A high pier verticality detection method and system

Abstract

The present application relates to the field of engineering surveying, and more particularly to a high bridge pier verticality detection method and system, the method comprising: obtaining a plurality of three-dimensional point cloud sub-data of a plurality of stations of a bridge pier, sequentially splicing adjacent three-dimensional point cloud sub-data based on an ICP algorithm to obtain an initial three-dimensional point cloud model; performing noise reduction segmentation processing on the initial three-dimensional point cloud model to obtain a target three-dimensional point cloud model; dividing the target three-dimensional point cloud model into a plurality of three-dimensional point cloud sub-models, performing point cloud slicing on the three-dimensional point cloud sub-models to obtain a plurality of candidate slicing circles, and determining a target slicing circle corresponding to the three-dimensional point cloud sub-model based on the roundness of the candidate slicing circle; fitting the target slicing circle based on the least squares method to obtain the center of each target slicing circle, calculating the sub-verticality of the three-dimensional point cloud sub-model according to the center of the target slicing circle, and obtaining the target verticality of the bridge pier based on a plurality of sub-verticalities. The present application has small measurement error and high efficiency for the verticality of the bridge pier.

Description

Technical Field

[0001] This invention relates to the field of engineering surveying technology, and in particular to a method and system for detecting the verticality of high bridge piers. Background Technology

[0002] Bridge piers are the main load-bearing components of highways. During vehicle traffic, they exert load pressure on the superstructure of the bridge, and the piers primarily transfer this pressure to the foundation. After the highway is put into use, the piers will withstand significant wind, water pressure, and wave forces, and must resist impacts from ships. Therefore, the construction of bridge piers has a significant impact on the overall quality of the highway, and the quality of construction is crucial to ensuring the safe and stable operation of the highway. The verticality of the bridge piers has become a critical indicator of bridge safety.

[0003] Existing methods for measuring the verticality of piers include the plumb line method, the total station horizontal distance method, and the centroid method. However, these methods are all based on measuring the verticality of a single pier at a given point, which is greatly affected by geographical conditions, weather conditions, and the surrounding environment. Furthermore, the measurement results are closely related to the operator's skill and accuracy. Due to the inconvenience of measurement, the efficiency of the work is low, requiring a significant amount of manpower, resources, and time. Summary of the Invention

[0004] To address the aforementioned technical problems, this invention provides a method and system for detecting the verticality of high bridge piers.

[0005] This invention adopts the following technical solution: a method for detecting the verticality of high bridge piers, the method comprising:

[0006] The three-dimensional point cloud data of the bridge piers is obtained. The three-dimensional point cloud data includes multiple three-dimensional point cloud sub-data corresponding to multiple stations. Based on the improved ICP algorithm, the adjacent three-dimensional point cloud sub-data are stitched together to obtain an initial three-dimensional point cloud model.

[0007] The initial 3D point cloud model is subjected to noise reduction and segmentation processing to obtain the target 3D point cloud model;

[0008] The target 3D point cloud model is divided into multiple 3D point cloud sub-models. The 3D point cloud sub-models are sliced ​​to obtain multiple candidate slice circles. The target slice circle corresponding to the 3D point cloud sub-model is determined based on the roundness of the candidate slice circles.

[0009] The target slice circle is fitted using the least squares method to obtain the center of each target slice circle. The sub-verticality of the three-dimensional point cloud sub-model is calculated based on the center of the target slice circle, and the target verticality of the bridge pier is obtained based on multiple sub-verticalities.

[0010] An embodiment of the high bridge pier verticality detection method of the present invention, based on an improved ICP algorithm, effectively removes erroneous point clouds, reduces the number of point clouds during splicing, and achieves faster splicing speed and better results. Furthermore, by performing noise reduction and segmentation on the initial 3D point cloud model, the resulting target 3D point cloud model has clear boundaries, ensuring smooth slicing and fitting of subsequent models. By obtaining the target slice circle of each 3D point cloud sub-model and calculating the sub-verticality of each 3D point cloud sub-model, the final calculated target verticality can effectively reflect the verticality of the bridge pier, resulting in small measurement errors and high efficiency in bridge pier verticality measurement.

[0011] Furthermore, the process of acquiring 3D point cloud data of the bridge piers, wherein the 3D point cloud data includes multiple 3D point cloud sub-data corresponding to multiple sites, and sequentially stitching together adjacent 3D point cloud sub-data based on the improved ICP algorithm to obtain an initial 3D point cloud model specifically includes:

[0012] Acquire multi-station 3D point cloud data of bridge piers, wherein the 3D point cloud data includes multiple 3D point cloud sub-data corresponding to multiple stations;

[0013] The adjacent 3D point cloud sub-data are stitched together according to a preset overlap degree, and the overlapping area of ​​the adjacent 3D point cloud sub-data is searched according to the KD tree algorithm to obtain co-location cloud set A and co-location cloud set B.

[0014] Calculate the Euclidean distance s between any pair of point clouds in the co-location cloud set A and the co-location cloud set B. i Set the distance error at the same location, and then set the Euclidean distance s. i Point cloud pairs with a distance error greater than the specified co-location distance are removed from co-location cloud set A and co-location cloud set B;

[0015] The distance error at the same location is:

[0016]

[0017] Among them, s i Let n be the Euclidean distance between the i-th pair of point clouds in the co-location cloud set A and the co-location cloud set B, and let n be the total number of point cloud pairs formed by the co-location cloud set A and the co-location cloud set B. The error coefficient;

[0018] Based on the ICP algorithm, a transformation matrix is ​​calculated according to the co-location cloud set A and the co-location cloud set B. The transformation matrix is ​​applied to the co-location cloud set B to obtain a new point cloud set B'. The average distance between any point cloud in the new point cloud set B' and all point clouds in the co-location cloud set A is calculated. A distance threshold is set for iterative stitching, thereby completing the stitching of adjacent 3D point cloud sub-data to obtain an initial 3D point cloud model.

[0019] Furthermore, the step of performing noise reduction and segmentation processing on the initial 3D point cloud model to obtain the target 3D point cloud model specifically includes:

[0020] The initial 3D point cloud model is coarsely trimmed to remove point cloud data outside the bridge piers;

[0021] The initial 3D point cloud model after coarse cropping is denoised using statistical denoising methods, with the deviation factor set to 0.05.

[0022] The initial 3D point cloud model after noise reduction is segmented based on the model fitting segmentation method to obtain the target 3D point cloud model.

[0023] Furthermore, the steps of dividing the target 3D point cloud model into multiple 3D point cloud sub-models, slicing the 3D point cloud sub-models to obtain multiple candidate slice circles, and determining the target slice circle corresponding to the 3D point cloud sub-model based on the roundness of the candidate slice circles specifically include:

[0024] The target 3D point cloud model is divided into multiple 3D point cloud sub-models according to a preset first spacing.

[0025] The 3D point cloud sub-model is sliced ​​laterally according to a preset second spacing to obtain multiple candidate slice circles, and the roundness O of the candidate slice circles is calculated. jk :

[0026]

[0027] Among them, O jk The roundness of the k-th candidate slice circle corresponding to the j-th 3D point cloud sub-model. The radius of the circumcircle of the k-th candidate slice circle corresponding to the j-th 3D point cloud sub-model is... R is the inscribed circle radius of the k-th candidate slice circle corresponding to the j-th three-dimensional point cloud sub-model, and R is the design radius of the bridge pier.

[0028] Let roundness O jk The largest candidate slice circle is taken as the target slice circle corresponding to the j-th three-dimensional point cloud sub-model.

[0029] Furthermore, the steps of fitting the target slice circles using the least squares method to obtain the center of each target slice circle, calculating the sub-verticality of the three-dimensional point cloud sub-model based on the center of the target slice circles, and obtaining the target verticality of the bridge pier based on multiple sub-verticalities specifically include:

[0030] The center of each target slice circle is obtained by fitting the target slice circle using the least squares method.

[0031] The tilt angle θ of the three-dimensional point cloud sub-model is calculated based on the center of the target slice circle. j :

[0032]

[0033] Among them, (x j ,y j ,z j (x0, y0, z0) represents the center coordinates of the target slice circle corresponding to the j-th 3D point cloud sub-model, and (x0, y0, z0) represents the center coordinates of the bottom circle of the 3D point cloud model.

[0034] The sub-verticality l of the 3D point cloud sub-model is calculated based on its tilt. j :

[0035] l j =h j ×tanθ j

[0036] Among them, l j h is the sub-verticality of the j-th 3D point cloud sub-model. j The height of the j-th 3D point cloud sub-model;

[0037] The target verticality L of the bridge pier is calculated based on the sub-verticality of multiple three-dimensional point cloud sub-models:

[0038]

[0039] Where m is the number of the three-dimensional point cloud sub-models.

[0040] The present invention also proposes a high bridge pier verticality detection system, the system comprising:

[0041] The acquisition module is used to acquire three-dimensional point cloud data of bridge piers. The three-dimensional point cloud data includes multiple three-dimensional point cloud sub-data corresponding to multiple stations. Based on the improved ICP algorithm, the adjacent three-dimensional point cloud sub-data are stitched together to obtain an initial three-dimensional point cloud model.

[0042] The segmentation module is used to perform noise reduction and segmentation processing on the initial 3D point cloud model to obtain the target 3D point cloud model.

[0043] The slicing module is used to divide the target 3D point cloud model into multiple 3D point cloud sub-models, slice the 3D point cloud sub-models to obtain multiple candidate slice circles, and determine the target slice circle corresponding to the 3D point cloud sub-model based on the roundness of the candidate slice circles.

[0044] The calculation module is used to fit the target slice circle based on the least squares method to obtain the center of each target slice circle, calculate the sub-verticality of the three-dimensional point cloud sub-model based on the center of the target slice circle, and obtain the target verticality of the bridge pier based on multiple sub-verticalities.

[0045] An embodiment of the high bridge pier verticality detection system of the present invention, based on an improved ICP algorithm, effectively removes erroneous point clouds, reduces the number of point clouds during splicing, and achieves faster splicing speed and better results. Furthermore, by performing noise reduction and segmentation on the initial 3D point cloud model, the resulting target 3D point cloud model has clear boundaries, ensuring smooth slicing and fitting of subsequent models. By acquiring the target slice circle of each 3D point cloud sub-model and calculating the sub-verticality of each 3D point cloud sub-model, the final calculated target verticality can effectively reflect the verticality of the bridge pier, resulting in small measurement errors and high efficiency in bridge pier verticality measurement.

[0046] Furthermore, the acquisition module is specifically used for:

[0047] The steps of acquiring 3D point cloud data of bridge piers, wherein the 3D point cloud data includes multiple 3D point cloud sub-data corresponding to multiple sites, and sequentially stitching together adjacent 3D point cloud sub-data based on an improved ICP algorithm to obtain an initial 3D point cloud model, specifically include:

[0048] Acquire multi-station 3D point cloud data of bridge piers, wherein the 3D point cloud data includes multiple 3D point cloud sub-data corresponding to multiple stations;

[0049] The adjacent 3D point cloud sub-data are stitched together according to a preset overlap degree, and the overlapping area of ​​the adjacent 3D point cloud sub-data is searched according to the KD tree algorithm to obtain co-location cloud set A and co-location cloud set B.

[0050] Calculate the Euclidean distance s between any pair of point clouds in the co-location cloud set A and the co-location cloud set B. i Set the distance error at the same location, and then set the Euclidean distance s. i Point cloud pairs with a distance error greater than the specified co-location distance are removed from co-location cloud set A and co-location cloud set B;

[0051] The distance error at the same location is:

[0052]

[0053] Among them, s i Let n be the Euclidean distance between the i-th pair of point clouds in the co-location cloud set A and the co-location cloud set B, and let n be the total number of point cloud pairs formed by the co-location cloud set A and the co-location cloud set B. The error coefficient;

[0054] Based on the ICP algorithm, a transformation matrix is ​​calculated according to the co-location cloud set A and the co-location cloud set B. The transformation matrix is ​​applied to the co-location cloud set B to obtain a new point cloud set B'. The average distance between any point cloud in the new point cloud set B' and all point clouds in the co-location cloud set A is calculated. A distance threshold is set for iterative stitching, thereby completing the stitching of adjacent 3D point cloud sub-data to obtain an initial 3D point cloud model.

[0055] Furthermore, the segmentation module is specifically used for:

[0056] The initial 3D point cloud model is coarsely trimmed to remove point cloud data outside the bridge piers;

[0057] The initial 3D point cloud model after coarse cropping is denoised using statistical denoising methods, with the deviation factor set to 0.05.

[0058] The initial 3D point cloud model after noise reduction is segmented based on the model fitting segmentation method to obtain the target 3D point cloud model.

[0059] Furthermore, the slicing module is specifically used for:

[0060] The target 3D point cloud model is divided into multiple 3D point cloud sub-models according to a preset first spacing.

[0061] The 3D point cloud sub-model is sliced ​​laterally according to a preset second spacing to obtain multiple candidate slice circles, and the roundness O of the candidate slice circles is calculated. jk :

[0062]

[0063] Among them, O jk The roundness of the k-th candidate slice circle corresponding to the j-th 3D point cloud sub-model. The radius of the circumcircle of the k-th candidate slice circle corresponding to the j-th 3D point cloud sub-model is... R is the inscribed circle radius of the k-th candidate slice circle corresponding to the j-th three-dimensional point cloud sub-model, and R is the design radius of the bridge pier.

[0064] Let roundness O jk The largest candidate slice circle is taken as the target slice circle corresponding to the j-th three-dimensional point cloud sub-model.

[0065] Furthermore, the computing module is specifically used for:

[0066] The center of each target slice circle is obtained by fitting the target slice circle using the least squares method.

[0067] The tilt angle θ of the three-dimensional point cloud sub-model is calculated based on the center of the target slice circle. j :

[0068]

[0069] Among them, (x j ,y j ,z j (x0, y0, z0) represents the center coordinates of the target slice circle corresponding to the j-th 3D point cloud sub-model, and (x0, y0, z0) represents the center coordinates of the bottom circle of the 3D point cloud model.

[0070] The sub-verticality l of the 3D point cloud sub-model is calculated based on its tilt. j :

[0071] l j =h j ×tanθ j

[0072] Among them, l j h is the sub-verticality of the j-th 3D point cloud sub-model. j The height of the j-th 3D point cloud sub-model;

[0073] The target verticality L of the bridge pier is calculated based on the sub-verticality of multiple three-dimensional point cloud sub-models:

[0074]

[0075] Where m is the number of the three-dimensional point cloud sub-models. Attached Figure Description

[0076] To more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0077] Figure 1This is a schematic diagram of the high bridge pier verticality detection method of the present invention;

[0078] Figure 2 This is a structural block diagram of the high bridge pier verticality detection method of the present invention. Detailed Implementation

[0079] Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain embodiments of the present invention, and should not be construed as limiting the present invention.

[0080] In the description of the embodiments of the present invention, it should be understood that the terms "length", "width", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing the embodiments of the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the present invention.

[0081] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of embodiments of the present invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0082] In the embodiments of the present invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," "linking," "fixing," etc., should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. Those skilled in the art can understand the specific meaning of the above terms in the embodiments of the present invention according to the specific circumstances.

[0083] Example 1

[0084] Reference Figure 1 According to the first embodiment of the present invention, a method for detecting the verticality of high bridge piers includes:

[0085] S1: Obtain the 3D point cloud data of the bridge piers. The 3D point cloud data includes multiple 3D point cloud sub-data corresponding to multiple sites. Based on the improved ICP algorithm, adjacent 3D point cloud sub-data are sequentially stitched together to obtain an initial 3D point cloud model. Further, the specific steps include:

[0086] Acquire multi-station 3D point cloud data of bridge piers, wherein the 3D point cloud data includes multiple 3D point cloud sub-data corresponding to multiple stations;

[0087] Adjacent 3D point cloud sub-data are stitched together according to a preset overlap, and the overlapping area of ​​adjacent 3D point cloud sub-data is searched according to the KD tree algorithm to obtain co-location cloud set A and co-location cloud set B.

[0088] Calculate the Euclidean distance s between any pair of point clouds in co-location cloud set A and co-location cloud set B. i Set the distance error at the same point and convert the Euclidean distance s i Point cloud pairs with a distance error greater than that of the same location are removed from the same location cloud set A and the same location cloud set B;

[0089] The distance error at the same location is:

[0090]

[0091] Among them, s i Let be the Euclidean distance between the i-th point cloud pairs in co-location cloud set A and co-location cloud set B, and let n be the total number of point cloud pairs formed by co-location cloud set A and co-location cloud set B. The error coefficient;

[0092] Based on the ICP algorithm, a transformation matrix is ​​calculated based on the same point cloud set A and the same point cloud set B. The transformation matrix is ​​applied to the same point cloud set B to obtain a new point cloud set B'. The average distance between any point cloud in the new point cloud set B' and all point clouds in the same point cloud set A is calculated. A distance threshold is set for iterative stitching, thereby completing the stitching of adjacent 3D point cloud sub-data to obtain the initial 3D point cloud model.

[0093] In this embodiment, the principles and steps of the KD-tree algorithm and the ICP algorithm are well known to those skilled in the art, and therefore will not be described in detail here.

[0094] S2: Perform noise reduction and segmentation processing on the initial 3D point cloud model to obtain the target 3D point cloud model; further, the specific steps include:

[0095] The initial 3D point cloud model is coarsely trimmed to remove point cloud data outside the bridge piers;

[0096] The initial 3D point cloud model after coarse cropping is denoised using statistical denoising method, with the deviation factor set to 0.05. In this embodiment, the principle and steps of statistical denoising method are well known to those skilled in the art, and therefore will not be described in detail here.

[0097] The initial 3D point cloud model after noise reduction is segmented based on the model fitting segmentation method to obtain the target 3D point cloud model.

[0098] S3: Divide the target 3D point cloud model into multiple 3D point cloud sub-models, slice the 3D point cloud sub-models to obtain multiple candidate slice circles, and determine the target slice circle corresponding to the 3D point cloud sub-model based on the roundness of the candidate slice circles; further, the specific steps include:

[0099] The target 3D point cloud model is divided into multiple 3D point cloud sub-models according to the preset first spacing; the size of the first spacing is proportional to the height of the bridge pier. The higher the bridge pier, the larger the first spacing, and the lower the bridge pier, the smaller the first spacing.

[0100] The 3D point cloud sub-model is sliced ​​horizontally according to the preset second spacing to obtain multiple candidate slice circles. The roundness O of the candidate slice circles is then calculated. jk :

[0101]

[0102] Among them, O jk Let $\frac{j}{k}$ be the roundness of the k-th candidate slice circle corresponding to the j-th 3D point cloud sub-model. Let be the radius of the circumcircle of the k-th candidate slice circle corresponding to the j-th 3D point cloud sub-model. R is the inscribed circle radius of the k-th candidate slice circle corresponding to the j-th 3D point cloud sub-model, and R is the design radius of the bridge pier.

[0103] The size of the second spacing is directly proportional to the size of the first spacing. The larger the first spacing, the larger the second spacing; the smaller the first spacing, the smaller the second spacing.

[0104] Let roundness O jk The largest candidate slice circle is taken as the target slice circle corresponding to the j-th 3D point cloud sub-model.

[0105] S4: Fit the target slice circles using the least squares method to obtain the center of each target slice circle. Calculate the sub-verticality of the 3D point cloud sub-model based on the center of each target slice circle, and obtain the target verticality of the bridge pier based on multiple sub-verticalities. Further, the specific steps include:

[0106] The center of each target slice circle is obtained by fitting the target slice circle using the least squares method.

[0107] Calculate the tilt θ of the 3D point cloud sub-model based on the center of the target slice circle. j :

[0108]

[0109] Among them, (x j ,y j ,z j (x0, y0, z0) represents the center coordinates of the target slice circle corresponding to the j-th 3D point cloud sub-model, and (x0, y0, z0) represents the center coordinates of the bottom circle of the 3D point cloud model. In this embodiment, the center coordinates of the bottom circle of the 3D point cloud model are obtained by fitting using the least squares method. The principle and steps of the least squares method are well known to those skilled in the art, so they will not be described in detail here.

[0110] Calculate the sub-verticality l of the 3D point cloud sub-model based on its tilt. j :

[0111] l j =h j ×tanθ j

[0112] Among them, l j h is the sub-verticality of the j-th 3D point cloud sub-model. j Let the height be the height of the j-th 3D point cloud sub-model;

[0113] The target verticality L of the bridge pier is calculated based on the sub-verticality of multiple 3D point cloud sub-models:

[0114]

[0115] Where m is the number of 3D point cloud sub-models.

[0116] An embodiment of the high bridge pier verticality detection method of the present invention, based on an improved ICP algorithm, effectively removes erroneous point clouds, reduces the number of point clouds during splicing, and achieves faster splicing speed and better results. Furthermore, by performing noise reduction and segmentation on the initial 3D point cloud model, the resulting target 3D point cloud model has clear boundaries, ensuring smooth slicing and fitting of subsequent models. By obtaining the target slice circle of each 3D point cloud sub-model and calculating the sub-verticality of each 3D point cloud sub-model, the final calculated target verticality can effectively reflect the verticality of the bridge pier, resulting in small measurement errors and high efficiency in bridge pier verticality measurement.

[0117] Example 2

[0118] Reference Figure 2 The second embodiment of the present invention proposes a high bridge pier verticality detection system, the system comprising:

[0119] The acquisition module is used to acquire the three-dimensional point cloud data of the bridge piers. The three-dimensional point cloud data includes multiple three-dimensional point cloud sub-data corresponding to multiple sites. Based on the improved ICP algorithm, the adjacent three-dimensional point cloud sub-data are stitched together to obtain the initial three-dimensional point cloud model.

[0120] The segmentation module is used to perform noise reduction and segmentation processing on the initial 3D point cloud model to obtain the target 3D point cloud model.

[0121] The slicing module is used to divide the target 3D point cloud model into multiple 3D point cloud sub-models, slice the 3D point cloud sub-models to obtain multiple candidate slice circles, and determine the target slice circle corresponding to the 3D point cloud sub-model based on the roundness of the candidate slice circles.

[0122] The calculation module is used to fit the target slice circle based on the least squares method to obtain the center of each target slice circle, calculate the sub-verticality of the three-dimensional point cloud sub-model based on the center of the target slice circle, and obtain the target verticality of the bridge pier based on multiple sub-verticalities.

[0123] Furthermore, the acquisition module is specifically used for:

[0124] The steps for obtaining 3D point cloud data of bridge piers, including multiple 3D point cloud sub-data corresponding to multiple sites, and sequentially stitching adjacent 3D point cloud sub-data based on an improved ICP algorithm to obtain an initial 3D point cloud model, specifically include:

[0125] Acquire multi-station 3D point cloud data of bridge piers, wherein the 3D point cloud data includes multiple 3D point cloud sub-data corresponding to multiple stations;

[0126] Adjacent 3D point cloud sub-data are stitched together according to a preset overlap, and the overlapping area of ​​adjacent 3D point cloud sub-data is searched according to the KD tree algorithm to obtain co-location cloud set A and co-location cloud set B.

[0127] Calculate the Euclidean distance s between any pair of point clouds in co-location cloud set A and co-location cloud set B. i Set the distance error at the same point and convert the Euclidean distance s i Point cloud pairs with a distance error greater than that of the same location are removed from the same location cloud set A and the same location cloud set B;

[0128] The distance error at the same location is:

[0129]

[0130] Among them, s i Let be the Euclidean distance between the i-th point cloud pairs in co-location cloud set A and co-location cloud set B, and let n be the total number of point cloud pairs formed by co-location cloud set A and co-location cloud set B. The error coefficient;

[0131] Based on the ICP algorithm, a transformation matrix is ​​calculated using co-location cloud A and co-location cloud B. This transformation matrix is ​​then applied to co-location cloud B to obtain a new cloud B'. The new cloud B' is then calculated. ` The average distance between any point cloud in the set and all point clouds in the same point cloud set A is calculated, and a distance threshold is set for iterative stitching to complete the stitching of adjacent 3D point cloud sub-data, thus obtaining the initial 3D point cloud model.

[0132] Furthermore, the segmentation module is specifically used for:

[0133] The initial 3D point cloud model is coarsely trimmed to remove point cloud data outside the bridge piers;

[0134] The initial 3D point cloud model after coarse cropping was denoised using statistical denoising methods, with the deviation factor set to 0.05.

[0135] The initial 3D point cloud model after noise reduction is segmented based on the model fitting segmentation method to obtain the target 3D point cloud model.

[0136] Furthermore, the slicing module is specifically used for:

[0137] The target 3D point cloud model is divided into multiple 3D point cloud sub-models according to the preset first spacing.

[0138] The 3D point cloud sub-model is sliced ​​horizontally according to the preset second spacing to obtain multiple candidate slice circles. The roundness O of the candidate slice circles is then calculated. jk :

[0139]

[0140] Among them, O jk Let $\frac{j}{k}$ be the roundness of the k-th candidate slice circle corresponding to the j-th 3D point cloud sub-model. Let be the radius of the circumcircle of the k-th candidate slice circle corresponding to the j-th 3D point cloud sub-model. R is the inscribed circle radius of the k-th candidate slice circle corresponding to the j-th 3D point cloud sub-model, and R is the design radius of the bridge pier.

[0141] Let roundness O jk The largest candidate slice circle is taken as the target slice circle corresponding to the j-th 3D point cloud sub-model.

[0142] Furthermore, the calculation module is specifically used for:

[0143] The center of each target slice circle is obtained by fitting the target slice circle using the least squares method.

[0144] Calculate the tilt θ of the 3D point cloud sub-model based on the center of the target slice circle.j :

[0145]

[0146] Among them, (x j ,y j ,z j (x0, y0, z0) represents the center coordinates of the target slice circle corresponding to the j-th 3D point cloud sub-model, and (x0, y0, z0) represents the center coordinates of the bottom circle of the 3D point cloud model.

[0147] Calculate the sub-verticality l of the 3D point cloud sub-model based on its tilt. j :

[0148] l j =h j ×tanθ j

[0149] Among them, l j h is the sub-verticality of the j-th 3D point cloud sub-model. j Let the height be the height of the j-th 3D point cloud sub-model;

[0150] The target verticality L of the bridge pier is calculated based on the sub-verticality of multiple 3D point cloud sub-models:

[0151]

[0152] Where m is the number of 3D point cloud sub-models.

[0153] An embodiment of the high bridge pier verticality detection system of the present invention, based on an improved ICP algorithm, effectively removes erroneous point clouds, reduces the number of point clouds during splicing, and achieves faster splicing speed and better results. Furthermore, by performing noise reduction and segmentation on the initial 3D point cloud model, the resulting target 3D point cloud model has clear boundaries, ensuring smooth slicing and fitting of subsequent models. By acquiring the target slice circle of each 3D point cloud sub-model and calculating the sub-verticality of each 3D point cloud sub-model, the final calculated target verticality can effectively reflect the verticality of the bridge pier, resulting in small measurement errors and high efficiency in bridge pier verticality measurement.

[0154] Example 3

[0155] In the third embodiment of the present invention, based on the same inventive concept, the present invention proposes a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the high bridge pier verticality detection method of the above embodiments.

[0156] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that contains storage, communication, propagation, or transmission programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0157] More specific examples of computer-readable media (a non-exhaustive list) include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0158] The memory may include a large-capacity storage device for data or instructions. For example, and not limitingly, the memory may include a hard disk drive (HDD), a floppy disk drive, a solid-state drive (SSD), flash memory, an optical disk drive, a magneto-optical disk drive, magnetic tape, or a Universal Serial Bus (USB) drive, or a combination of two or more of these. Where appropriate, the memory may include removable or non-removable (or fixed) media. Where appropriate, the memory may be internal or external to the data processing device. In a particular embodiment, the memory is non-volatile memory. In a particular embodiment, the memory includes read-only memory (ROM) and random access memory (RAM). Where appropriate, the ROM may be a mask-programmed ROM, a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), an electrically alterable read-only memory (EAROM), or flash memory, or a combination of two or more of these. Where appropriate, the RAM can be Static Random-Access Memory (SRAM) or Dynamic Random-Access Memory (DRAM). DRAM can be Fast Page Mode Dynamic Random-Access Memory (FPMDRAM), Extended Data Out Dynamic Random-Access Memory (EDODRAM), Synchronous Dynamic Random-Access Memory (SDRAM), etc.

[0159] Example 4

[0160] In the fourth embodiment of the present invention, based on the same inventive concept, the present invention proposes a terminal, the terminal comprising: a processor and a memory; the processor and the memory communicate with each other; the memory is used to store instructions; the processor is used to execute the instructions in the memory to execute the high bridge pier verticality detection method of the above embodiment.

[0161] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0162] In the description of this specification, references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0163] Without causing conflict, those skilled in the art can freely combine and use the above-mentioned additional technical features.

[0164] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for detecting the verticality of high bridge piers, characterized in that, The method includes: The three-dimensional point cloud data of the bridge piers is obtained. The three-dimensional point cloud data includes multiple three-dimensional point cloud sub-data corresponding to multiple stations. Based on the improved ICP algorithm, the adjacent three-dimensional point cloud sub-data are stitched together to obtain an initial three-dimensional point cloud model. The initial 3D point cloud model is subjected to noise reduction and segmentation processing to obtain the target 3D point cloud model; The target 3D point cloud model is divided into multiple 3D point cloud sub-models. The 3D point cloud sub-models are sliced ​​to obtain multiple candidate slice circles. The target slice circle corresponding to the 3D point cloud sub-model is determined based on the roundness of the candidate slice circles. The target slice circle is fitted using the least squares method to obtain the center of each target slice circle. The sub-verticality of the three-dimensional point cloud sub-model is calculated based on the center of the target slice circle, and the target verticality of the bridge pier is obtained based on multiple sub-verticalities. The steps of acquiring 3D point cloud data of bridge piers, wherein the 3D point cloud data includes multiple 3D point cloud sub-data corresponding to multiple sites, and sequentially stitching together adjacent 3D point cloud sub-data based on an improved ICP algorithm to obtain an initial 3D point cloud model, specifically include: Acquire multi-station 3D point cloud data of bridge piers, wherein the 3D point cloud data includes multiple 3D point cloud sub-data corresponding to multiple stations; The adjacent 3D point cloud sub-data are stitched together according to a preset overlap degree, and the overlapping region of the adjacent 3D point cloud sub-data is searched according to the KD tree algorithm to obtain the same point cloud set. and same site cluster ; Calculate the co-location cloud The same site cluster Euclidean distance of any pair of point clouds Set the same point distance error, and use the Euclidean distance Point cloud pairs with a distance error greater than the stated co-location distance are from the co-location cloud set. and the aforementioned co-location cluster Remove from the middle; The distance error at the same location is: in, For the same site cloud The same site cluster The Middle The Euclidean distance between the point cloud pairs described in the group. For the same site cloud The same site cluster The total number of point cloud pairs that make up the total number of points cloud pairs. The error coefficient; Based on the ICP algorithm, according to the co-location cloud The same site cluster Calculate the transformation matrix and apply it to the co-location cloud. Get new points gathering Calculate the new point cloud Any point cloud within the same location and the corresponding point cloud set The average distance of all point clouds in the data is calculated, and a distance threshold is set for iterative stitching to complete the stitching of adjacent 3D point cloud sub-data to obtain an initial 3D point cloud model. The steps of dividing the target 3D point cloud model into multiple 3D point cloud sub-models, slicing the 3D point cloud sub-models to obtain multiple candidate slice circles, and determining the target slice circle corresponding to the 3D point cloud sub-model based on the roundness of the candidate slice circles specifically include: The target 3D point cloud model is divided into multiple 3D point cloud sub-models according to a preset first spacing. The 3D point cloud sub-model is sliced ​​laterally according to a preset second spacing to obtain multiple candidate slice circles, and the roundness of the candidate slice circles is calculated. : in, For the first The first corresponding to the three-dimensional point cloud sub-model The roundness of the selected slice circles For the first The first corresponding to the three-dimensional point cloud sub-model The circumradius of the selected slice circle. For the first The first corresponding to the three-dimensional point cloud sub-model The inscribed radius of the selected slice circle. The design radius of the bridge pier; Roundness The largest of the candidate slice circles is used as the first... The target slice circle corresponding to the three-dimensional point cloud sub-model.

2. The method for detecting the verticality of high bridge piers according to claim 1, characterized in that, The steps of performing noise reduction and segmentation processing on the initial 3D point cloud model to obtain the target 3D point cloud model specifically include: The initial 3D point cloud model is coarsely trimmed to remove point cloud data outside the bridge piers; The initial 3D point cloud model after coarse cropping is denoised using statistical denoising methods, with the deviation factor set to 0.

05. The initial 3D point cloud model after noise reduction is segmented based on the model fitting segmentation method to obtain the target 3D point cloud model.

3. The method for detecting the verticality of high bridge piers according to claim 1, characterized in that, The steps of fitting the target slice circles using the least squares method to obtain the center of each target slice circle, calculating the sub-verticality of the 3D point cloud sub-model based on the center of the target slice circles, and obtaining the target verticality of the bridge pier based on multiple sub-verticalities specifically include: The center of each target slice circle is obtained by fitting the target slice circle using the least squares method. The tilt of the 3D point cloud sub-model is calculated based on the center of the target slice circle. : in, For the first The center coordinates of the target slice circle corresponding to each of the three-dimensional point cloud sub-models. The coordinates of the center of the circle at the bottom of the three-dimensional point cloud model are given. The sub-verticality of the 3D point cloud sub-model is calculated based on its tilt. : in, For the first The sub-verticality of the aforementioned 3D point cloud sub-model For the first The height of the three-dimensional point cloud sub-model; The target verticality of the bridge pier is calculated based on the sub-verticality of multiple three-dimensional point cloud sub-models. : in, The number of the three-dimensional point cloud sub-models.

4. A system for detecting the verticality of high bridge piers, characterized in that, The system includes: The acquisition module is used to acquire three-dimensional point cloud data of bridge piers. The three-dimensional point cloud data includes multiple three-dimensional point cloud sub-data corresponding to multiple stations. Based on the improved ICP algorithm, the adjacent three-dimensional point cloud sub-data are stitched together to obtain an initial three-dimensional point cloud model. The segmentation module is used to perform noise reduction and segmentation processing on the initial 3D point cloud model to obtain the target 3D point cloud model. The slicing module is used to divide the target 3D point cloud model into multiple 3D point cloud sub-models, slice the 3D point cloud sub-models to obtain multiple candidate slice circles, and determine the target slice circle corresponding to the 3D point cloud sub-model based on the roundness of the candidate slice circles. The calculation module is used to fit the target slice circle based on the least squares method to obtain the center of each target slice circle, calculate the sub-verticality of the three-dimensional point cloud sub-model based on the center of the target slice circle, and obtain the target verticality of the bridge pier based on multiple sub-verticalities. The acquisition module is specifically used for: The steps of acquiring 3D point cloud data of bridge piers, wherein the 3D point cloud data includes multiple 3D point cloud sub-data corresponding to multiple sites, and sequentially stitching together adjacent 3D point cloud sub-data based on an improved ICP algorithm to obtain an initial 3D point cloud model, specifically include: Acquire multi-station 3D point cloud data of bridge piers, wherein the 3D point cloud data includes multiple 3D point cloud sub-data corresponding to multiple stations; The adjacent 3D point cloud sub-data are stitched together according to a preset overlap degree, and the overlapping region of the adjacent 3D point cloud sub-data is searched according to the KD tree algorithm to obtain the same point cloud set. and same site cluster ; Calculate the co-location cloud The same site cluster Euclidean distance of any pair of point clouds Set the same point distance error, and use the Euclidean distance Point cloud pairs with a distance error greater than the stated co-location distance are from the co-location cloud set. and the aforementioned co-location cluster Remove from the middle; The distance error at the same location is: in, For the same site cloud The same site cluster The Middle The Euclidean distance between the point cloud pairs described in the group. For the same site cloud The same site cluster The total number of point cloud pairs that make up the total number of points cloud pairs. The error coefficient; Based on the ICP algorithm, according to the co-location cloud The same site cluster Calculate the transformation matrix and apply it to the co-location cloud. Get new points gathering Calculate the new point cloud Any point cloud within the same location and the corresponding point cloud set The average distance of all point clouds in the data is calculated, and a distance threshold is set for iterative stitching to complete the stitching of adjacent 3D point cloud sub-data to obtain an initial 3D point cloud model. The slicing module is specifically used for: The target 3D point cloud model is divided into multiple 3D point cloud sub-models according to a preset first spacing. The 3D point cloud sub-model is sliced ​​laterally according to a preset second spacing to obtain multiple candidate slice circles, and the roundness of the candidate slice circles is calculated. : in, For the first The first corresponding to the three-dimensional point cloud sub-model The roundness of the selected slice circles For the first The first corresponding to the three-dimensional point cloud sub-model The circumradius of the selected slice circle. For the first The first corresponding to the three-dimensional point cloud sub-model The inscribed radius of the selected slice circle. The design radius of the bridge pier; Roundness The largest of the candidate slice circles is used as the first... The target slice circle corresponding to the three-dimensional point cloud sub-model.

5. The high bridge pier verticality detection system according to claim 4, characterized in that, The segmentation module is specifically used for: The initial 3D point cloud model is coarsely trimmed to remove point cloud data outside the bridge piers; The initial 3D point cloud model after coarse cropping is denoised using statistical denoising methods, with the deviation factor set to 0.

05. The initial 3D point cloud model after noise reduction is segmented based on the model fitting segmentation method to obtain the target 3D point cloud model.

6. The high bridge pier verticality detection system according to claim 4, characterized in that, The calculation module is specifically used for: The center of each target slice circle is obtained by fitting the target slice circle using the least squares method. The tilt of the 3D point cloud sub-model is calculated based on the center of the target slice circle. : in, For the first The center coordinates of the target slice circle corresponding to each of the three-dimensional point cloud sub-models. The coordinates of the center of the circle at the bottom of the three-dimensional point cloud model are given. The sub-verticality of the 3D point cloud sub-model is calculated based on its tilt. : in, For the first The sub-verticality of the aforementioned 3D point cloud sub-model For the first The height of the three-dimensional point cloud sub-model; The target verticality of the bridge pier is calculated based on the sub-verticality of multiple three-dimensional point cloud sub-models. : in, The number of the three-dimensional point cloud sub-models.

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