-dimensional point cloud data of the steel structure comprises:
determining mass center data of the three-dimensional point cloud data from the three-dimensional point cloud data of the steel structure;
constructing a covariance matrix according to the three-dimensional point cloud data and the centroid data;
and determining the extension direction of the steel structure according to the covariance matrix.
In some embodiments, extracting two-dimensional profile data of the steel structure from the bounding box data according to the extension direction comprises:
determining a cross section corresponding to the bounding box data according to the extension direction and the centroid data;
according to the distance from the bounding box data to the cross section, the bounding box data corresponding to the distance which accords with the preset distance interval is taken as the section data;
the cross-sectional data is projected onto the cross-section to obtain two-dimensional profile data.
In some embodiments, the steel structure contour template includes a plurality of second angular points and a plurality of second triangles constructed according to the second angular points, and the coarse registration of the two-dimensional contour data with the preset steel structure contour template to obtain the first measurement data includes:
acquiring a first corner of two-dimensional contour data;
constructing a plurality of first triangles corresponding to the first corner points;
performing matching calculation on the first triangle and the second triangle to obtain a rotation matrix and a translation vector;
and converting the rotation matrix and the translation vector to obtain first measurement data.
In some embodiments, acquiring the first corner point of the two-dimensional profile data comprises:
acquiring a plurality of adjacent points of two-dimensional contour data;
connecting the plurality of adjacent points with the two-dimensional contour data to obtain a plurality of edges;
acquiring an included angle formed between every two edges from the plurality of edges, wherein the included angle comprises an angle value;
and taking the two-dimensional profile data corresponding to the included angle which accords with the preset angle value as a first angular point.
In some embodiments, matching the first triangle and the second triangle to obtain the rotation matrix and the translation vector comprises:
acquiring a plurality of similar triangles matched with the first triangle from the second triangle;
calculating an initial rotation matrix and an initial translation vector of coincidence of corresponding points between every two similar triangles;
acquiring a plurality of registration errors according to the initial rotation matrix and the initial translation vector;
and determining the minimum registration error from the plurality of registration errors, and taking the initial rotation matrix and the initial translation vector corresponding to the minimum registration error as the final rotation matrix and translation vector.
In order to solve the technical problem, the embodiment of the application provides a steel construction deformation detection device, and steel construction deformation detection device includes:
the acquisition module is used for acquiring three-dimensional point cloud data of the steel structure;
the extraction module is used for extracting the two-dimensional contour data of the steel structure according to the three-dimensional point cloud data;
the rough registration module is used for carrying out rough registration on the two-dimensional contour data and a preset steel structure contour template to obtain first measurement data;
the fine registration module is used for performing fine registration on the first measurement data according to an iterative closest point algorithm to obtain a second measurement quantity;
and the calculation module is used for calculating the minimum distance error from the second measurement data to the steel structure contour template so as to determine the deformation amount of the steel structure.
In some embodiments, the extraction module comprises:
the extension direction determining unit is used for determining the extension direction of the steel structure according to the three-dimensional point cloud data of the steel structure;
the bounding box unit is used for determining bounding box data of the steel structure from the three-dimensional point cloud data of the steel structure;
and the extraction unit is used for extracting the two-dimensional contour data of the steel structure from the bounding box data according to the extending direction.
In some embodiments, the extension direction determining unit includes:
the centroid subunit is used for determining centroid data of the three-dimensional point cloud data from the three-dimensional point cloud data of the steel structure;
the matrix subunit is used for constructing a covariance matrix according to the three-dimensional point cloud data and the centroid data;
and the extension direction subunit is used for determining the extension direction of the steel structure according to the covariance matrix.
In some embodiments, the extraction unit comprises:
the cross section subunit is used for determining a cross section corresponding to the bounding box data according to the extension direction and the mass center data;
the section data subunit is used for taking bounding box data corresponding to the distance which accords with the preset distance interval as section data according to the distance from the bounding box data to the cross section;
and the projection subunit is used for projecting the section data to the cross section so as to acquire two-dimensional contour data.
In some embodiments, the steel structure contour template includes a plurality of second corner points and a plurality of second triangles constructed from the second corner points, and the coarse registration module includes:
an angular point acquisition unit, configured to acquire a first angular point of the two-dimensional contour data;
the construction unit is used for constructing a plurality of first triangles corresponding to the first corner points;
the matching calculation unit is used for performing matching calculation on the first triangle and the second triangle to obtain a rotation matrix and a translation vector;
and the conversion unit is used for converting the rotation matrix and the translation vector to obtain first measurement data.
In some embodiments, the corner point acquiring unit comprises:
a near point acquiring subunit, configured to acquire a plurality of near points of the two-dimensional contour data;
the side connection subunit is used for connecting the plurality of adjacent points with the two-dimensional contour data to obtain a plurality of sides;
the included angle obtaining subunit is used for obtaining an included angle formed between every two edges from the multiple edges, and the included angle comprises an angle value;
and the angular point acquisition subunit is used for taking the two-dimensional profile data corresponding to the included angle which accords with the preset angle value as the first angular point.
In some embodiments, the matching calculation unit comprises:
the similarity subunit is used for acquiring a plurality of similar triangles matched with the first triangle from the second triangle;
the attitude calculation subunit is used for calculating an initial rotation matrix and an initial translation vector of coincidence of corresponding points between every two similar triangles;
a registration error obtaining subunit, configured to obtain a plurality of registration errors according to the initial rotation matrix and the initial translation vector;
and the posture determining subunit is used for determining the minimum registration error from the plurality of registration errors and taking the initial rotation matrix and the initial translation vector corresponding to the minimum registration error as the final rotation matrix and the final translation vector.
In order to solve the technical problem, an embodiment of the present application further provides a computer device, which includes a memory and a processor, where the memory stores a computer program, and the processor implements the steps of the steel structure deformation detection method when executing the computer program.
In order to solve the above technical problem, an embodiment of the present application further provides a computer-readable storage medium, where a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the steps of the steel structure deformation detection method are implemented.
Compared with the prior art, the embodiment of the application mainly has the following beneficial effects:
the method comprises the steps of obtaining three-dimensional point cloud data of a steel structure, extracting two-dimensional outline data of the steel structure according to the three-dimensional point cloud data, roughly registering the two-dimensional outline data with a preset steel structure outline template to obtain first measurement data, finely registering the first measurement data according to an iterative closest point algorithm to obtain second measurement data, and calculating the minimum distance error between the second measurement data and the steel structure outline template to determine the deformation amount of the steel structure, wherein on one hand, the three-dimensional point cloud data improves the efficiency and the precision of steel structure surface data acquisition, on the other hand, the three-dimensional point cloud data is beneficial to reducing unnecessary interference data extracted from the three-dimensional point cloud data, the calculation amount of steel structure deformation is reduced, the registration and calculation efficiency is improved, on the other hand, the two-dimensional outline data of the steel structure is roughly registered and finely registered, namely, the double registration mode is beneficial to improving the accuracy of steel structure variable measurement, and then realize comprehensive, accurate, quick steel construction deformation analysis effect.
Drawings
In order to more clearly illustrate the solution of the present application, the drawings needed for describing the embodiments of the present application will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present application, and that other drawings can be obtained by those skilled in the art without inventive effort.
FIG. 1 is an exemplary system architecture diagram in which the present application may be applied;
FIG. 2 is a schematic flow chart of a steel structure deformation detection method provided in an embodiment of the present application;
3-a is a schematic diagram of steel structure three-dimensional point cloud data in the embodiment of the application;
FIG. 3-b is a schematic diagram of a two-dimensional contour extraction and corner detection result of a steel structure according to an embodiment of the present application;
FIG. 4 is a schematic view of a steel structure profile template according to an embodiment of the present application;
FIG. 5 is a schematic diagram illustrating the result of coarse registration of three-dimensional point cloud data with a design template based on template contour matching according to an embodiment of the present application;
FIG. 6 is a schematic diagram of a three-dimensional point cloud data and a design template fine registration result according to an embodiment of the present application;
FIG. 7 is a schematic structural diagram of an embodiment of the steel structure deformation detection device provided by the present application;
FIG. 8 is a schematic block diagram illustrating one embodiment of a computer device provided herein.
Detailed Description
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs; the terminology used in the description of the application herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application; the terms "including" and "having," and any variations thereof, in the description and claims of this application and the description of the above figures are intended to cover non-exclusive inclusions. The terms "first," "second," and the like in the description and claims of this application or in the above-described drawings are used for distinguishing between different objects and not for describing a particular order.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the application. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. It is explicitly and implicitly understood by one skilled in the art that the embodiments described herein can be combined with other embodiments.
In order to make the technical solutions better understood by those skilled in the art, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings.
As shown in fig. 1, the system architecture 100 may include terminal devices 101, 102, 103, a network 104, and a server 105. The network 104 serves as a medium for providing communication links between the terminal devices 101, 102, 103 and the server 105. Network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, to name a few.
The user may use the terminal devices 101, 102, 103 to interact with the server 105 via the network 104 to receive or send messages or the like. The terminal devices 101, 102, 103 may have various communication client applications installed thereon, such as a web browser application, a shopping application, a search application, an instant messaging tool, a mailbox client, social platform software, and the like.
The terminal devices 101, 102, 103 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, e-book readers, MP3 players (Moving Picture Experts Group Audio Layer III, mpeg compression standard Audio Layer 3), MP4 players (Moving Picture Experts Group Audio Layer IV, mpeg compression standard Audio Layer 4), laptop portable computers, desktop computers, and the like.
The server 105 may be a server providing various services, such as a background server providing support for pages displayed on the terminal devices 101, 102, 103.
It should be noted that the steel structure deformation detection method provided in the embodiment of the present application is generally executed by a server/terminal device, and accordingly, the steel structure deformation detection apparatus is generally disposed in the server/terminal device.
It should be understood that the number of terminal devices, networks, and servers in fig. 1 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.
In the embodiment of the present application, as shown in fig. 2, fig. 2 is a schematic flow chart of a steel structure deformation detection method provided in the embodiment of the present application, and the concrete implementation of the steel structure deformation detection method includes:
s201: and acquiring three-dimensional point cloud data of the steel structure.
The three-dimensional point cloud data is a massive point set which expresses target space distribution and target surface characteristics under the same space reference system, and after the space coordinates of each sampling point on the surface of an object are obtained, the point set is obtained.
The three-dimensional point cloud data includes a three-dimensional image of the steel structure, which expresses data of three dimensions in space, such as length, width, and depth. The three-dimensional point cloud data is mainly divided into two types according to the measurement principle, one type is obtained according to the laser measurement principle and comprises three-dimensional coordinates (X, Y, Z) and laser reflection Intensity (Intensity), and the Intensity information is related to the surface material, roughness and incident angle direction of a target, the emission energy of an instrument and the laser wavelength; the other is obtained according to photogrammetric principles, including three-dimensional coordinates (X, Y, Z) and color information (RGB). In the embodiment of the application, the three-dimensional point cloud data of the steel structure is acquired by adopting the depth camera.
As shown in fig. 3-a, fig. 3-a is a schematic diagram of steel structure three-dimensional point cloud data according to an embodiment of the present application.
S202: and extracting two-dimensional outline data of the steel structure according to the three-dimensional point cloud data.
Furthermore, two-dimensional data of the steel structure outline is extracted from the three-dimensional point cloud data, namely each two-dimensional data represents coordinate information of a two-dimensional outline point, namely the two-dimensional outline data.
In some embodiments, the extracting the two-dimensional profile data of the steel structure from the three-dimensional point cloud data specifically includes the following steps:
determining the extension direction of the steel structure according to the three-dimensional point cloud data of the steel structure;
determining bounding box data of the steel structure from the three-dimensional point cloud data of the steel structure;
two-dimensional profile data of the steel structure is extracted from the bounding box data according to the extension direction.
Specifically, in order to measure the deformation condition inside the steel structure, the internal structure condition of the steel structure, namely the extending direction of the steel structure, needs to be determined, and then the enclosure data around the extending direction needs to be determined.
In some embodiments, determining the extension direction of the steel structure from the three-dimensional point cloud data of the steel structure comprises:
determining mass center data of the three-dimensional point cloud data from the three-dimensional point cloud data of the steel structure;
constructing a covariance matrix according to the three-dimensional point cloud data and the centroid data;
and determining the extension direction of the steel structure according to the covariance matrix.
For example, the extending direction of the steel structure is determined, and may be obtained according to the formula (1) and the formula (2).
Further, determining the centroid data of the three-dimensional point cloud data from the three-dimensional point cloud number of the steel structure is shown in formula (1):
where o is the centroid, the centroid data may be expressed as o ═ o (o) x ，o y ，o z )，o x ，o y ，o z Respectively expressed as three-dimensional coordinates of the mass center on a space coordinate system, k is the number of three-dimensional space points in three-dimensional point cloud data p, p i Is any three-dimensional point cloud data.
Further, a covariance matrix is constructed according to the three-dimensional point cloud data and the centroid data as shown in formula (2):
wherein CV is a covariance matrix including three eigenvalues corresponding to each eigenvector,
is the vector outer product sign.
Further, the eigenvector corresponding to the maximum eigenvalue of the covariance matrix is the direction of a straight line fit by the three-dimensional point cloud data of the steel structure, that is, the extension direction is obtained by fitting. Therefore, the eigenvector corresponding to the largest eigenvalue is obtained from the covariance matrix CV, and is used as the extension direction of the steel structure three-dimensional data, and the extension direction can be expressed as
Wherein, a x ，a y ，a z Respectively expressed as three-dimensional coordinates of the extension direction on a spatial coordinate system, i.e. an x-coordinate, a y-coordinate and a z-coordinate.
In the embodiment of the application, the covariance matrix is constructed by combining the mass center data of the three-dimensional point cloud data of the steel structure and according to the three-dimensional point cloud data and the mass center data, so that the extension direction of the steel structure is determined, and basic data support is provided for subsequent comprehensive, accurate and efficient processing and overall deformation analysis of the three-dimensional point cloud data of the steel structure.
In some embodiments, extracting two-dimensional profile data of the steel structure from the bounding box data according to the extension direction comprises:
determining a cross section corresponding to the bounding box data according to the extension direction and the centroid data;
according to the distance from the bounding box data to the cross section, the bounding box data corresponding to the distance which accords with the preset distance interval is taken as the section data;
the cross-sectional data is projected onto the cross-section to obtain two-dimensional profile data.
Specifically, the bounding box algorithm is an algorithm for solving an optimal bounding space of a discrete point set, and the basic idea is to approximately replace a complex geometric object with a geometric body (called a bounding box) with a slightly larger volume and simple characteristics, so that the bounding box data of a steel structure can be determined from the three-dimensional point cloud data by using the bounding box algorithm, that is, the bounding box data is partial three-dimensional point cloud data of the steel structure, and the bounding box algorithm can be, but is not limited to, an AABB bounding box, a bounding sphere, a directional bounding box OBB, a fixed-direction convex hull FDH, and the like, and is not limited herein.
Further, perpendicular to the extending direction of the steel structure, the two-dimensional profile data of the steel structure is intercepted at the middle part of the bounding box data of the steel structure, and the concrete realization can be obtained according to the formula (3) and the formula (6):
determining the corresponding cross section of the bounding box data from the extension direction and the centroid data can be obtained according to equation (3):
a x ·x+a y ·y+a z z + c ═ 0 equation (3)
Wherein the direction of extension
c＝-(a x ·o x +a y ·o y +a z ·o z ) Data of center of mass
That is, formula (3) is an expression of a cross section based on bounding box data
And the expression of the known cross section, the data of each bounding box can be obtained
At a distance from the cross-section of
Wherein b represents the number of points in the bounding box, and the specific number is influenced by the sparsity degree of the point cloud acquired by the sensor.
Further, all distances d are recorded i Points falling within a predetermined distance interval, e.g. the predetermined distance interval is set to [0, 0.015 ]]That is, the bounding box data falling within the preset distance interval is taken as the section data, and the section data is projected to the cross section to obtain the two-dimensional profile data
Thus obtaining the two-dimensional contour points. Wherein the content of the first and second substances,
the representation is point cloud data corresponding to the two-dimensional contour, m represents the number of data points on the cross section, and the specific number is influenced by the sparsity degree of the point cloud collected by the sensor.
Exemplarily, fig. 3-b is a schematic diagram of a two-dimensional profile extraction and corner detection result of a steel structure according to an embodiment of the present application. The surrounding box data of the overall surrounding steel structure is determined from the three-dimensional point cloud data, and then the surrounding data in the extending direction is calculated and projected to screen out the two-dimensional outline data of the steel structure, so that the interference of other three-dimensional point cloud data irrelevant to the calculation of steel structure deformation data is reduced, and meanwhile, the two-dimensional data amount is far less than that of the three-dimensional point cloud data, so that the measuring efficiency of the steel structure is improved.
S203: and carrying out coarse registration on the two-dimensional profile data and a preset steel structure profile template to obtain first measurement data.
The first measurement data are obtained by rough registration calculation of two-dimensional contour data.
As shown in fig. 4, fig. 4 is a schematic view of a steel structure contour formwork according to an embodiment of the present application. The preset steel structure outline template is standard structure reference data of the theoretical model outline, whether the actual steel structure deforms or not can be determined through comparison with data of the actual steel structure, and the preset steel structure outline template can be regarded as an ideal model and can be generated through modeling software.
Specifically, the coarse registration of the two-dimensional profile data and a preset steel structure profile template is performed to obtain first measurement data, and the first measurement data comprises:
acquiring a first corner of two-dimensional contour data;
constructing a plurality of first triangles corresponding to the first corner points;
performing matching calculation on the first triangle and the second triangle to obtain a rotation matrix and a translation vector;
and converting the rotation matrix and the translation vector to obtain first measurement data.
In some embodiments, acquiring the first corner point of the two-dimensional profile data comprises:
acquiring a plurality of adjacent points of two-dimensional contour data;
connecting the plurality of adjacent points with the two-dimensional contour data to obtain a plurality of edges;
obtaining an included angle formed between every two edges from the plurality of edges, wherein the included angle comprises an angle value;
and taking the two-dimensional profile data corresponding to the included angle which accords with the preset angle value as a first angular point.
Specifically, using KNN (K-Nearest Neighbor ) algorithm to find w adjacent points from two-dimensional contour points in two-dimensional contour data, and respectively connecting the w adjacent points with the two-dimensional contour points to obtainTo the w edges. Calculating the angle value corresponding to the included angle formed by any two edges to obtain
An angle value. The preset angle value can be set at 90 degrees, if the angle value corresponding to the included angle is distributed near 90 degrees, the two-dimensional contour point corresponding to the included angle at the moment is used as the angular point, namely the first angular point, and if the angle values are mostly distributed near 180 degrees or 0 degrees, the two-dimensional contour point corresponding to the included angle at the moment is not the angular point. As shown in fig. 4, fig. 4 is a schematic diagram of a two-dimensional contour extraction and corner detection result of a steel structure according to an embodiment of the present application.
The first corner of the two-dimensional contour data not only retains the important characteristics of the shape of the steel structure, but also can effectively reduce the data volume of the calculation deformation of the steel structure, effectively improve the calculation speed, be beneficial to the reliable matching of the subsequent steel structure contour template and enable the real-time processing to be possible.
Further, a plurality of first corner points may be obtained in the above manner, and the plurality of first corner points may be regarded as a two-dimensional cross-sectional corner point set, which may be denoted as H. In a two-dimensional cross-section corner point set H, randomly selecting 3 first corner points to form a first triangle, and finally obtaining
A first triangle.
Further, the steel structure contour template comprises a plurality of second corner points of the standard steel structure contour and a plurality of second triangles constructed according to the second corner points, the manner of obtaining the second corner points and the second triangles is the same as the manner of obtaining the first corner points and the first triangles, namely, the plurality of second corner points can be used as a theoretical model contour corner point set I, and optionally three second corner points can be used as a second triangle in the theoretical model contour corner point set I, and finally, the second corner points are obtained
The specific process of the second triangle is not described herein again.
In some embodiments, matching the first triangle and the second triangle to obtain the rotation matrix and the translation vector comprises:
acquiring a plurality of similar triangles matched with the first triangle from the second triangle;
calculating an initial rotation matrix and an initial translation vector of coincidence of corresponding points between every two similar triangles;
acquiring a plurality of registration errors according to the initial rotation matrix and the initial translation vector;
and determining the minimum registration error from the plurality of registration errors, and taking the initial rotation matrix and the initial translation vector corresponding to the minimum registration error as the final rotation matrix and the final translation vector.
In particular, it is directed to
Each of the first triangles is
And searching a plurality of second triangles with the most similarity, namely similar triangles, in the second triangles, wherein the number of the similar triangles can be set according to actual measurement, and 3 similar triangles are obtained in the embodiment of the application. The acquisition mode of the similar triangle comprises the following steps:
the three edge length values of each first triangle are recorded and arranged in order from small to large, that is: for the ith first triangle, the edge length values of the first triangles are respectively
Wherein
Similarly, three edge length values of each second triangle are recorded, and the edge length values of the second triangles are respectively
Calculate any firstThe difference value between triangle i and the second triangle j
d ij Smaller means that the first triangle and the second triangle are more similar; according to d ij Against the rank from
Each of the first triangles is at
The most similar 3 second triangles are searched for from the second triangles.
Further, calculating an initial rotation matrix and an initial translation vector of the coincidence of the corresponding points between each two similar triangles includes:
according to the corresponding relation of the side lengths of the 3 most similar second triangles, the corresponding relation of the vertexes of the second triangles, namely the three vertexes of the first triangle, can be determined
Respectively corresponding to three vertexes of the second triangle
Computing a covariance matrix
Wherein
SVD (Singular Value Decomposition) Decomposition is carried out on the covariance matrix H to obtain [ U, S, V ]]Svd (h), wherein U and V represent two mutually orthogonal matrices and S represents a diagonal matrix; decomposing the obtained U and V to obtain an initial rotation matrix R i ＝VU T Initial translation vector
Further, the registration error is calculated according to equation (4):
and finding an initial rotation matrix and an initial translation vector corresponding to the minimum error in all the registration errors, namely finding a final rotation matrix and a final translation vector.
Further, based on the obtained rotation matrix and translation vector, coarse registration of the two-dimensional profile data of the steel structure and the steel structure profile template is realized to transform into first measurement data, which can be obtained according to a formula (5):
Q＝R * P+L * formula (5)
Wherein R is * Representing the final rotation matrix, P representing the actual two-dimensional profile data, L * And representing a final translation vector, wherein Q is represented as first measurement data, namely a new data set obtained after actual two-dimensional contour data is transformed, and the posture of Q is basically consistent with that of the steel structure contour template.
As shown in fig. 5, fig. 5 is a schematic diagram of a result of coarse registration of three-dimensional point cloud data based on template contour matching and a design template according to an embodiment of the present application.
S204: and carrying out fine registration on the first measurement data according to an iterative closest point algorithm to obtain a second measurement quantity.
Further, the iterative Closest point algorithm is an ICP (iterative Closest point) algorithm, and the iterative Closest point ICP algorithm is used to realize the precise registration of the first measurement data of the steel structure and the steel structure contour template, specifically including: discretizing the steel structure outline template to obtain a point set S; for each point Q in the first measurement data Q i Searching the Nearest point in the point set S by using a KNN (K-Nearest Neighbor, K-adjacent) algorithm, namely q i The corresponding point of (a); calculating a rigid body transformation matrix which minimizes the average distance of the corresponding point pairs; based on this rigid bodyTransforming a matrix, and carrying out spatial transformation on the first measurement data Q to obtain a new point set Q'; iterating the process of finding corresponding points, calculating rigid transformation matrix and transforming the actual measurement point set until the average distance between the corresponding points of the two point sets is less than a threshold value delta, and finally obtaining the actual measurement point set Q through transformation * I.e. the second measurement data.
Specifically, calculating the rigid body transformation matrix includes: calculating a covariance matrix based on the corresponding relationship between points in the point set S and the point set Q
Wherein the content of the first and second substances,
for each point in the first measurement data Q,
for the closest point found in the set of points S, i.e. the corresponding point for each point in the first measurement data Q,
l is the number of corresponding point pairs; SVD decomposition is carried out on the covariance matrix CV to obtain [ U ', S ', V ']Svd (cv), wherein U ' and V ' represent two mutually orthogonal matrices, and S ' represents a diagonal matrix; (ii) a A rigid body transformation matrix minimizing the average distance between the point set Q and the point set S, including the smallest rotation matrix R ═ V 'U' T Minimum translation vector
Further, based on the rigid body transformation obtained above, the rigid body transformation is performed on the first measurement data Q to obtain a new point set Q * I.e. second measurement data, wherein Q * ＝R*Q+t。
As shown in fig. 6, fig. 6 is a schematic diagram of a fine registration result of three-dimensional point cloud data and a design template according to an embodiment of the present disclosure.
S205: and calculating the minimum distance error of the second measurement data to the steel structure contour template so as to determine the deformation amount of the steel structure.
Calculating the minimum distance error from the second measurement data to the steel structure contour template, namely the deformation quantity of the actual steel structure, and comprising the following steps: for Q * And calculating the minimum distance from each two-dimensional contour data point to the steel structure contour template, namely the deformation amount of the actual steel structure.
The method comprises the steps of obtaining three-dimensional point cloud data of a steel structure, extracting two-dimensional outline data of the steel structure according to the three-dimensional point cloud data, roughly registering the two-dimensional outline data with a preset steel structure outline template to obtain first measurement data, finely registering the first measurement data according to an iterative closest point algorithm to obtain second measurement data, and calculating the minimum distance error between the second measurement data and the steel structure outline template to determine the deformation amount of the steel structure, wherein on one hand, the three-dimensional point cloud data improves the efficiency and the precision of steel structure surface data acquisition, on the other hand, the three-dimensional point cloud data is beneficial to reducing unnecessary interference data extracted from the three-dimensional point cloud data, the calculation amount of steel structure deformation is reduced, the registration and calculation efficiency is improved, on the other hand, the two-dimensional outline data of the steel structure is roughly registered and finely registered, namely, the double registration mode is beneficial to improving the accuracy of steel structure variable measurement, and then realize comprehensive, accurate, quick steel construction deformation analysis effect.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a computer-readable storage medium, and can include the processes of the embodiments of the methods described above when the computer program is executed. The storage medium may be a non-volatile storage medium such as a magnetic disk, an optical disk, a Read-Only Memory (ROM), or a Random Access Memory (RAM).
It should be understood that, although the steps in the flowcharts of the figures are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and may be performed in other orders unless explicitly stated herein. Moreover, at least a portion of the steps in the flow chart of the figure may include multiple sub-steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, which are not necessarily performed in sequence, but may be performed alternately or alternately with other steps or at least a portion of the sub-steps or stages of other steps.
With further reference to fig. 7, as an implementation of the method shown in fig. 2, the present application provides an embodiment of a steel structure deformation detection apparatus, which corresponds to the method embodiment shown in fig. 2, and which can be applied to various electronic devices.
As shown in fig. 7, for the structural schematic diagram of an embodiment of the steel structure deformation detection device provided by the present application, the steel structure deformation detection device further includes: an acquisition module 71, an extraction module 72, a coarse registration module 73, a fine registration module 74, and a calculation module 75. Wherein the content of the first and second substances,
the acquisition module 71 is used for acquiring three-dimensional point cloud data of the steel structure;
an extraction module 72, configured to extract two-dimensional contour data of the steel structure according to the three-dimensional point cloud data;
the rough registration module 73 is configured to perform rough registration on the two-dimensional profile data and a preset steel structure profile template to obtain first measurement data;
a fine registration module 74, configured to perform fine registration on the first measurement data according to an iterative closest point algorithm to obtain a second measurement quantity;
and the calculating module 75 is used for calculating the minimum distance error from the second measurement data to the steel structure contour template so as to determine the deformation amount of the steel structure.
In some embodiments, the extraction module 72 includes:
the extension direction determining unit is used for determining the extension direction of the steel structure according to the three-dimensional point cloud data of the steel structure;
the bounding box unit is used for determining bounding box data of the steel structure from the three-dimensional point cloud data of the steel structure;
and the extraction unit is used for extracting the two-dimensional contour data of the steel structure from the bounding box data according to the extending direction.
In some embodiments, the extension direction determining unit includes:
the centroid subunit is used for determining centroid data of the three-dimensional point cloud data from the three-dimensional point cloud data of the steel structure;
the matrix subunit is used for constructing a covariance matrix according to the three-dimensional point cloud data and the centroid data;
and the extension direction subunit is used for determining the extension direction of the steel structure according to the covariance matrix.
In some embodiments, the extraction unit comprises:
the cross section subunit is used for determining a cross section corresponding to the bounding box data according to the extension direction and the mass center data;
the section data subunit is used for taking bounding box data corresponding to the distance which accords with the preset distance interval as section data according to the distance from the bounding box data to the cross section;
and the projection subunit is used for projecting the section data to the cross section so as to acquire two-dimensional contour data.
In some embodiments, the steel structure contour template includes a plurality of second corner points and a plurality of second triangles constructed according to the second corner points, and the coarse registration module 73 includes:
an angular point acquisition unit, configured to acquire a first angular point of the two-dimensional contour data;
the construction unit is used for constructing a plurality of first triangles corresponding to the first corner points;
the matching calculation unit is used for performing matching calculation on the first triangle and the second triangle to obtain a rotation matrix and a translation vector;
and the conversion unit is used for converting the rotation matrix and the translation vector to obtain first measurement data.
In some embodiments, the corner point acquiring unit comprises:
a near point acquiring subunit, configured to acquire a plurality of near points of the two-dimensional contour data;
the side connection subunit is used for connecting the plurality of adjacent points with the two-dimensional contour data to obtain a plurality of sides;
the included angle obtaining subunit is used for obtaining an included angle formed between every two edges from the multiple edges, and the included angle comprises an angle value;
and the angular point acquisition subunit is used for taking the two-dimensional profile data corresponding to the included angle which accords with the preset angle value as the first angular point.
In some embodiments, the matching calculation unit comprises:
the similarity subunit is used for acquiring a plurality of similar triangles matched with the first triangle from the second triangle;
the attitude calculation subunit is used for calculating an initial rotation matrix and an initial translation vector of coincidence of corresponding points between every two similar triangles;
a registration error obtaining subunit, configured to obtain a plurality of registration errors according to the initial rotation matrix and the initial translation vector;
and the posture determining subunit is used for determining the minimum registration error from the plurality of registration errors and taking the initial rotation matrix and the initial translation vector corresponding to the minimum registration error as the final rotation matrix and translation vector.
With regard to the steel structure deformation detecting apparatus in the above-mentioned embodiment, the specific manner in which each module performs the operation has been described in detail in the embodiment related to the method, and will not be elaborated herein.
In order to solve the technical problem, an embodiment of the present application further provides a computer device. Referring to fig. 8, fig. 8 is a block diagram of a basic structure of a computer device according to the present embodiment.
The computer device 8 comprises a memory 81, a processor 82 and a network interface 83 which are communicatively connected to each other via a system bus. It is noted that only computer device 8 having components 81-83 is shown, but it is understood that not all of the shown components are required to be implemented, and that more or fewer components may be implemented instead. As will be understood by those skilled in the art, the computer device is a device capable of automatically performing numerical calculation and/or information processing according to a preset or stored instruction, and the hardware includes, but is not limited to, a microprocessor, an Application Specific Integrated Circuit (ASIC), a Programmable Gate Array (FPGA), a Digital Signal Processor (DSP), an embedded device, and the like.
The computer device can be a desktop computer, a notebook, a palm computer, a cloud server and other computing devices. The computer equipment can carry out man-machine interaction with a user through a keyboard, a mouse, a remote controller, a touch panel or voice control equipment and the like.
The memory 81 includes at least one type of readable storage medium including a flash memory, a hard disk, a multimedia card, a card-type memory (e.g., an SD or D steel structure deformation sensing memory, etc.), a Random Access Memory (RAM), a Static Random Access Memory (SRAM), a Read Only Memory (ROM), an Electrically Erasable Programmable Read Only Memory (EEPROM), a Programmable Read Only Memory (PROM), a magnetic memory, a magnetic disk, an optical disk, etc. In some embodiments, the storage 81 may be an internal storage unit of the computer device 8, such as a hard disk or a memory of the computer device 8. In other embodiments, the memory 81 may also be an external storage device of the computer device 8, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like, which are provided on the computer device 8. Of course, the memory 81 may also comprise both an internal storage unit of the computer device 8 and an external storage device thereof. In this embodiment, the memory 81 is generally used for storing an operating system installed in the computer device 8 and various application software, such as a program code of a steel structure deformation detection method. Further, the memory 81 may also be used to temporarily store various types of data that have been output or are to be output.
The processor 82 may be a Central Processing Unit (CPU), controller, microcontroller, microprocessor, or other data Processing chip in some embodiments. The processor 82 is typically used to control the overall operation of the computer device 8. In this embodiment, the processor 82 is configured to run a program code stored in the memory 81 or process data, for example, a program code for running the steel structure deformation detection method.
The network interface 83 may comprise a wireless network interface or a wired network interface, and the network interface 83 is generally used for establishing communication connections between the computer device 8 and other electronic devices.
The present application further provides another embodiment, which is to provide a computer-readable storage medium storing a steel structure deformation detection program, where the steel structure deformation detection program is executable by at least one processor to cause the at least one processor to execute the steps of the steel structure deformation detection method as described above.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solutions of the present application may be embodied in the form of a software product, which is stored in a storage medium (such as ROM/RAM, magnetic disk, optical disk) and includes instructions for enabling a terminal device (such as a mobile phone, a computer, a server, an air conditioner, or a network device) to execute the method according to the embodiments of the present application.
It is to be understood that the above-described embodiments are merely illustrative of some, but not restrictive, of the broad invention, and that the appended drawings illustrate preferred embodiments of the invention and do not limit the scope of the invention. This application is capable of embodiments in many different forms and is provided for the purpose of enabling a thorough understanding of the disclosure of the application. Although the present application has been described in detail with reference to the foregoing embodiments, it will be apparent to one skilled in the art that the present application may be practiced without modification or with equivalents of some of the features described in the foregoing embodiments. All equivalent structures made by using the contents of the specification and the drawings of the present application are directly or indirectly applied to other related technical fields, and all the equivalent structures are within the protection scope of the present application.
Claims (10)
1. A steel structure deformation detection method is characterized by comprising the following steps:
acquiring three-dimensional point cloud data of a steel structure;
extracting two-dimensional contour data of the steel structure according to the three-dimensional point cloud data;
carrying out coarse registration on the two-dimensional contour data and a preset steel structure contour template to obtain first measurement data;
performing fine registration on the first measurement data according to an iterative closest point algorithm to obtain a second measurement quantity;
and calculating the minimum distance error from the second measurement data to the steel structure contour template so as to determine the deformation amount of the steel structure.
2. The steel structure deformation detection method according to claim 1, wherein the extracting two-dimensional profile data of the steel structure from the three-dimensional point cloud data comprises:
determining the extension direction of the steel structure according to the three-dimensional point cloud data of the steel structure;
determining bounding box data of the steel structure from the three-dimensional point cloud data of the steel structure;
extracting two-dimensional profile data of the steel structure from the bounding box data according to the extension direction.
3. The method for detecting deformation of a steel structure according to claim 2, wherein the determining the extending direction of the steel structure according to the three-dimensional point cloud data of the steel structure comprises:
determining centroid data of the three-dimensional point cloud data from the three-dimensional point cloud data of the steel structure;
constructing a covariance matrix according to the three-dimensional point cloud data and the centroid data;
and determining the extension direction of the steel structure according to the covariance matrix.
4. The steel structure deformation detection method according to claim 2, wherein the extracting of the two-dimensional profile data of the steel structure from the bounding box data according to the extending direction includes:
determining a cross section corresponding to the bounding box data according to the extension direction and the centroid data;
according to the distance from the bounding box data to the cross section, the bounding box data corresponding to the distance meeting a preset distance interval is used as section data;
projecting the cross-sectional data onto the cross-section to obtain two-dimensional profile data.
5. The steel structure deformation detection method according to claim 1, wherein the steel structure contour template comprises a plurality of second angular points and a plurality of second triangles constructed according to the second angular points, and the coarse registration of the two-dimensional contour data with a preset steel structure contour template to obtain first measurement data comprises:
acquiring a first corner of the two-dimensional contour data;
constructing a plurality of first triangles corresponding to the first corner points;
performing matching calculation on the first triangle and the second triangle to obtain a rotation matrix and a translation vector;
and converting the rotation matrix and the translation vector to obtain first measurement data.
6. The steel structure deformation detection method according to claim 5, wherein the acquiring the first corner point of the two-dimensional profile data comprises:
acquiring a plurality of adjacent points of the two-dimensional profile data;
connecting the plurality of adjacent points with the two-dimensional contour data to obtain a plurality of edges;
obtaining an included angle formed between every two edges from the plurality of edges, wherein the included angle comprises an angle value;
and taking the two-dimensional profile data corresponding to the included angle which accords with the preset angle value as a first angular point.
7. The method for detecting deformation of a steel structure according to claim 5, wherein the matching calculation of the first triangle and the second triangle to obtain a rotation matrix and a translation vector comprises:
obtaining a plurality of similar triangles matched with the first triangle from the second triangle;
calculating an initial rotation matrix and an initial translation vector of coincidence of corresponding points between every two similar triangles;
acquiring a plurality of registration errors according to the initial rotation matrix and the initial translation vector;
and determining the minimum registration error from the plurality of registration errors, and taking the initial rotation matrix and the initial translation vector corresponding to the minimum registration error as the final rotation matrix and translation vector.
8. The utility model provides a steel construction deformation detection device which characterized in that, steel construction deformation detection device includes:
the acquisition module is used for acquiring three-dimensional point cloud data of the steel structure;
the extraction module is used for extracting the two-dimensional contour data of the steel structure according to the three-dimensional point cloud data;
the rough registration module is used for carrying out rough registration on the two-dimensional contour data and a preset steel structure contour template to obtain first measurement data;
the fine registration module is used for performing fine registration on the first measurement data according to an iterative closest point algorithm to obtain a second measurement quantity;
and the calculation module is used for calculating the minimum distance error from the second measurement data to the steel structure contour template so as to determine the deformation amount of the steel structure.
9. A computer device comprising a memory in which a computer program is stored and a processor that implements the steps of the steel structure deformation detection method according to any one of claims 1 to 7 when the computer program is executed.
10. A computer-readable storage medium, characterized in that the computer-readable storage medium has stored thereon a computer program which, when being executed by a processor, realizes the steps of the steel structure deformation detection method according to any one of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210491706.7A CN114841965B (en) | 2022-04-30 | 2022-04-30 | Steel structure deformation detection method and device, computer equipment and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210491706.7A CN114841965B (en) | 2022-04-30 | 2022-04-30 | Steel structure deformation detection method and device, computer equipment and storage medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114841965A true CN114841965A (en) | 2022-08-02 |
| CN114841965B CN114841965B (en) | 2023-08-01 |
Family
ID=82567011
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210491706.7A Active CN114841965B (en) | 2022-04-30 | 2022-04-30 | Steel structure deformation detection method and device, computer equipment and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114841965B (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| GB2616322A (en) * | 2022-03-03 | 2023-09-06 | Univ Hefei Technology | Computer vision-based dynamic bridge shape recognition method |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102564347A (en) * | 2011-12-30 | 2012-07-11 | 中国科学院上海光学精密机械研究所 | Object three-dimensional outline measuring device and method based on Dammann grating |
| CN105852971A (en) * | 2016-05-04 | 2016-08-17 | 苏州点合医疗科技有限公司 | Registration navigation method based on skeleton three-dimensional point cloud |
| CN106022210A (en) * | 2016-05-04 | 2016-10-12 | 成都指码科技有限公司 | Vein profile three-dimensional point cloud matching identity identifying method and device |
| CN106600690A (en) * | 2016-12-30 | 2017-04-26 | 厦门理工学院 | Complex building three-dimensional modeling method based on point cloud data |
| CN112669359A (en) * | 2021-01-14 | 2021-04-16 | 武汉理工大学 | Three-dimensional point cloud registration method, device, equipment and storage medium |
| CN112902874A (en) * | 2021-01-19 | 2021-06-04 | 中国汽车工程研究院股份有限公司 | Image acquisition device and method, image processing method and device and image processing system |
| WO2021232463A1 (en) * | 2020-05-19 | 2021-11-25 | 北京数字绿土科技有限公司 | Multi-source mobile measurement point cloud data air-ground integrated fusion method and storage medium |
| CN114332212A (en) * | 2022-03-11 | 2022-04-12 | 中国铁路设计集团有限公司 | Track superelevation and front-back height detection method based on vehicle-mounted mobile laser point cloud |
-
2022
- 2022-04-30 CN CN202210491706.7A patent/CN114841965B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102564347A (en) * | 2011-12-30 | 2012-07-11 | 中国科学院上海光学精密机械研究所 | Object three-dimensional outline measuring device and method based on Dammann grating |
| CN105852971A (en) * | 2016-05-04 | 2016-08-17 | 苏州点合医疗科技有限公司 | Registration navigation method based on skeleton three-dimensional point cloud |
| CN106022210A (en) * | 2016-05-04 | 2016-10-12 | 成都指码科技有限公司 | Vein profile three-dimensional point cloud matching identity identifying method and device |
| CN106600690A (en) * | 2016-12-30 | 2017-04-26 | 厦门理工学院 | Complex building three-dimensional modeling method based on point cloud data |
| WO2021232463A1 (en) * | 2020-05-19 | 2021-11-25 | 北京数字绿土科技有限公司 | Multi-source mobile measurement point cloud data air-ground integrated fusion method and storage medium |
| CN112669359A (en) * | 2021-01-14 | 2021-04-16 | 武汉理工大学 | Three-dimensional point cloud registration method, device, equipment and storage medium |
| CN112902874A (en) * | 2021-01-19 | 2021-06-04 | 中国汽车工程研究院股份有限公司 | Image acquisition device and method, image processing method and device and image processing system |
| CN114332212A (en) * | 2022-03-11 | 2022-04-12 | 中国铁路设计集团有限公司 | Track superelevation and front-back height detection method based on vehicle-mounted mobile laser point cloud |
Non-Patent Citations (3)
| Title |
|---|
| 尹吉林;谢杰;: "一种改进的曲面配准方法的研究", no. 03, pages 1 - 2 * |
| 赵夫群;耿国华;: "基于图像特征和奇异值分解的点云配准算法", no. 10 * |
| 黄方;宁涛;陈志同;沈云超;: "二维点轮廓与矢量轮廓配准研究", no. 05, pages 598 - 606 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| GB2616322A (en) * | 2022-03-03 | 2023-09-06 | Univ Hefei Technology | Computer vision-based dynamic bridge shape recognition method |
| GB2616322B (en) * | 2022-03-03 | 2024-02-21 | Univ Hefei Technology | Computer vision-based dynamic bridge shape recognition method |
Also Published As
| Publication number | Publication date |
|---|---|
| CN114841965B (en) | 2023-08-01 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108764048B (en) | Face key point detection method and device | |
| CN111508021B (en) | Pose determining method and device, storage medium and electronic equipment | |
| JP4686762B2 (en) | Three-dimensional shape alignment method and program | |
| JP6114052B2 (en) | Point cloud analysis processing device and point cloud analysis processing program | |
| US20110304619A1 (en) | Primitive quadric surface extraction from unorganized point cloud data | |
| CN111459269B (en) | Augmented reality display method, system and computer readable storage medium | |
| CN110599582B (en) | Rapid algorithm of rectangular object bounding box | |
| JP5870011B2 (en) | Point cloud analysis device, point cloud analysis method, and point cloud analysis program | |
| CN110704652A (en) | Vehicle image fine-grained retrieval method and device based on multiple attention mechanism | |
| CN101359342A (en) | Search method for three-dimensional model of mixing characteristic based on feedback | |
| CN114863060A (en) | Point cloud-based three-dimensional model reconstruction method, device, equipment and storage medium | |
| Yoshimura et al. | Automatic registration of MLS point clouds and SfM meshes of urban area | |
| CN114841965A (en) | Steel structure deformation detection method and device, computer equipment and storage medium | |
| Lu et al. | Three-dimensional object recognition using an extensible local surface descriptor | |
| CN115031635A (en) | Measuring method and device, electronic device and storage medium | |
| CN111368915A (en) | Drawing verification method, device, equipment and storage medium | |
| CN116798027A (en) | Three-dimensional point cloud real-time cloud matching method and device based on multi-scale feature extraction | |
| CN201576308U (en) | Registration system for complex curved surface | |
| CN114266876B (en) | Positioning method, visual map generation method and device | |
| CN115496860A (en) | Flexible rule model construction method and device, terminal equipment and storage medium | |
| CN114910892A (en) | Laser radar calibration method and device, electronic equipment and storage medium | |
| Robinson et al. | Pattern design for 3D point matching | |
| CN115655128A (en) | Steel structure deformation positioning method and device, computer equipment and storage medium | |
| CN112100900B (en) | Space non-cooperative target point cloud initial attitude measurement method | |
| Zhao et al. | Accurately and robustly estimating three-dimensional rigid transformation using an effective local reference frame |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |