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Building local deformation analysis method based on point cloud curved surface feature constraints
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A local deformation and analysis method technology, applied in image analysis, image data processing, measuring devices, etc., can solve the problems of inappropriateness, lowering the accuracy and reliability of building deformation monitoring, and improve the accuracy and reliability Effect
Active Publication Date: 2020-07-03
SHANDONG JIAOTONG UNIV
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For the point-to-point deformation analysis method, it is not sure whether the exact same point can be sampled in two different periods, and the point density on the object may be different from scanning measurements from different angles, so a direct point-to-point comparison is not appropriate of
Based on the calculation method of deformation from point to plane or between planes, although surface characteristic parameters are applied to deformation monitoring, only plane characteristic parameters are used, which cannot meet the needs of deformation monitoring of large and complex buildings
The feature extraction method based on the overall point cloud data of the building, such as the principal component analysis method, only uses mathematical calculation methods to extract the principal component vector of the overall point cloud data. This method still does not consider the constraint relationship between the surfaces of the building, so , nor can it judge the local spatial deformation between various parts of the building
In addition, most of the current surface feature extraction methods based on building point cloud data only use curvature and normal loss vectors to distinguish point cloud data with similar features to achieve point cloud data segmentation. The judgment and recognition of the surface type of point cloud data does not apply the constraint relationship between the building surfaces to the monitoring of building deformation. Therefore, the relative deformation between the various parts of the building is not considered in the deformation analysis of the building. This will reduce the accuracy and reliability of building deformation monitoring
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example 1
[0186] Example 1: Analysis and calculation of bridge cylinder deformation
[0187] Such as Image 6 As shown, in order to detect the constraint relationship between bridge columns and check the construction quality of bridge components, the FOCUS 3D The 3D laserscanner has carried out 3D scanning measurement of the bridge, and selected 5 faces A, B, C, D, E on the side of the bridge, and the corresponding point cloud data are as follows: Figure 7 shown. In order to test the bridge construction quality, we consulted the bridge design data and found that the five surfaces A, B, C, D, and E are designed as planes, and the design constraints among the surfaces are shown in Table 1.
[0188] Table 1 Constraint relationship table of each plane (design)
[0189]
[0190] The obtained point cloud data are processed by registration, noise removal and data segmentation, and the segmented point cloud data are obtained as follows: Figure 7 shown.
[0191] The number of selected...
example 2
[0215] Example 2: Coaxial detection of building load-bearing columns
[0216] In order to test the construction quality of house pillars, select such as Figure 8 The load-bearing column of the house shown, the column is divided into two parts, the lower part C 2 It is the base part of the column, supporting the upper part C 1 . According to the house design data, the lower part C 2 The radius is larger than the upper half of C 1 Large, the design central axes of the two columns are on the same straight line, that is, the design of the two columns is coaxial. Such as Figure 9 As shown, in order to carry out coaxial detection on the pillars of the house, 399057 data points in the cylindrical point cloud data are selected.
[0217] The obtained point cloud data is processed by registration, noiseelimination and data segmentation, and the segmented point cloud data is obtained, and the cylindrical surface fitting model is obtained, such as Figure 10 shown.
[0218] The...
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Abstract
The invention discloses a local deformation analysis method based on building point cloud curved surface feature constraints, and the method comprises the steps: firstly carrying out the preprocessingand data segmentation of obtained building dense point cloud data, and obtaining the point cloud data of each surface of a building; secondly, performing curved surface feature recognition on the segmented point cloud data, judging the curved surface type of the surface of the building, and performing curved surface fitting calculation on the point cloud data according to the curved surface typeto obtain curved surface feature parameters of each surface of the building; then, according to the characteristic parameters of the building curved surface, the size constraint and the structure constraint of the building curved surface are calculated; and finally, judging the spatial position relationship between the building curved surfaces according to the building curved surface feature constraint relationship, and analyzing the local change between the curved surfaces. According to the method, the local deformation conditions of different parts of the building can be judged, the safety of the building is analyzed and evaluated from multiple local angles, and the accuracy and reliability of building deformation analysis are improved.
Description
technical field [0001] The invention specifically relates to a local deformation analysis method based on feature constraints of building point clouds and curved surfaces. Background technique [0002] In recent years, in order to meet the housing needs of urban residents, large buildings with complex structures have emerged continuously, which has also laid hidden dangers for the safety of buildings. Therefore, it is necessary to adopt fast and high-precision building deformation monitoring means to measure the building and obtain the latest deformation information in time. The traditional three-dimensional measurement technology is mainly to measure the three-dimensional coordinate data of one or more discrete positioning points of the target and the three-dimensional characteristics of the point with high precision. This technology can only measure the positioning point data, and measure the simple geometric dimensions between different positioning points. The 3D laser s...
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