BIM-based steel structure component collision detection method

Abstract

The present application relates to the technical field of three-dimensional point cloud data matching, and specifically to a BIM-based anti-collision detection method for steel structure components. The method comprises: obtaining a BIM model and a three-dimensional point cloud data model of the steel structure component; determining the curvature position difference of each point cloud data within the three-dimensional point cloud data model to determine the point cloud model of the steel structure component; converting the BIM model into a BIM point cloud model of the steel structure component; matching the point cloud model of the steel structure component with the point cloud data in the BIM point cloud model of the steel structure component based on a point cloud feature descriptor of the point cloud data; modifying the point cloud feature descriptor based on the curvature difference between the source point cloud and the target point cloud, and the similarity between the spatial position vectors of the source point cloud and the target point cloud; and performing static collision detection on the steel structure component using the matched point cloud model of the steel structure component. The present application improves the accuracy of anti-collision detection of steel structure components.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of three-dimensional point cloud data matching, in particular to a steel structure component anti-collision detection method based on BIM. BACKGROUND

[0002] BIM (Building Information Modeling) is a technology based on digital technology for architectural design, construction and operation management. BIM can perform collision detection in the design stage by modeling all structural components, equipment, pipes, beams and columns in a virtual space, so that problems can be found and solved in time, thereby reducing changes and project delays in the construction process.

[0003] However, if the data completeness of the BIM model is low and the precision is not high, the result of the final collision detection will have a large error. As the size of the project increases, the number of steel structure components, equipment and pipes will also increase significantly, and the calculation amount and complexity of collision detection will also increase significantly. In the actual construction process, there may be inconsistencies between components and design, resulting in collision problems caused by construction errors. SUMMARY

[0004] To solve the above technical problems, the present application provides a steel structure component anti-collision detection method based on BIM to solve the existing problems.

[0005] The steel structure component anti-collision detection method based on BIM of the present application adopts the following technical scheme:

[0006] One embodiment of the present application provides a steel structure component anti-collision detection method based on BIM, which comprises the following steps:

[0007] Obtain the BIM model of the steel structure component by using the design drawing of the steel structure component, collect three-dimensional point cloud data of the same steel structure component, and obtain a three-dimensional point cloud data model of the steel structure component;

[0008] Divide the three-dimensional point cloud data model into cubic spaces, analyze the curvature difference and distance interval of each point cloud data in each cubic space and the remaining point cloud data, and determine the curvature position difference of each point cloud data in each cubic space;

[0009] Based on the curvature position difference of all point cloud data in the three-dimensional point cloud data model, divide all point cloud data in the three-dimensional point cloud data model into curved surface structure data and planar structure data, downsample the planar structure data, and use all point cloud data after down-sampling of the three-dimensional point cloud data model as a steel structure component point cloud model;

[0010] transforming the BIM model into a steel structure component BIM point cloud model; based on a point cloud feature descriptor, matching point cloud data in the steel structure component point cloud model with point cloud data in the steel structure component BIM point cloud model, wherein, in the process of matching, a search space of the source point cloud and the target point cloud is determined respectively, and a spatial position vector of the source point cloud and the target point cloud in the respective search space and other point cloud data is determined; according to the curvature difference of the source point cloud and the target point cloud and the similarity of the spatial position vectors of the source point cloud and the target point cloud, the point cloud feature descriptor is corrected;

[0011] Performing static collision detection on the steel structure component through the matched steel structure component point cloud model.

[0012] In one embodiment, the determination of the curvature position difference includes:

[0013] Calculating a ratio of the curvature difference and the distance interval, determining the curvature position difference of each point cloud data in each cubic space based on the ratio, and the curvature position difference is positively correlated with the absolute value of the ratio.

[0014] In one embodiment, the curvature position difference is the mean value of the absolute value of each point cloud data and all other point cloud data in each cubic space.

[0015] In one embodiment, the curvature difference is the absolute value of the difference of the curvature of each point cloud data and all other point cloud data.

[0016] In one embodiment, the dividing of all point cloud data in the three-dimensional point cloud data model into curved surface structure data and plane structure data includes:

[0017] Using a clustering algorithm, the curvature position difference of all point cloud data in the three-dimensional point cloud data model is divided into two clusters, the mean value of all elements in each cluster is calculated, all point cloud data corresponding to elements in the cluster with the maximum mean value are taken as curved surface structure data, and all point cloud data corresponding to elements in the cluster with the minimum mean value are taken as plane structure data.

[0018] In one embodiment, the downsampling of the plane structure data includes:

[0019] Obtaining the centroid of the plane structure data belonging to the same plane, and replacing the plane structure data with the centroid.

[0020] In one embodiment, the search space is a spherical region obtained by taking the source point cloud and the target point cloud as the center point and the same preset search radius.

[0021] In one embodiment, the spatial position vector is a directed line segment from the source point cloud and the target point cloud to the other point cloud data in the respective search space.

[0022] In one embodiment, the modifying the point cloud feature descriptor comprises:

[0023] The curvature difference between the source point cloud and the target point cloud is denoted as a first difference, the minimum value of the point cloud data in the search space of the source point cloud and the target point cloud is obtained, and the product of the first difference and the minimum value is calculated;

[0024] The similarity between the jth spatial position vector corresponding to the source point cloud and the jth spatial position vector corresponding to the target point cloud is calculated, the ratio of the sum of the product and the minimum value and the similarity is determined, denoted as a point cloud search feature, and the point cloud feature descriptor is modified based on the point cloud search feature.

[0025] In one embodiment, the last dimension data in the point cloud feature descriptor of the source point cloud and the target point cloud is replaced by the point cloud search feature.

[0026] The present application has at least the following beneficial effects:

[0027] The present application divides the three-dimensional point cloud data model into cubic spaces, and distinguishes curved surface and plane structure based on curvature position difference, and performs down-sampling on the plane area, which significantly reduces the amount of redundant data, retains the key geometric curved surface features, avoids excessive calculation on flat areas, and improves the speed of subsequent collision detection. In addition, the determination of the curvature position difference quantifies the local geometric mutation of the steel structure member, so that the curved surface structure data can be completely retained, the curved surface deformation features can be effectively captured, and the feature loss problem caused by traditional uniform down-sampling can be avoided. Further, by introducing the curvature difference and the similarity of the spatial position vector, the point cloud feature descriptor of the point cloud data in the matching process of the steel structure member point cloud model and the steel structure member BIM point cloud model is modified, the influence of point cloud noise and local missing is resisted, stable matching can be ensured in the occlusion scene, the false matching problem of similar geometric structures is inhibited, the matching accuracy of the steel structure member point cloud model and the steel structure member BIM point cloud model is improved, the error existing in the collision detection based on the BIM model of the steel structure member is eliminated, and the accuracy of the collision detection of the steel structure member is improved. BRIEF DESCRIPTION OF DRAWINGS

[0028] In order to more clearly illustrate the technical solutions and advantages of the embodiments of the present application or the prior art, the drawings needed in the following embodiment or prior art description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and those skilled in the art can obtain other drawings according to these drawings without creating any creative labor.

[0029] Figure 1 A step flow chart of the BIM-based steel structure component anti-collision detection method provided in the present application is provided.

[0030] Figure 2 A flow chart for obtaining a BIM correction point cloud model of a steel structure component is provided. DETAILED DESCRIPTION

[0031] In order to further illustrate the technical means and effects adopted by the present application to achieve the predetermined purposes, the specific embodiments, structures, features and effects of the BIM-based steel structure component anti-collision detection method according to the present application are described in detail below in combination with the drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures or characteristics in one or more embodiments can be combined in any suitable form.

[0032] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the present application belongs.

[0033] The specific scheme of the BIM-based steel structure component anti-collision detection method provided in the present application is described below in combination with the drawings.

[0034] The BIM-based steel structure component anti-collision detection method provided in one embodiment of the present application, specifically, the following BIM-based steel structure component anti-collision detection method is provided, please refer to Figure 1 The method comprises the following steps:

[0035] Step S001, obtaining a BIM model of a steel structure component by using design drawings of the steel structure component, collecting three-dimensional point cloud data of the same steel structure component, and obtaining a three-dimensional point cloud data model of the steel structure component.

[0036] The main steel structure components in the construction site of a steel structure project are diagonal grid trusses, steel columns, supports and other scattered piece hoisting units, etc. This embodiment collects data for any steel structure component, and obtains a BIM model of the steel structure component through CAD drawings of the steel structure component.

[0037] Secondly, three-dimensional point cloud data of the actual steel structure component is collected by using a high-precision three-dimensional scanner to obtain a three-dimensional point cloud data model of the steel structure component. The implementer can select other existing technologies to obtain the three-dimensional point cloud data model of the steel structure component.

[0038] Step S002, the three-dimensional point cloud data model is divided into each cubic space, the curvature difference of each point cloud data in each cubic space and the remaining point cloud data is analyzed, and the distance interval is analyzed, and the curvature position difference of each point cloud data in each cubic space is determined.

[0039] For the point cloud data model of the steel structure member collected, since the point cloud resolution used in the actual construction process is higher and higher, the scale of the point cloud data of the steel structure member collected is also larger and larger. There is a lot of redundant data information in the collected point cloud data, which easily interferes with the anti-collision detection of the steel structure member.

[0040] Therefore, the embodiment filters the point cloud data of the steel structure member on the basis of retaining as much original information as possible.

[0041] In the steel structure construction process, steel structures are often used for the construction of curtain walls, and the surface of the wall of the building curtain wall has a large number of smooth space forms, such as the structures of the wall surface, the ground and other parts. At the same time, the key position area of the collision of the steel structure member is mostly at the arc-shaped structure position, and these positions are prone to collision due to the complex reinforcement condition and the relatively dense number of steel bars. Therefore, the embodiment analyzes and calculates the point cloud data of the steel member in combination with the local position characteristics of the steel member in the steel structure construction process.

[0042] The embodiment first divides the three-dimensional point cloud data model of the steel structure member into a plurality of cubic spaces of a preset size, and the preset size in the embodiment is 128, which can be set by the implementer according to the actual situation, and the embodiment does not limit this.

[0043] Secondly, the curvature position difference of each point cloud data in each cubic space is determined, specifically: the curvature difference of each point cloud data in each cubic space and the remaining point cloud data is calculated, and the distance interval of each point cloud data in each cubic space and the remaining point cloud data is calculated, and the curvature position difference is obtained in combination with the curvature difference and the distance interval.

[0044] It should be noted that the difference represents the difference between two variables, which can be calculated by using the absolute value of the difference, the square of the difference, the ratio, etc., and the embodiment does not limit this.

[0045] In the embodiment, the expression of the curvature position difference of each point cloud data in each cubic space is:

[0046] In the formula, is the curvature position difference of the xth point cloud data in each cubic space, is the number of point cloud data in the cubic space of the xth point cloud data, is the curvature of the xth point cloud data in each cubic space, is the curvature of the kth point cloud data in the cubic space except the xth point cloud data, is the spatial Euclidean distance between the xth point cloud data and the kth point cloud data except the xth point cloud data in the cubic space.

[0047] Through the above calculation, the curvature position difference of the steel structure member at different point cloud data is obtained. If the point cloud data is in the curved surface structure region of the steel structure surface, the curvature difference between the point cloud data at this position and other positions is larger, and the adjacent point clouds are usually closer. The closer the distance between the two point clouds, the larger the curvature position difference calculated at this time, indicating that the point cloud data in the steel structure member point cloud data space is in the curved surface structure region, and the probability of collision at this time is larger.

[0048] In step S003, based on the curvature position difference of all point cloud data in the three-dimensional point cloud data model, all point cloud data in the three-dimensional point cloud data model is divided into curved surface structure data and planar structure data. The planar structure data is down-sampled, and all point cloud data after down-sampling of the three-dimensional point cloud data model is used as the steel structure member point cloud model.

[0049] By calculating the curvature position difference of each point cloud data in the three-dimensional point cloud data model of the steel structure member, the data state at each point cloud position can be distinguished. Further, the data characteristics at different point cloud positions in the steel structure member are obtained by clustering algorithm.

[0050] In this embodiment, K-Means clustering algorithm is used, K=2 is set, the curvature position difference of all point cloud data in the three-dimensional point cloud data model of the steel structure member is used as output, and two clustering clusters are output, i.e. all point cloud data in the three-dimensional point cloud data model is divided into two categories. The mean value of all elements in each clustering cluster is calculated, all point cloud data corresponding to the elements in the clustering cluster with the maximum mean value are used as curved surface structure data, and all point cloud data corresponding to the elements in the clustering cluster with the minimum mean value are used as planar structure data. The K-Means clustering algorithm is a known technology, and other feasible clustering algorithms can be selected by the implementer, which is not limited in this embodiment.

[0051] Because there are a large number of smooth planes in the steel structure member, there are relatively dense point cloud data on the surface, and these densely distributed point cloud data depict the smooth and flat characteristics of the steel structure surface, and the information amount is relatively single. Therefore, all planar structure data of the steel structure member in the cubic space is processed in this embodiment, the centroid of the planar structure data of each plane of the steel structure member in the cubic space is calculated and obtained, and the centroid is used to replace all planar structure data of the plane, so as to realize the down-sampling processing of the planar structure data.

[0052] At the curved surface position of the steel structure component, since the probability of collision at this position is relatively high, in order to avoid omission of information in the point cloud data after downsampling, this embodiment retains all surface structure data in the three-dimensional point cloud data model of the steel structure component.

[0053] The downsampled three-dimensional point cloud data model is recorded as the steel structure component point cloud model. Downsampling can effectively eliminate the redundant data information in the original point cloud data of the steel structure component, and realize the rapid calculation and analysis of the steel structure component data.

[0054] Step S004, converting the BIM model into a BIM point cloud model of the steel structure component; matching the point cloud model of the steel structure component with the point cloud data in the BIM point cloud model of the steel structure component based on the point cloud feature descriptor of the point cloud data, wherein, during the matching process, the search spaces of the source point cloud and the target point cloud, as well as the spatial position vectors of the source point cloud and the target point cloud and other point cloud data in their respective search spaces are determined respectively; and the point cloud feature descriptor is corrected according to the curvature difference between the source point cloud and the target point cloud, and the similarity between the spatial position vectors of the source point cloud and the target point cloud.

[0055] Typically, the BIM model is built based on the preliminary drawings of the project. However, during the actual construction process, the actual steel structure components may deviate from the design drawings. Therefore, this embodiment aligns the point cloud model of the steel structure components based on the BIM model of the steel structure components. Specifically,

[0056] First, the BIM model of the steel structure component is converted into a BIM point cloud model of the steel structure component, and the BIM point cloud model of the steel structure component and the point cloud model of the steel structure component are rotated to ensure that the two point cloud models are in the same coordinate system. This embodiment uses the engineering construction coordinate system to process the point cloud model.

[0057] Select any point cloud data in the point cloud model of the steel structure component and record it as the source point cloud , get the source point cloud As the center point, the spherical area with the search radius r as the radius is used as the source point cloud Search space; select any point cloud data in the BIM point cloud model of the steel structure component and record it as the target point cloud , get the target point cloud As the center point, the spherical area with the search radius r as the radius is used as the target point cloud In this embodiment, r=32, which can be set by the implementer according to the actual situation, and this embodiment does not impose any restrictions on this.

[0058] Since the steel structure component BIM point cloud model and the steel structure component point cloud model both represent the same steel structure component, the distribution characteristics in the point cloud model are beneficial to the matching accuracy of the two point cloud models of the steel structure component.

[0059] The point cloud feature descriptor PFH (Persistent Feature Histograms) is extracted from the point cloud data in the steel structure component BIM point cloud model and the steel structure component point cloud model respectively. The PFH feature describes the spatial characteristics of the point cloud data by constructing four-dimensional information. In the PFH feature, the first three dimensions represent the angle characteristics of the point cloud data in space, and the last dimension represents the Euclidean distance characteristics of the point cloud data. PFH is a known technology, and the specific process will not be described here. In actual engineering construction, the space occupied by each steel structure component is usually small. At this time, the Euclidean distance feature in the fourth dimension of the PFH feature has no obvious difference in the point cloud data space. Therefore, the fourth dimension feature data in the PFH feature is optimized in this embodiment to ensure that the PFH feature constructed can accurately reflect the spatial physical state of the steel structure component. Specifically:

[0060] For the steel structure component point cloud model, in the search space of the source point cloud , the source point cloud is connected with the remaining point cloud data to obtain the directed line segment of the source point cloud pointing to the remaining point cloud data, as the spatial position vector formed by the source point cloud and the remaining point cloud data. Correspondingly, for the steel structure component BIM point cloud model, in the search space of the target point cloud , the same method is used to obtain the spatial position vector formed by the target point cloud and the remaining point cloud data. Further, the point cloud search feature of the search space with r as the radius is determined, and the specific expression is:

[0061] ; in the formula, is the curvature of the source point cloud in the search space with r as the radius in the steel structure component point cloud model, is the curvature of the target point cloud in the search space with r as the radius in the steel structure component BIM point cloud model, is the spatial position vector of the source point cloud and the jth point cloud data in its search space, is the spatial position vector of the target point cloud and the jth point cloud data in its search space, is the cosine similarity calculation function, is the cosine similarity calculation function, With the target point cloud The minimum value of the point cloud data in the search space with r as radius, is the point cloud search feature of the search space with r as radius. Recorded as the first difference.

[0062] The cosine similarity calculation function measures the and For the similarity between two spatial position vectors, the implementer may select other feasible similarity calculation methods, such as the Pearson correlation coefficient, etc., and this embodiment does not impose any limitation on this.

[0063] In this embodiment, the point cloud search features are used to replace the source point cloud With the target point cloud The Euclidean distance feature of the last dimension in the point cloud feature descriptor greatly avoids the redundant dimensional information in the PFH feature caused by the use of Euclidean distance.

[0064] Using the ICP (Iterative Closest Point) matching algorithm, based on the PFH features after the source point cloud and the target point cloud are replaced, the BIM point cloud model of the steel structure component and the point cloud model of the steel structure component are automatically matched, and the matched point cloud model of the steel structure component is used as the BIM corrected point cloud model of the steel structure component. The ICP matching algorithm is a well-known technology, and the specific process is not described in detail. The flow chart for obtaining the BIM corrected point cloud model of the steel structure component is as follows: Figure 2 shown.

[0065] Step S005: Perform static collision detection on the steel structure component using the matched point cloud model of the steel structure component.

[0066] Finally, static collision detection is performed on the BIM-corrected point cloud model of the steel structure components. Areas without any collision risk are excluded, and the bounding box method is applied to high-risk collision areas. This generates a collision information table for the steel structure components, providing the coordinate information of the collision location. Static collision detection is a well-known technique, and the specific process is not detailed here.

[0067] It should be noted that the order in which the embodiments of the present application are presented is for illustrative purposes only and does not necessarily represent the superiority or inferiority of the embodiments. Furthermore, the foregoing descriptions of specific embodiments of this specification are provided. Furthermore, the processes depicted in the accompanying drawings do not necessarily require the specific order or sequential sequence shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0068] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on the differences from other embodiments.

[0069] The above-described embodiments are only used to illustrate the technical solutions of the present application, but not limit the present application; the technical solutions described in the foregoing embodiments are modified, or some technical features are replaced equivalently, and the essence of the corresponding technical solutions does not deviate from the scope of the technical solutions of the embodiments of the present application, which should be included in the protection scope of the present application.

Claims

1. A BIM-based anti-collision detection method for steel structure components, characterized in that: The method comprises the following steps: Use the design drawings of steel structure components to obtain the BIM model of the steel structure components, collect the 3D point cloud data of the same steel structure components, and obtain the 3D point cloud data model of the steel structure components; The three-dimensional point cloud data model is divided into cubic spaces, and a curvature difference and a distance interval between each point cloud data in each cubic space and the remaining point cloud data are analyzed, a ratio of the curvature difference to the distance interval is calculated, and a curvature position difference of each point cloud data in each cubic space is determined based on the ratio; the curvature position difference is the average of the absolute values ​​of the ratios of each point cloud data in each cubic space to all the remaining point cloud data; Based on the curvature position difference of all point cloud data in the 3D point cloud data model, all point cloud data in the 3D point cloud data model are divided into surface structure data and plane structure data, the plane structure data is downsampled, and all point cloud data after downsampling of the 3D point cloud data model are used as the point cloud model of the steel structure component; The BIM model is converted into a BIM point cloud model of a steel structure component; based on a point cloud feature descriptor of the point cloud data, the point cloud data in the steel structure component point cloud model is used as the source point cloud and the point cloud data in the steel structure component BIM point cloud model is used as the target point cloud, and the steel structure component point cloud model is matched with the point cloud data in the steel structure component BIM point cloud model, wherein, during the matching process, the search spaces of the source point cloud and the target point cloud are respectively determined, as well as the spatial position vectors of the source point cloud and the target point cloud and other point cloud data in their respective search spaces; the point cloud feature descriptor is modified according to the curvature difference between the source point cloud and the target point cloud, and the similarity between the spatial position vectors of the source point cloud and the target point cloud; Static collision detection of steel structure components is performed through the matched point cloud model of the steel structure components.

2. The BIM-based anti-collision detection method for steel structure components according to claim 1, characterized in that: The curvature difference is the absolute value of the difference between the curvature of each point cloud data and the remaining point cloud data.

3. The BIM-based anti-collision detection method for steel structure components according to claim 1, characterized in that: The step of dividing all point cloud data in the three-dimensional point cloud data model into surface structure data and plane structure data includes: Using the clustering algorithm, the curvature position difference of all point cloud data in the three-dimensional point cloud data model is divided into two clusters. The mean of all elements in each cluster is calculated. All point cloud data corresponding to the elements in the cluster with the largest mean is used as surface structure data, and all point cloud data corresponding to the elements in the cluster with the smallest mean is used as plane structure data.

4. The BIM-based anti-collision detection method for steel structure components according to claim 1, characterized in that: The downsampling of the plane structure data includes: Obtain the centroid of the plane structure data belonging to the same plane, and use the centroid to replace the plane structure data.

5. The BIM-based anti-collision detection method for steel structure components according to claim 1, characterized in that: The search space is a spherical area with the source point cloud and the target point cloud as center points and the same preset search radius.

6. The BIM-based anti-collision detection method for steel structure members according to claim 1, characterized in that: The spatial position vector is a directed line segment of the source point cloud and the target point cloud pointing to other point cloud data in their respective search spaces.

7. The BIM-based anti-collision detection method for steel structure members according to claim 1, characterized in that: The modifying the point cloud feature descriptor comprises: Recording the curvature difference between the source point cloud and the target point cloud as a first difference, obtaining the minimum value of the point cloud data in the search space of the source point cloud and the target point cloud, and calculating the product of the first difference and the minimum value; Calculate the similarity between the j-th spatial position vector corresponding to the source point cloud and the j-th spatial position vector corresponding to the target point cloud, determine the ratio of the product to the sum of the minimum value and the similarities, record it as the point cloud search feature, and correct the point cloud feature descriptor based on the point cloud search feature.

8. The BIM-based anti-collision detection method for steel structure members according to claim 7, characterized in that: The last dimension data in the point cloud feature descriptors of the source point cloud and the target point cloud is replaced with the point cloud search feature.

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