CN117197410B - Virtual splicing method, device and equipment for steel structure and storage medium - Google Patents
Virtual splicing method, device and equipment for steel structure and storage medium Download PDFInfo
- Publication number
- CN117197410B CN117197410B CN202311461167.3A CN202311461167A CN117197410B CN 117197410 B CN117197410 B CN 117197410B CN 202311461167 A CN202311461167 A CN 202311461167A CN 117197410 B CN117197410 B CN 117197410B
- Authority
- CN
- China
- Prior art keywords
- plane
- point cloud
- equations
- spliced
- target
- 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.)
- Active
Links
- 229910000831 Steel Inorganic materials 0.000 title claims abstract description 96
- 239000010959 steel Substances 0.000 title claims abstract description 96
- 238000000034 method Methods 0.000 title claims abstract description 77
- 238000003860 storage Methods 0.000 title claims abstract description 15
- 210000001503 joint Anatomy 0.000 claims abstract description 7
- 238000004422 calculation algorithm Methods 0.000 claims description 35
- 238000000605 extraction Methods 0.000 claims description 26
- 238000012545 processing Methods 0.000 claims description 26
- 239000011159 matrix material Substances 0.000 claims description 25
- 238000004590 computer program Methods 0.000 claims description 21
- 230000011218 segmentation Effects 0.000 claims description 21
- 238000005070 sampling Methods 0.000 claims description 12
- 238000012216 screening Methods 0.000 claims description 12
- 230000008569 process Effects 0.000 description 24
- 238000004519 manufacturing process Methods 0.000 description 10
- 230000006870 function Effects 0.000 description 7
- 238000010586 diagram Methods 0.000 description 5
- 238000010276 construction Methods 0.000 description 4
- 230000008878 coupling Effects 0.000 description 3
- 238000010168 coupling process Methods 0.000 description 3
- 238000005859 coupling reaction Methods 0.000 description 3
- 239000000463 material Substances 0.000 description 3
- 238000010845 search algorithm Methods 0.000 description 3
- 238000004364 calculation method Methods 0.000 description 2
- 238000004891 communication Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 238000009434 installation Methods 0.000 description 2
- 238000013178 mathematical model Methods 0.000 description 2
- 241000228740 Procrustes Species 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 238000003491 array Methods 0.000 description 1
- 230000003416 augmentation Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000009826 distribution Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000009776 industrial production Methods 0.000 description 1
- 238000007689 inspection Methods 0.000 description 1
- 238000003754 machining Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Classifications
-
- 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
Landscapes
- Image Analysis (AREA)
- Length Measuring Devices With Unspecified Measuring Means (AREA)
Abstract
The invention provides a virtual assembly method, a device, equipment and a storage medium of a steel structure, wherein the method comprises the following steps: acquiring point cloud data sets of all steel structures to be spliced; extracting coordinate information of all pose feature points from the point cloud data set, wherein the pose feature points are endpoints of all sections to be spliced of all the steel structures to be spliced; pre-splicing each spliced section based on coordinate information of endpoints of all sections to be spliced; extracting coordinate information of all splicing characteristic points from the point cloud data set, wherein the splicing characteristic points are hole center coordinates of bolt holes on the side surfaces of the end parts of the steel structures to be spliced, and a plurality of butt joint bolt holes are formed in the side surfaces of the end parts of each steel structure to be spliced; and carrying out structural assembly on all the steel structures to be assembled based on the coordinate information of all the assembly characteristic points. The invention can improve the assembly precision.
Description
Technical Field
The present invention relates to the field of virtual assembly technologies, and in particular, to a virtual assembly method, apparatus, device, and storage medium for a steel structure.
Background
Steel structures are typically assembled by manufacturing the components at the factory and transporting them to the construction site. To ensure accurate installation of the components in place after shipment to the site, physical preassembly is typically required at the factory to verify the assemblability of the structure. The factory entity pre-assembly is to carry out the whole or segmented layered temporary assembly operation process before leaving the factory by using steel members such as large-scale columns, beams, trusses, supports and the like manufactured in a segmented mode and a multi-layer steel frame structure, particularly a large-scale steel structure connected by high-strength bolts, a steel shell structure manufactured in a segmented mode and supplied with goods and the like.
At present, in the pre-assembly process of the steel structure, the manual pre-assembly method is widely applied. The manufactured steel segments are placed in a ground sample line drawn according to theoretical data by using hoisting equipment, and workers bolt the steel box segments with the matching relationship.
However, manual pre-assembly not only requires great manpower, material resources and time, but also requires a large enough site. At present, although a method for virtually splicing point cloud data is adopted, the splicing precision is low, and the requirement of high splicing precision cannot be met.
Disclosure of Invention
The embodiment of the invention provides a virtual assembly method, device and equipment of a steel structure and a storage medium, which are used for solving the problem of low accuracy of the existing virtual assembly.
In a first aspect, an embodiment of the present invention provides a virtual assembly method for a steel structure, including:
acquiring point cloud data sets of all steel structures to be spliced;
extracting coordinate information of all pose feature points from the point cloud data set, wherein the pose feature points are endpoints of all sections to be spliced of all the steel structures to be spliced;
pre-splicing each spliced section based on coordinate information of endpoints of all sections to be spliced;
extracting coordinate information of all splicing characteristic points from the point cloud data set, wherein the splicing characteristic points are hole center coordinates of bolt holes on the side surfaces of the end parts of the steel structures to be spliced, and a plurality of butt joint bolt holes are formed in the side surfaces of the end parts of each steel structure to be spliced;
and carrying out structural assembly on all the steel structures to be assembled based on the coordinate information of all the assembly characteristic points.
In one possible implementation, extracting coordinate information of all pose feature points from the point cloud dataset includes:
dividing and screening the point cloud data sets based on an European clustering algorithm to screen out the end point cloud data sets of each section to be spliced;
Performing plane segmentation processing on the endpoint cloud data sets of each section to be spliced based on a region growing method to determine point cloud clusters positioned on different target planes, wherein the target planes are any one plane;
fitting the endpoint cloud clusters of all different target planes based on a random sampling consistency algorithm to obtain a plane equation of each target plane;
and determining coordinate information of all pose feature points based on plane equations of all target planes.
In one possible implementation, determining coordinate information of all pose feature points based on plane equations of all target planes includes:
all the plane equations are arranged and combined to form a plane equation set, wherein the plane equation set comprises a plurality of sets of equations, each set of equations comprises 3 plane equations, and each set of equations is different;
screening an intersecting plane set from the plane set based on a rank and a preset value of a non-zero row of a coefficient matrix formed by a target set of equations, wherein the target set of equations is any one set of equations in the plane set of equations;
and solving each set of equations in the intersecting plane set, and determining coordinate information of all pose feature points.
In one possible implementation, extracting coordinate information of all the assembled feature points from the point cloud dataset includes:
projecting the point cloud cluster of the target plane to a plane equation corresponding to the target plane to obtain a projected point cloud cluster of the target plane;
carrying out extraction fitting on the projection point cloud cluster of the target plane, and determining round boundary point cloud data of the bolt hole on the target plane;
fitting the circular boundary point cloud data of the bolt holes on all the target planes to obtain the hole center coordinates of all the bolt holes.
In one possible implementation manner, the method for extracting and fitting the projection point cloud cluster of the target plane to determine the round boundary point cloud data of the bolt hole on the target plane includes:
and (3) extracting and fitting the projection point cloud cluster of the target plane based on an Alpha shape algorithm, and determining the round boundary point cloud data of the bolt hole on the target plane.
In one possible implementation manner, fitting is performed on the circular boundary point cloud data of the bolt holes on all the target planes to obtain hole center coordinates of all the bolt holes, including:
and carrying out fitting processing on the round boundary point cloud data of the bolt holes on all target planes based on a random sampling consistency algorithm to obtain the hole center coordinates of all the bolt holes.
In one possible implementation manner, the steel structure to be assembled is structurally assembled based on coordinate information of all the assembled feature points, including:
constructing a difference matrix based on the difference value of the hole center coordinates of each group of corresponding bolt holes on the extracted two steel structures to be spliced in the XYZ direction;
calculating the minimum value of Frobenius norms of a difference matrix, and determining the moving distance of hole center coordinates of two correspondingly connected bolt holes in the XYZ direction;
based on the moving distance, the steel structure to be assembled is structurally assembled.
In a second aspect, an embodiment of the present invention provides a virtual assembly device of a steel structure, including:
the data acquisition module is used for acquiring point cloud data sets of all the steel structures to be assembled;
the pose information extraction module is used for extracting coordinate information of all pose feature points from the point cloud data set, wherein the pose feature points are endpoints of all to-be-spliced sections of all to-be-spliced steel structures;
the pre-splicing module is used for pre-splicing each spliced section based on the coordinate information of the endpoints of all the sections to be spliced;
the assembly information extraction module is used for extracting coordinate information of all assembly characteristic points from the point cloud data set, wherein the assembly characteristic points are hole center coordinates of bolt holes on the side surfaces of the end parts of the steel structures to be assembled, and a plurality of butt joint bolt holes are formed in the side surfaces of the end parts of each steel structure to be assembled;
And the assembly module is used for structurally assembling all the steel structures to be assembled based on the coordinate information of all the assembly characteristic points.
In a possible implementation manner, the pose information extraction module is used for carrying out segmentation screening processing on the point cloud data sets based on an European clustering algorithm so as to screen out endpoint cloud data sets of all sections to be spliced;
performing plane segmentation processing on the endpoint cloud data sets of each section to be spliced based on a region growing method to determine point cloud clusters positioned on different target planes, wherein the target planes are any one plane;
fitting the endpoint cloud clusters of all different target planes based on a random sampling consistency algorithm to obtain a plane equation of each target plane;
and determining coordinate information of all pose feature points based on plane equations of all target planes.
In one possible implementation manner, the pose information extraction module is configured to arrange and combine all plane equations to form a plane equation set, where the plane equation set includes a plurality of sets of equations, each set of equations includes 3 plane equations, and each set of equations is different;
screening an intersecting plane set from the plane set based on a rank and a preset value of a non-zero row of a coefficient matrix formed by a target set of equations, wherein the target set of equations is any one set of equations in the plane set of equations;
And solving each set of equations in the intersecting plane set, and determining coordinate information of all pose feature points.
In one possible implementation manner, the assembly information extraction module is used for projecting the point cloud cluster of the target plane to a plane equation corresponding to the target plane to obtain a projected point cloud cluster of the target plane;
carrying out extraction fitting on the projection point cloud cluster of the target plane, and determining round boundary point cloud data of the bolt hole on the target plane;
fitting the circular boundary point cloud data of the bolt holes on all the target planes to obtain the hole center coordinates of all the bolt holes.
In one possible implementation manner, the assembly information extraction module is used for extracting and fitting the projection point cloud cluster of the target plane based on an Alpha shapes algorithm, and determining the round boundary point cloud data of the bolt hole on the target plane.
In one possible implementation manner, the assembly information extraction module is configured to perform fitting processing on the circular boundary point cloud data of the bolt holes on all the target planes based on a random sampling consistency algorithm, so as to obtain hole center coordinates of all the bolt holes.
In one possible implementation manner, the assembling module is used for constructing a difference matrix based on the difference value of the hole center coordinates of each group of corresponding bolt holes on the extracted two steel structures to be assembled in the XYZ direction;
Calculating the minimum value of Frobenius norms of a difference matrix, and determining the moving distance of hole center coordinates of two correspondingly connected bolt holes in the XYZ direction;
based on the moving distance, the steel structure to be assembled is structurally assembled.
In a third aspect, an embodiment of the present invention provides an electronic device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, the processor implementing the steps of the method according to the first aspect or any one of the possible implementations of the first aspect, when the computer program is executed by the processor.
In a fourth aspect, embodiments of the present invention provide a computer readable storage medium storing a computer program which, when executed by a processor, implements the steps of the method as described above in the first aspect or any one of the possible implementations of the first aspect.
The embodiment of the invention provides a virtual assembly method, a virtual assembly device, virtual assembly equipment and a virtual assembly storage medium for a steel structure. And then, pre-splicing the sections based on the coordinate information of the endpoints of all the sections to be spliced. Next, coordinate information of all the assembled feature points is extracted from the point cloud data set. And finally, carrying out structural assembly on all the steel structures to be assembled based on the coordinate information of all the assembly characteristic points. By considering pose characteristic points and splicing characteristic points, the splicing process can be closer to engineering practice, and even if manufacturing errors exist in a steel structure, accurate splicing can be realized based on the splicing characteristic points.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the embodiments or the description of the prior art will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of an implementation of a virtual assembly method of a steel structure provided by an embodiment of the invention;
fig. 2 is a schematic diagram of pose feature points of a section to be spliced provided by an embodiment of the invention;
fig. 3 is a flowchart of an implementation of a nearest neighbor search algorithm of KD-Tree provided by an embodiment of the present invention;
FIG. 4 is a flowchart of an implementation of the region growing method according to an embodiment of the present invention;
FIG. 5 is a flow chart of an implementation of the determination process of the plane equation provided by an embodiment of the present invention;
FIG. 6 is a flowchart of an implementation of solving coordinate information of all pose feature points according to an embodiment of the present invention;
FIG. 7 is a schematic diagram of a structure for solving the coordinates of the hole center of a bolt hole according to an embodiment of the present invention;
fig. 8 is a schematic structural diagram of a virtual assembly device of a steel structure according to an embodiment of the present invention;
Fig. 9 is a schematic diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth such as the particular system architecture, techniques, etc., in order to provide a thorough understanding of the embodiments of the present invention. It will be apparent, however, to one skilled in the art that the present invention may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present invention with unnecessary detail.
For the purpose of making the objects, technical solutions and advantages of the present invention more apparent, the following description will be made by way of specific embodiments with reference to the accompanying drawings.
As described in the background art, the steel structure has the advantages of good stress performance, wide material sources, mature industrial production mode, convenient transportation, high installation efficiency and the like, and is widely applied to the bridge construction field in China. In order to ensure the processing and production quality of the steel structure, the production unit also needs to be preassembled after completing some necessary quality inspection work. However, currently, the machining quality of steel structures is mainly detected by manual pre-assembly. However, manual pre-assembly requires a lot of manpower, material resources, financial resources, time and space. At present, although point cloud virtual assembly is also available, the accuracy of the existing virtual assembly is poor, the requirement of high-precision assembly can not be met,
In order to solve the problems in the prior art, the embodiment of the invention provides a virtual assembly method, a virtual assembly device, virtual assembly equipment and a storage medium of a steel structure. The virtual assembly method of the steel structure provided by the embodiment of the invention is first described below.
Referring to fig. 1, a flowchart of an implementation of a virtual assembly method of a steel structure provided by an embodiment of the present invention is shown, and details are as follows:
and step S110, acquiring point cloud data sets of all the steel structures to be assembled.
And acquiring point cloud data of each steel structure to be assembled by using a 3D laser scanner to form point cloud data sets of all the steel structures to be assembled. And generating a txt file format from the point cloud data set, and performing data processing through a PCL point cloud library.
And step S120, extracting coordinate information of all pose feature points from the point cloud data set.
The pose characteristic points are the end points of each section to be assembled of all the steel structures to be assembled, are used for considering the characteristic points of the geometric shape information of the sections to be assembled, and are used for virtual pre-assembly of the large steel structure. The pose feature points are used for considering the space pose of the large-scale steel structure, if the corresponding relation among the point cloud data for assembly is not considered, the situation that the assembled structure is upside down or front and back is likely to occur after the assembly is completed, so that the structure can be pre-assembled based on the end points of each section to be assembled, and the accuracy of the structure in the space pose is ensured.
In general, the assembled section of the large steel structure is generally rectangular, and all pose characteristic points such as the points A, B, C and D in fig. 2 can be extracted from the rectangle.
In some embodiments, because of the huge data volume of the point cloud data sets scanned by the large-scale steel structure, if the point cloud data sets are directly used for virtual assembly, great time consumption is caused for subsequent point cloud processing and calculation processes. In order to facilitate the subsequent processing, only the coordinate information of all the pose feature points may be extracted. The specific extraction process is as follows:
and step 1201, performing segmentation screening processing on the point cloud data sets based on an European clustering algorithm to screen out the endpoint cloud data sets of each section to be spliced.
Because the steel structure to be assembled has a plurality of end face data, the workload of an algorithm is increased if the plurality of end face data are processed simultaneously, point clouds on different end faces can be mutually influenced, and the difficulty of extracting the coordinate information of pose feature points is increased. Therefore, the European clustering method can be adopted to divide the point cloud data of different end faces into different point cloud clusters. Thereby improving the accuracy of extraction. Because different ends generally have relatively larger Euclidean distance, the point cloud data on the same plane can be separated by clustering the point cloud data, so that interference among planes is reduced, and errors are reduced. The endpoint cloud data sets of the sections to be assembled are screened through a clustering method, so that the processing speed of an algorithm can be improved, the accuracy of plane extraction is improved, and the processing condition of a plurality of end data can be better dealt with.
In order to meet the real-time performance of the three-dimensional laser point cloud processing algorithm, a topological relation is established among discrete point clouds, so that the quick search of points or clusters in the field is realized.
The application adopts a nearest neighbor search algorithm based on KD-Tree, which is a multidimensional data structure for dividing a space into a plurality of disjoint subspaces. The KD-Tree can effectively organize and store multidimensional data and realize quick search. In the algorithm, for a certain point P in space, k points nearest to P are found through a KD-Tree neighbor search algorithm, and the points are clustered into a set Q, wherein points with a distance less than a set threshold value are clustered together. If the number of elements in the set Q is not increased any more, ending the whole clustering process; otherwise, a point other than P needs to be selected in the set Q, and the process is repeated until the number of elements in the set Q is not increased any more. The specific workflow is shown in fig. 3.
In the clustering process, three parameters mainly need to be set, the first is a proper clustering search radius, and the search radius is used for defining the range of a point for searching the adjacent points in the clustering process, namely, searching the adjacent points with the distance within a specified radius range in a spherical area with each point as the center. The larger the search radius, the wider the search range of neighboring points for each point, and the more distant points may be included, so that points that would otherwise belong to different clusters may be merged into the same cluster, resulting in excessive merging of clusters. The smaller the search radius, the narrower the neighboring point search range for each point, possibly resulting in the point that would otherwise belong to the same cluster being divided into clusters. And secondly, the minimum value a and the maximum value b of the number of points in each point cloud cluster are used as effective clusters to be clustered only when the number of samples in the clusters is within a threshold value range in the clustering process. The reasonable threshold setting can achieve the effects of removing point cloud noise and improving clustering efficiency. The setting of the three parameters should be adjusted according to the information of the point cloud original data. The specific adjustment process is as follows:
First, a point P is found in space, and the nearest n points are found by KD-Tree. Next, points with a distance less than the distance threshold r are placed in Q and removed from the source point cloud. Then, step 1 is repeated with the other points in Q until the points in Q no longer increase. And finally, when the quantity of point clouds a is smaller than n and smaller than b in the point cloud cluster Q, saving the point cloud cluster, otherwise deleting the point cloud cluster.
And step 1202, carrying out plane segmentation processing on the endpoint cloud data sets of each section to be spliced based on a region growing method so as to determine the point cloud clusters positioned on different target planes.
The target plane is any one plane.
In order to obtain the point cloud clusters on the same plane, a point cloud data set can be segmented by adopting a region growing method. Starting from the selected initial seed point, surrounding point clouds are continuously merged according to a given growth criterion, so that a segmentation area is enlarged, and the segmentation of the point clouds is realized. The region growth has certain robustness to noise and local shape change of the point cloud, and can be accurately segmented in a complex point cloud environment. The point cloud data may be processed using information such as geometric feature method, curvature, and vector as judgment conditions. Each set of region-growing cluster-splitting plane outputs is considered to be part of the same smooth surface.
The selection of seed points and the growth criteria are two key factors affecting the segmentation quality. To ensure the segmentation effect, the region growing method generally starts growing from the point of minimum curvature, so that it can start growing from the smoother region to improve efficiency and reduce the total number of segmented regions. In the region growing process, through judging the similarity degree between adjacent points, the more similar points are merged into the current region, so that the point cloud segmentation is realized. The detailed growth process of the region growing method will be described in detail as follows:
first, a data point meeting the condition is selected as a seed point. Then, a smoothness constraint threshold is specified, a seed point is used as a growth starting point, and points which meet the constraint nearby are combined into the same cluster based on the comparison of angles between points and normals. The new growth units are then continued to merge and grow until all data points have been processed and the final segmentation results are obtained. And finally, taking each extracted plane as a clustering condition by a plane clustering algorithm based on region growth, and completing the segmentation of all planes.
Referring to fig. 4, first, curvature values of an input point cloud are calculated, sorting is performed according to the curvature values, and a point with the smallest curvature is used as an initial seed point. Growth begins from the region where the initial seed point is located. Next, an empty cluster region C, a seed point sequence Q, and a cluster array L are set. Then, the initial seed points are added to the Q-sequence and the angle between the normal of each neighborhood point of the seed point and the normal of the seed point is traversed. If the included angle is smaller than the set threshold, the neighborhood point is added to the C sequence. At the same time, it is checked whether the curvature of the neighborhood point is smaller than a threshold value, and if so, it is added to the Q sequence. After the neighborhood point traversal is completed, the current seed point is deleted and the seed point is reselected in the Q sequence. The above steps are repeated until the Q sequence is empty and the growth of one region is completed. It is added to the L sequence. And secondly, sorting the point clouds from large to small according to the curvature value, and repeatedly executing the previous steps to finish clustering segmentation. And finally, setting a plane point cloud area threshold, calculating the distance from the point cloud class smaller than the threshold to each plane, and classifying the point cloud class smaller than the distance threshold as the current plane point cloud.
Step S1203, fitting the endpoint cloud clusters of all different target planes based on a random sampling consistency algorithm to obtain a plane equation of each target plane.
The random sampling consistency algorithm (Random Sample Consensus, RANSAC) is a method for estimating mathematical model parameters in a dataset containing various imperfections such as noise, outliers, etc. The basic idea of the algorithm is to randomly sample from a sample dataset to derive valid data from which mathematical model parameters are then estimated. Because of the uncertainty of the algorithm, the algorithm needs to be iterated for many times to improve the probability to obtain reasonable results. As the most classical model fitting segmentation algorithm, the method firstly samples point cloud data, calculates model parameters of each group of sampling sets, then counts the distance from each point to a model, and takes the point with the distance smaller than a set threshold value as an inner point. And (3) sampling and counting for one round, wherein the extraction result takes a model with the largest number of interior points, and the interior points are the segmentation results corresponding to the extraction model. And finally, outputting a fitting result of the point cloud through multiple rounds of circulation. Inputs to RANSAC are observed data, a parametric model and some confidence parameters, it being noted that the parametric model can be interpreted or adapted to local points.
At least 3 points are needed when the plane is fitted, 3 points are randomly selected, and then the plane model parameters are solved and calculated according to a plane equation.
The plane equation is set as follows:。
the estimated plane model is checked with the points in one plane except for these 3 points, the resulting error is calculated, and the error is compared with a set threshold. If the error is less than the threshold, the point is determined to be an interior point on the plane and the number of interior points under the model is counted and recorded. And then repeating the steps, continuously updating the model parameters, and storing the model parameters with the largest number of interior points. And (3) the iteration process is carried out until the set iteration threshold is reached, and the model parameter with the largest number of inner points is obtained. And finally, estimating the model parameters by using the inner points to obtain final model parameters. As shown in fig. 5, the specific operation steps are as follows:
firstly, three point clouds are randomly selected from the point cloud clusters of the acquired target plane, and a plane is formed by the three point clouds. Then, the distances from all other points to the plane are calculated, and if the distances are smaller than the threshold T, the points are considered to be on the same plane with the plane. Then, if the number of points on the same plane exceeds n, the plane is saved and the points are marked as matched. Next, the termination condition is that after N iterations, no three unlabeled points or less than N planar points are found. And finally, respectively carrying out plane fitting on the point cloud clusters obtained by the region growth segmentation to obtain all plane equations.
And step 1204, determining coordinate information of all pose feature points based on plane equations of all target planes.
After all plane equations of the section to be spliced are determined, the plane equations are sequentially combined, and coordinate information of all pose feature points is solved. Referring to fig. 6, the specific solving steps are as follows:
and step A, arranging and combining all plane equations to form a plane equation set.
The set of plane equations includes a plurality of sets of equations, each set of equations including 3 plane equations, each set of equations being different.
And B, screening an intersecting plane set from the plane equation set based on the rank and preset value of the non-zero row of the coefficient matrix formed by the target set equation.
The objective set of equations is any one set of equations in the set of plane equations.
The positional relationship of the three planes in space is related to the rank of the augmentation matrix of their simultaneous sets of linear equations. When the non-zero row rank r (a) of the coefficient matrix of the linear equation set is equal to 3, it means that three planes intersect at one point. Therefore, it is possible to screen out plane groups that are not parallel to each other using this as a determination condition.
And C, solving each set of equations in the intersecting plane set, and determining coordinate information of all pose feature points.
The following description will take a specific equation set as an example, where the equation set is composed of three planes that are not parallel to each other:
；
the normal vector of the intersecting straight line of the plane 1 and the plane 2 isThe calculation formula is as follows:
；
the straight line can be expressed as:
；
substituting x, y, z intoCan be solved to obtain t and further obtain the intersection point coordinate (x 0 ，y 0 ，z 0 ) The method comprises the following steps of:
，/> ，/>；
；
；
；
。
and step S130, pre-splicing each spliced section based on the coordinate information of the endpoints of all sections to be spliced.
After the coordinate information of all pose feature points of the two sections to be assembled is obtained, procrustes Analysis can be used for unifying two groups of coordinates of the two sections to be assembled into the same coordinate system, and in the assembling process of the step, the sequence of the feature points in the coordinate matrix is required to be ensured to be mutually opposite, so that the correctness of the spatial pose after the structure pre-assembling is completed can be realized.
If each section to be spliced is provided with 8 pose characteristic points, the coordinates are as follows:
，/>。
and step S140, extracting coordinate information of all the spliced feature points from the point cloud data set.
The assembly characteristic points are hole center coordinates of bolt holes on the side surfaces of the end parts of the steel structures to be assembled, and a plurality of butt joint bolt holes are formed in the side surfaces of the end parts of each steel structure to be assembled.
After the pre-assembly of the space structure is completed, the assembly characteristic points of the structure manufacturing errors are further considered to realize the assembly of the structure.
The shape and dimensions of large steel structures inevitably produce manufacturing errors during the manufacturing process, which results in two cross-sectional shapes for assembly that are not exactly identical. If the manufacturing error of the structure is not considered, the assembled point cloud model does not conform to the actual structure, and has no guiding significance on the actual construction. Therefore, the assembly characteristic points of the structure are required to be extracted for accurately assembling the structure, the actual manufacturing error of the structure is considered, and the practicability of the assembled structure is ensured.
The extraction steps of the coordinate information of the spliced feature points are as follows:
step S1401, projecting the point cloud cluster of the target plane to a plane equation corresponding to the target plane, to obtain a projected point cloud cluster of the target plane.
And projecting the point cloud clusters of each target plane which is already segmented in the previous step to a plane equation corresponding to the target plane. According to the different density of the projected bolt hole point clouds, the hole center of the bolt hole can be determined more accurately.
The projection process is as follows:
the general equation for the target plane is assumed to be:；
assuming that the three-dimensional space coordinates not on the plane are (x 0, y0, z 0), the coordinates of the projection points on the plane are. Because the current point to the projection point are perpendicular to the plane, the following conditions are required to be satisfied according to the perpendicular constraint conditions:
，/>；
After bringing the above 2 formulas into the general equation for the target plane, we get:
，/>，
。
the projection coordinates of the space three-dimensional point to the plane can be obtained。
Step S1402, extracting and fitting the projection point cloud cluster of the target plane, and determining the circular boundary point cloud data of the bolt hole on the target plane.
In some embodiments, an Alpha shapes algorithm may be used to extract and fit the projected point cloud clusters of the target plane to determine circular boundary point cloud data for the bolt holes on the target plane.
The Alpha Shapes algorithm uses a circle of radius Alpha to scroll outside the point set P, and when Alpha is large enough, the circle does not scroll inside the point set, as shown in fig. 7.
Inside the point set P, any two points P can be passed 1 ，p 2 Drawing a circle of radius α considers p if there are no other points within the circle 1 And p 2 Is the boundary point, the connection p between them 1 ，p 2 Is a boundary line segment. For a finite set of points P, consisting of n points, then the n points may constitute n-1 directed line segments. Our task is to determine which segments are boundary segments. Known point p 1 (x 1 ，y 1 )，p 2 (x 2 ，y 2 ) The center p of the two circles is calculated 3 (x 3 ，y 3 ) Can be obtained by a rear distance intersection method.
；
；
After the circle center is obtained, whether other points exist in the circle can be judged by judging the relation between the distance from other points to the circle center and the radius alpha. The specific steps are as follows:
First, an arbitrary point P from a point set P 1 Firstly, taking any point P in a neighborhood point set R with radius of 2 alpha 2 Calculate the point p 1 And point p 2 Determined center of circle p 0 . Then, calculating other points in the neighborhood to the circle center p 0 If all di are greater than a, indicating that there are no other points in the circle, p 1 p 2 Is a boundary line segment; otherwise, the line segment is a non-boundary line segment. Finally, repeating the first two steps for the rest points in the neighborhood point set R until all the points in the R are judged to be finished; repeating the steps for the rest points in the point set P until all the points in the point set P are judged.
Step S1403, fitting the circular boundary point cloud data of the bolt holes on all the target planes to obtain the hole center coordinates of all the bolt holes.
In some embodiments, fitting processing can be performed on the circular boundary point cloud data of the bolt holes on all target planes based on Random sample consensus algorithm, so as to obtain the hole center coordinates of all the bolt holes.
After the round boundary point cloud data of the bolt holes, the information of the space three-dimensional circle is fitted by using a RANSAC algorithm. When a sphere intersects a plane, its intersection line is a circle. Conversely, any circle of space may be represented as an intersection of a sphere and a plane. Therefore, the rectangular equation for a space circle is:
；
The specific fitting process is as follows:
first, initializing the local point set Inputs and the number of loops. And secondly, three points are randomly selected from the boundary point set D, and parameters (circle center and radius) of the circle are calculated. And thirdly, calculating the distance between each boundary and the circle center calculated in the last step, adding the points with the distance smaller than the threshold value into the local point set, and otherwise, considering the points as the local points. Step four, counting the number M of local points on the circle, if M is greater than a threshold value Mmin, considering that the estimation is successful, and entering a fifth step; otherwise, the sixth step is carried out. And fifthly, recalculating the parameters of the circle by using a least square method for all points in the point set points to obtain a final result. And step six, adding one to the cycle times, ending if the cycle times exceed the set upper limit kmax, otherwise returning to the step 2 to continue iteration.
And step S150, carrying out structural assembly on all the steel structures to be assembled based on the coordinate information of all the assembly characteristic points.
After the coordinate information of the hole center of each group of correspondingly spliced bolt holes is obtained, the distance to be finally moved is determined according to the current coordinate information, so that accurate splicing is facilitated. The specific assembly process is as follows:
step S1501, constructing a difference matrix based on the difference value of the hole center coordinates of each group of corresponding bolt holes on the two steel structures to be spliced, which are required to be spliced, in the XYZ direction.
The difference matrix E is constructed as follows:
；
step S1502, calculating a minimum value of Frobenius norms of the difference matrix, and determining a moving distance of hole center coordinates of two correspondingly connected bolt holes in XYZ directions.
The difference matrix is respectively moved to X, Y and Z directions by a distance d x 、d y And d z And obtaining minimum values by respectively obtaining the deviation of the three direction parameters, namely obtaining the moving distances of all the directions.
Still described in the difference matrix E above:
for a pair of
D in (d) x 、d y And d z The bias derivative is equivalent to pair
D in (d) x 、d y And d z And (5) obtaining deviation guide.
D when finding the minimum value of the function x 、d y And d z The coordinate matrix can be obtained by moving the coordinate matrix in the X, Y and Z directions respectively.
And step S1503, carrying out structural assembly on the steel structure to be assembled based on the moving distance.
If the error value of the difference matrix after deviation adjustment meets the structure allowable deviation range, the two structures can be considered to smoothly complete the splicing process, and structure splicing is realized.
According to the virtual assembly method provided by the invention, firstly, point cloud data sets of all steel structures to be assembled are obtained, and then, coordinate information of all pose feature points is extracted from the point cloud data sets. And then, pre-splicing the sections based on the coordinate information of the endpoints of all the sections to be spliced. Next, coordinate information of all the assembled feature points is extracted from the point cloud data set. And finally, carrying out structural assembly on all the steel structures to be assembled based on the coordinate information of all the assembly characteristic points. By considering pose characteristic points and splicing characteristic points, the splicing process can be closer to engineering practice, and even if manufacturing errors exist in a steel structure, accurate splicing can be realized based on the splicing characteristic points.
It should be understood that the sequence number of each step in the foregoing embodiment does not mean that the execution sequence of each process should be determined by the function and the internal logic, and should not limit the implementation process of the embodiment of the present invention.
Based on the virtual assembly method of the steel structure provided by the embodiment, correspondingly, the invention further provides a specific implementation mode of the virtual assembly device of the steel structure, which is applied to the virtual assembly method of the steel structure. Please refer to the following examples.
As shown in fig. 8, there is provided a virtual splice device 800 of steel construction, the device comprising:
the data acquisition module 810 is configured to acquire point cloud data sets of all steel structures to be assembled;
the pose information extraction module 820 is configured to extract coordinate information of all pose feature points from the point cloud data set, where the pose feature points are endpoints of each section to be assembled of all the steel structures to be assembled;
the pre-assembly module 830 is configured to pre-assemble each assembled section based on coordinate information of endpoints of all sections to be assembled;
the assembly information extraction module 840 is configured to extract coordinate information of all assembly feature points from the point cloud data set, where the assembly feature points are hole center coordinates of bolt holes on side surfaces of end parts of the steel structures to be assembled, and a plurality of butt joint bolt holes are arranged on side surfaces of end parts of each steel structure to be assembled;
And the assembling module 850 is used for structurally assembling all the steel structures to be assembled based on the coordinate information of all the assembling feature points.
In one possible implementation manner, the pose information extraction module 820 is configured to perform segmentation screening processing on the point cloud data set based on the european clustering algorithm, so as to screen out endpoint cloud data sets of each section to be spliced;
performing plane segmentation processing on the endpoint cloud data sets of each section to be spliced based on a region growing method to determine point cloud clusters positioned on different target planes, wherein the target planes are any one plane;
fitting the endpoint cloud clusters of all different target planes based on a random sampling consistency algorithm to obtain a plane equation of each target plane;
and determining coordinate information of all pose feature points based on plane equations of all target planes.
In one possible implementation, the pose information extraction module 820 is configured to arrange and combine all plane equations to form a plane equation set, where the plane equation set includes a plurality of sets of equations, each set of equations includes 3 plane equations, and each set of equations is different;
screening an intersecting plane set from the plane set based on a rank and a preset value of a non-zero row of a coefficient matrix formed by a target set of equations, wherein the target set of equations is any one set of equations in the plane set of equations;
And solving each set of equations in the intersecting plane set, and determining coordinate information of all pose feature points.
In one possible implementation manner, the assembly information extraction module 840 is configured to project the point cloud cluster of the target plane to a plane equation corresponding to the target plane, to obtain a projected point cloud cluster of the target plane;
carrying out extraction fitting on the projection point cloud cluster of the target plane, and determining round boundary point cloud data of the bolt hole on the target plane;
fitting the circular boundary point cloud data of the bolt holes on all the target planes to obtain the hole center coordinates of all the bolt holes.
In one possible implementation manner, the assembly information extraction module 840 is configured to extract and fit the projection point cloud clusters of the target plane based on the Alpha shapes algorithm, and determine the circular boundary point cloud data of the bolt hole on the target plane.
In one possible implementation, the assembly information extraction module 840 is configured to perform fitting processing on the circular boundary point cloud data of the bolt holes on all the target planes based on the Random sample consensus algorithm, so as to obtain the hole center coordinates of all the bolt holes.
In one possible implementation manner, the assembling module 850 is configured to construct a difference matrix based on the difference value in XYZ direction of the hole center coordinates of each group of corresponding bolt holes on the extracted two steel structures to be assembled;
Calculating the minimum value of Frobenius norms of a difference matrix, and determining the moving distance of hole center coordinates of two correspondingly connected bolt holes in the XYZ direction;
based on the moving distance, the steel structure to be assembled is structurally assembled.
Fig. 9 is a schematic diagram of an electronic device according to an embodiment of the present invention. As shown in fig. 9, the electronic apparatus 9 of this embodiment includes: a processor 90, a memory 91 and a computer program 92 stored in said memory 91 and executable on said processor 90. The processor 90, when executing the computer program 92, implements the steps of the virtual assembly method embodiment of each steel structure described above, such as steps 110 through 150 shown in fig. 1. Alternatively, the processor 90, when executing the computer program 92, performs the functions of the modules in the apparatus embodiments described above, such as the functions of the modules 810-850 shown in fig. 8.
Illustratively, the computer program 92 may be partitioned into one or more modules that are stored in the memory 91 and executed by the processor 90 to complete the present invention. The one or more modules may be a series of computer program instruction segments capable of performing the specified functions for describing the execution of the computer program 92 in the electronic device 9. For example, the computer program 92 may be partitioned into modules 810 through 850 shown in FIG. 8.
The electronic device 9 may include, but is not limited to, a processor 90, a memory 91. It will be appreciated by those skilled in the art that fig. 9 is merely an example of the electronic device 9 and is not meant to be limiting as the electronic device 9 may include more or fewer components than shown, or may combine certain components, or different components, e.g., the electronic device may further include an input-output device, a network access device, a bus, etc.
The processor 90 may be a central processing unit (Central Processing Unit, CPU), other general purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), field-programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory 91 may be an internal storage unit of the electronic device 9, such as a hard disk or a memory of the electronic device 9. The memory 91 may also be an external storage device of the electronic device 9, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card) or the like, which are provided on the electronic device 9. Further, the memory 91 may also include both an internal storage unit and an external storage device of the electronic device 9. The memory 91 is used for storing the computer program and other programs and data required by the electronic device. The memory 91 may also be used for temporarily storing data that has been output or is to be output.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-described division of the functional units and modules is illustrated, and in practical application, the above-described functional distribution may be performed by different functional units and modules according to needs, i.e. the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-described functions. The functional units and modules in the embodiment may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit, where the integrated units may be implemented in a form of hardware or a form of a software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working process of the units and modules in the above system may refer to the corresponding process in the foregoing method embodiment, which is not described herein again.
In the foregoing embodiments, the descriptions of the embodiments are emphasized, and in part, not described or illustrated in any particular embodiment, reference is made to the related descriptions of other embodiments.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus/electronic device and method may be implemented in other manners. For example, the apparatus/electronic device embodiments described above are merely illustrative, e.g., the division of the modules or units is merely a logical function division, and there may be additional divisions in actual implementation, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection via interfaces, devices or units, which may be in electrical, mechanical or other forms.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated modules/units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the present invention may implement all or part of the flow of the method of the above embodiment, or may be implemented by instructing related hardware by a computer program, where the computer program may be stored in a computer readable storage medium, and the computer program may implement the steps of the virtual assembly method embodiment of each steel structure when executed by a processor. Wherein the computer program comprises computer program code which may be in source code form, object code form, executable file or some intermediate form etc. The computer readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), an electrical carrier signal, a telecommunications signal, a software distribution medium, and so forth.
The above embodiments are only for illustrating the technical solution of the present invention, and not for limiting the same; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention, and are intended to be included in the scope of the present invention.
Claims (8)
1. The virtual assembly method of the steel structure is characterized by comprising the following steps of:
acquiring point cloud data sets of all steel structures to be spliced;
dividing and screening the point cloud data sets based on an European clustering algorithm to screen out the endpoint cloud data sets of each section to be spliced;
performing plane segmentation processing on the endpoint cloud data sets of the sections to be spliced based on a region growing method to determine point cloud clusters positioned on different target planes, wherein the target planes are any one plane;
fitting the endpoint cloud clusters of all different target planes based on a random sampling consistency algorithm to obtain a plane equation of each target plane;
Arranging and combining all plane equations to form a plane equation set, wherein the plane equation set comprises a plurality of groups of equations, each group of equations comprises 3 plane equations, and each group of equations is different;
screening an intersecting plane set from the plane set of equations based on a rank and a preset value of a non-zero row of a coefficient matrix formed by a target set of equations, wherein the target set of equations is any one set of equations in the plane set of equations;
solving each set of equations in the intersecting plane set, and determining coordinate information of all pose characteristic points, wherein the pose characteristic points are endpoints of all sections to be spliced of all the steel structures to be spliced;
pre-splicing each spliced section based on the coordinate information of the endpoints of all sections to be spliced;
extracting coordinate information of all assembling characteristic points from the point cloud data set, wherein the assembling characteristic points are hole centers of bolt holes on the side surfaces of the end parts of the steel structures to be assembled, and a plurality of butt joint bolt holes are formed in the side surfaces of the end parts of each steel structure to be assembled;
and carrying out structural assembly on all the steel structures to be assembled based on the coordinate information of all the assembly characteristic points.
2. The virtual assembly method of steel structures according to claim 1, wherein the extracting coordinate information of all assembly feature points from the point cloud dataset includes:
projecting the point cloud cluster of the target plane to a plane equation corresponding to the target plane to obtain a projected point cloud cluster of the target plane;
extracting and fitting the projection point cloud cluster of the target plane, and determining round boundary point cloud data of the bolt hole on the target plane;
fitting the circular boundary point cloud data of all the bolt holes on the target plane to obtain the hole center coordinates of all the bolt holes.
3. The virtual assembly method of a steel structure according to claim 2, wherein the performing extraction fitting on the projection point cloud cluster of the target plane to determine the circular boundary point cloud data of the bolt hole on the target plane includes:
and extracting and fitting the projection point cloud cluster of the target plane based on an Alpha shapes algorithm, and determining the circular boundary point cloud data of the bolt hole on the target plane.
4. The virtual assembly method of the steel structure according to claim 2 or 3, wherein the fitting processing is performed on the circular boundary point cloud data of the bolt holes on all the target planes to obtain hole center coordinates of all the bolt holes, and the method comprises the following steps:
And fitting the circular boundary point cloud data of the bolt holes on all the target planes based on a random sampling consistency algorithm to obtain the hole center coordinates of all the bolt holes.
5. The virtual splicing method of the steel structure according to claim 1, wherein the structural splicing of the steel structure to be spliced based on the coordinate information of all splicing feature points comprises:
constructing a difference matrix based on the difference value of the hole center coordinates of each group of corresponding bolt holes on the extracted two steel structures to be spliced in the XYZ direction;
calculating the minimum value of Frobenius norms of the difference matrix, and determining the moving distance of hole center coordinates of two correspondingly connected bolt holes in the XYZ direction;
based on the moving distance, structural assembly is carried out on the steel structure to be assembled.
6. A virtual splicing device of steel structure, comprising:
the data acquisition module is used for acquiring point cloud data sets of all the steel structures to be assembled;
the pose information extraction module is used for carrying out segmentation screening processing on the point cloud data sets based on an European clustering algorithm so as to screen out the endpoint cloud data sets of each section to be spliced; performing plane segmentation processing on the endpoint cloud data sets of the sections to be spliced based on a region growing method to determine point cloud clusters positioned on different target planes, wherein the target planes are any one plane; fitting the endpoint cloud clusters of all different target planes based on a random sampling consistency algorithm to obtain a plane equation of each target plane; arranging and combining all plane equations to form a plane equation set, wherein the plane equation set comprises a plurality of groups of equations, each group of equations comprises 3 plane equations, and each group of equations is different; screening an intersecting plane set from the plane set of equations based on a rank and a preset value of a non-zero row of a coefficient matrix formed by a target set of equations, wherein the target set of equations is any one set of equations in the plane set of equations; solving each set of equations in the intersecting plane set, and determining coordinate information of all pose characteristic points, wherein the pose characteristic points are endpoints of all sections to be spliced of all the steel structures to be spliced;
The pre-splicing module is used for pre-splicing each spliced section based on the coordinate information of the endpoints of all the sections to be spliced;
the assembly information extraction module is used for extracting coordinate information of all assembly characteristic points from the point cloud data set, wherein the assembly characteristic points are hole centers of bolt holes on the side surfaces of the end parts of the steel structures to be assembled, and a plurality of butt joint bolt holes are formed in the side surfaces of the end parts of each steel structure to be assembled;
and the assembly module is used for structurally assembling all the steel structures to be assembled based on the coordinate information of all the assembly characteristic points.
7. An electronic device comprising a memory for storing a computer program and a processor for invoking and running the computer program stored in the memory to perform the method of any of claims 1 to 5.
8. A computer readable storage medium storing a computer program, characterized in that the computer program when executed by a processor implements the steps of the method according to any one of claims 1 to 5.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311461167.3A CN117197410B (en) | 2023-11-06 | 2023-11-06 | Virtual splicing method, device and equipment for steel structure and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311461167.3A CN117197410B (en) | 2023-11-06 | 2023-11-06 | Virtual splicing method, device and equipment for steel structure and storage medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN117197410A CN117197410A (en) | 2023-12-08 |
| CN117197410B true CN117197410B (en) | 2024-03-26 |
Family
ID=88994578
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311461167.3A Active CN117197410B (en) | 2023-11-06 | 2023-11-06 | Virtual splicing method, device and equipment for steel structure and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117197410B (en) |
Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107292947A (en) * | 2016-03-31 | 2017-10-24 | 上海宝冶集团有限公司 | Construction steel structure digitlization pre-assembly method based on 3-D scanning and reverse modeling |
| CN108563860A (en) * | 2018-04-10 | 2018-09-21 | 中交第二航务工程局有限公司 | A kind of virtual pre-assembly method of steel construction |
| CN111091062A (en) * | 2019-11-21 | 2020-05-01 | 东南大学 | Robot out-of-order target sorting method based on 3D visual clustering and matching |
| WO2021097618A1 (en) * | 2019-11-18 | 2021-05-27 | 深圳市大疆创新科技有限公司 | Point cloud segmentation method and system, and computer storage medium |
| CN113722789A (en) * | 2021-07-22 | 2021-11-30 | 河北工业大学 | Steel structure bridge virtual assembly method based on 3D laser scanning and process feedback |
| CN114140301A (en) * | 2021-08-31 | 2022-03-04 | 重庆大学 | Data acquisition method for engineering structure digital pre-assembly |
| CN114332402A (en) * | 2021-12-23 | 2022-04-12 | 中交第二公路勘察设计研究院有限公司 | Ground-based and handheld laser scanning-fused steel bridge simulation pre-assembly method |
| CN116520353A (en) * | 2023-06-29 | 2023-08-01 | 广汽埃安新能源汽车股份有限公司 | Ground detection method, device, storage medium and equipment based on laser point cloud |
| CN116579046A (en) * | 2023-03-02 | 2023-08-11 | 中交第二公路工程局有限公司 | Steel structure pre-assembling method with center registration and joint self-holding device |
| CN116596755A (en) * | 2023-03-13 | 2023-08-15 | 深圳优立全息科技有限公司 | Method, device, equipment and storage medium for splicing point cloud data |
| CN116975963A (en) * | 2023-07-12 | 2023-10-31 | 香港理工大学深圳研究院 | Linear prediction method of stress-free state virtual assembly result |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2003056470A1 (en) * | 2001-12-21 | 2003-07-10 | 3Dfacto Aps | A method, a computer system, and a computer program product for configuring a virtual representation of an assembly of a plurality of components |
| US9803977B2 (en) * | 2013-01-23 | 2017-10-31 | Fuchs Consulting, Inc. | Apparatus and method for bridge assembly |
| US10450053B2 (en) * | 2016-05-11 | 2019-10-22 | The Boeing Company | Methods using predictive shimming to optimize part-to-part alignment |
-
2023
- 2023-11-06 CN CN202311461167.3A patent/CN117197410B/en active Active
Patent Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107292947A (en) * | 2016-03-31 | 2017-10-24 | 上海宝冶集团有限公司 | Construction steel structure digitlization pre-assembly method based on 3-D scanning and reverse modeling |
| CN108563860A (en) * | 2018-04-10 | 2018-09-21 | 中交第二航务工程局有限公司 | A kind of virtual pre-assembly method of steel construction |
| WO2021097618A1 (en) * | 2019-11-18 | 2021-05-27 | 深圳市大疆创新科技有限公司 | Point cloud segmentation method and system, and computer storage medium |
| CN111091062A (en) * | 2019-11-21 | 2020-05-01 | 东南大学 | Robot out-of-order target sorting method based on 3D visual clustering and matching |
| CN113722789A (en) * | 2021-07-22 | 2021-11-30 | 河北工业大学 | Steel structure bridge virtual assembly method based on 3D laser scanning and process feedback |
| CN114140301A (en) * | 2021-08-31 | 2022-03-04 | 重庆大学 | Data acquisition method for engineering structure digital pre-assembly |
| CN114332402A (en) * | 2021-12-23 | 2022-04-12 | 中交第二公路勘察设计研究院有限公司 | Ground-based and handheld laser scanning-fused steel bridge simulation pre-assembly method |
| CN116579046A (en) * | 2023-03-02 | 2023-08-11 | 中交第二公路工程局有限公司 | Steel structure pre-assembling method with center registration and joint self-holding device |
| CN116596755A (en) * | 2023-03-13 | 2023-08-15 | 深圳优立全息科技有限公司 | Method, device, equipment and storage medium for splicing point cloud data |
| CN116520353A (en) * | 2023-06-29 | 2023-08-01 | 广汽埃安新能源汽车股份有限公司 | Ground detection method, device, storage medium and equipment based on laser point cloud |
| CN116975963A (en) * | 2023-07-12 | 2023-10-31 | 香港理工大学深圳研究院 | Linear prediction method of stress-free state virtual assembly result |
Non-Patent Citations (2)
| Title |
|---|
| 基于改进区域生长法的羊体点云分割及体尺参数测量;马学磊;薛河儒;周艳青;姜新华;刘娜;;中国农业大学学报(第03期);全文 * |
| 基于改进果蝇优化算法的BP神经网络在组合梁钢构件定位中的应用;梁栋等;重庆交通大学学报;第42卷(第06期);第1-8页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN117197410A (en) | 2023-12-08 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109541997B (en) | Spraying robot rapid intelligent programming method for plane/approximate plane workpiece | |
| WO2024077812A1 (en) | Single building three-dimensional reconstruction method based on point cloud semantic segmentation and structure fitting | |
| Murali et al. | Indoor Scan2BIM: Building information models of house interiors | |
| CN111986115A (en) | Accurate elimination method for laser point cloud noise and redundant data | |
| CN110599506B (en) | Point cloud segmentation method for three-dimensional measurement of complex special-shaped curved surface robot | |
| Wei et al. | Automatic coarse registration of point clouds using plane contour shape descriptor and topological graph voting | |
| CN111027140B (en) | Airplane standard part model rapid reconstruction method based on multi-view point cloud data | |
| CN116402866A (en) | Point cloud-based part digital twin geometric modeling and error assessment method and system | |
| CN114743259A (en) | Pose estimation method, pose estimation system, terminal, storage medium and application | |
| CN114663373B (en) | Point cloud registration method and device for part surface quality detection | |
| CN114332291B (en) | Method for extracting outline rule of oblique photography model building | |
| CN115797418B (en) | Complex mechanical part measurement point cloud registration method and system based on improved ICP | |
| CN112396641A (en) | Point cloud global registration method based on congruent two-baseline matching | |
| CN115546116A (en) | Method and system for extracting and calculating spacing of discontinuous surface of fully-covered rock mass | |
| CN117132630A (en) | Point cloud registration method based on second-order spatial compatibility measurement | |
| CN114972640B (en) | Cable three-dimensional reconstruction and parameter calculation method based on point cloud | |
| CN113971718B (en) | Method for performing Boolean operation on three-dimensional point cloud model | |
| CN117197410B (en) | Virtual splicing method, device and equipment for steel structure and storage medium | |
| CN114463396A (en) | Point cloud registration method using plane shape and topological graph voting | |
| Gollub et al. | A partitioned approach for efficient graph–based place recognition | |
| CN117253205A (en) | Road surface point cloud rapid extraction method based on mobile measurement system | |
| Tushev et al. | Parallel algorithms for effective correspondence problem solution in computer vision | |
| CN115147471A (en) | Laser point cloud automatic registration method based on curvature density characteristics | |
| CN112767462B (en) | Point cloud single-point alignment method based on ridge-valley characteristics and depth characteristic descriptors | |
| Wang et al. | Quasi-Dense Matching Algorithm for Close-Range Image Combined With Feature Line Constraint |
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 |