A three-dimensional reconstruction method and device for missing power line point cloud data

Abstract

This invention relates to the field of 3D modeling technology for power lines, and discloses a method and apparatus for 3D reconstruction of missing power line point cloud data. The method involves acquiring 3D point cloud data of power lines, performing voxel sampling and ground point identification to obtain target points for the power line, fitting these points to generate a basic 3D catenary model, and supplementing these with interior points to obtain an initial 3D power line model. Multiple initial 3D power line models are obtained by repeating this process a set number of times, and the model with the most interior points is selected as the target power line model. All interior points of this target model are used as the overall feature vector for calculation; if a preset threshold is met, it is considered a valid power line model. The cross-sectional model of the power line is calculated and integrated with the target power line model to obtain a 3D catenary model. This invention achieves accurate segmentation and reconstruction of power line point cloud data, significantly improving the accuracy and efficiency of power line extraction, especially improving the accuracy of extracting and reconstructing broken power line point cloud data, and enhancing the realism of the modeling.

Description

Technical Field

[0001] This invention relates to the field of three-dimensional modeling technology for power lines, and in particular to a method and apparatus for three-dimensional reconstruction of missing power line point cloud data. Background Technology

[0002] With the rapid development of society and the economy, the demand for electricity in people's lives has grown rapidly. In 2024, the total electricity consumption of the whole society increased by 6.8% year-on-year, reaching 9.8521 trillion kilowatt-hours. Behind the rapid growth in electricity demand, higher requirements have also been placed on the stable transmission of power from the power grid. Digital inspection of the power grid is an important means of maintaining the safe operation of the power grid. In order to achieve large-scale power grid inspection, the three-dimensional point cloud scanning of the power grid channel is carried out by the Unmanned Aerial Vehicle (UAV) and LiDAR (Light Detection and Ranging) system. This technology can obtain the three-dimensional spatial coordinates of the targets on the power grid line, so as to carry out high-efficiency, high-precision and low-cost inspection of the power grid channel. The collected three-dimensional point cloud data is classified and reconstructed. Power grid channel inspection refers to the calculation of the three-dimensional spatial relationship between the ground objects such as trees, bamboo, and buildings and the power lines through three-dimensional point cloud data, timely detection of hidden dangers within the safe distance of the power lines, cutting down hidden danger vegetation and relocating hidden danger buildings to avoid safety accidents such as fires and power outages caused by hidden dangers being too close to the power lines. Furthermore, through classification and 3D reconstruction, the 3D point cloud data of the power grid can be used to construct high-precision 3D models of entities, providing centimeter-level high-precision data support for the digital power grid.

[0003] However, during UAV LiDAR patrols, obstacles such as occlusion, water mist, and the performance of the LiDAR scanner itself (range, field of view, scanning frequency) can affect the 3D point cloud scanning process. Issues such as missing power lines and data gaps may occur, impacting the power grid channel inspection results. Furthermore, missing power lines significantly affect the accuracy of power line classification. Traditional algorithms cannot accurately identify single incomplete power line point clouds, making precise 3D power line reconstruction impossible. This necessitates re-flying and re-collecting power line point cloud data, wasting manpower and resources. Summary of the Invention

[0004] This invention provides a method and apparatus for three-dimensional reconstruction of missing power line point cloud data, which can achieve accurate segmentation and reconstruction of power line point cloud data, significantly improve the accuracy and efficiency of power line extraction, especially improve the accuracy of extraction and reconstruction of broken power line point cloud data, and greatly improve the realism of the model obtained.

[0005] This invention provides a method for three-dimensional reconstruction of missing power line point cloud data, including:

[0006] S1. Collect the three-dimensional point cloud of the power line to obtain the raw data, and perform voxel sampling and ground point identification on the raw data to obtain the target point of the power line;

[0007] S2. A basic three-dimensional catenary model is generated by fitting the target points of the power line, and the interior points of the basic three-dimensional catenary model are supplemented by a preset fitting residual model to obtain an initial three-dimensional model of the power line.

[0008] S3. Repeat step S2 a set number of times to obtain multiple initial three-dimensional electric field models, and select the electric field model with the most interior points among the initial three-dimensional electric field models as the target electric field model.

[0009] S4. Treat all interior points in the target power line model as a whole and calculate its feature vector. When the feature vector meets the preset threshold requirement, determine that the target power line model is a valid power line model; otherwise, return to step S2.

[0010] S5. Calculate the cross-sectional model of the electric field line based on the effective electric field line model, and integrate it with the target electric field line model to obtain a complete three-dimensional catenary model.

[0011] Furthermore, S1 specifically includes:

[0012] S101. Acquire the raw data by collecting the 3D point cloud of the power line, obtain the 3D spatial bounding box of the raw data, construct the bounding box by voxelization, and assign the voxel position to each point cloud, as shown in the following formula:

[0013] ;

[0014] in, Represents the coordinates of a 3D point cloud The row, column, and layer of the voxel. The length, width, and height of a voxel unit. The minimum coordinates of the bounding box in 3D space; ceil() is the floor function;

[0015] S102. Iterate through all voxel units in sequence. When the point cloud data in the voxel unit is 0, skip the voxel unit. When the voxel unit contains multiple point cloud data, perform random sampling to retain one as a sampling point to obtain the initial point cloud data.

[0016] S103. Ground point identification is performed on the initial point cloud data through ground filtering, and a two-dimensional grid is divided according to the ground points. The ground height feature of the point cloud in the two-dimensional grid is calculated, and the point cloud with a ground height greater than a set threshold is used as the target point of the power line.

[0017] Furthermore, S2 specifically includes:

[0018] S201. Randomly select three points from the target points of the power line as seed points, and use the seed points to fit and generate a basic three-dimensional catenary model; wherein, the basic three-dimensional catenary model includes the curved part of the catenary and the horizontal part, and the specific expression is:

[0019] ;

[0020] Where a and b are the curvature parameters of the catenary in the x and y directions, respectively; is the translation parameter in the x and y directions; c is the offset in the vertical direction; k is the slope of the line in the xy plane; d is the intercept of the line in the horizontal plane.

[0021] S202. Randomly select one or more points from the remaining target points of the power line after excluding the seed points as points to be optimized, and construct a residual model based on the points to be optimized. Then, filter and supplement interior points that conform to the geometric characteristics of the power line into the basic three-dimensional catenary model according to the residual model to obtain the initial three-dimensional model of the power line; wherein, the expression of the residual model is:

[0022] ;

[0023] in, Let be the fitting residual of the i-th point to be optimized relative to the basic three-dimensional catenary model; Let be the three-dimensional coordinate components of the i-th point to be optimized; To convert the general form of a line into the formula for the perpendicular distance from a point to a line, to ensure... This represents the perpendicular distance from the point to be optimized to the projected line of the model.

[0024] Furthermore, S202 specifically includes:

[0025] After excluding the seed points from the target points of the power lines, the remaining points are selected as the set of points to be optimized.

[0026] For each point in the set of points to be optimized The residual model is used to calculate the fitting residual between the residual model and the basic catenary model. And the geometric distance between it and the key feature points of the model is calculated using the spatial distance formula. ;

[0027] Set residual threshold And a spatial distance threshold r, to perform interior point filtering and supplementation, specifically:

[0028] like and If the point to be optimized is determined to be an interior point, it will be added to the basic three-dimensional catenary model for subsequent model parameter optimization.

[0029] like or Then further judgment If the point is within the range (r, 2r], it is marked as a potential noise point; otherwise, it is determined as an external point and excluded from the basic three-dimensional catenary model.

[0030] Furthermore, geometric distance The calculation formula is:

[0031] ;

[0032] in, Let p be the two-dimensional spatial distance from the point to be optimized to the key geometric feature point of the basic three-dimensional catenary model; Let p be the three-dimensional coordinate components of the point to be optimized. Represents the coordinate components of key geometric feature points in the basic three-dimensional catenary model.

[0033] Furthermore, S4 specifically includes:

[0034] S401. Treat all interior points in the target power line model as a whole to obtain the interior point set;

[0035] S402. Perform principal component analysis on the interior point set and extract the two eigenvalues ​​with the largest values. , Substitute the values ​​into the formula to calculate the linearity L. The formula is as follows:

[0036] ;

[0037] S403. Analyze the spatial distribution of the inlier set and calculate its effective coverage volume. and reference volume Substituting into the formula to calculate continuity C, the formula is:

[0038] ;

[0039] S404, Count the number of internal points and the number of noise points Substitute into the formula to calculate the point noise ratio Its formula is:

[0040] ;

[0041] S405, linearity L, continuity C, and point noise ratio. The features are combined as feature vectors, and linearity threshold, continuity threshold, and point-to-noise ratio threshold are set. If all the feature vectors satisfy their corresponding thresholds, the target power line model is determined to be an effective power line model; if any feature in the feature vectors does not satisfy its corresponding threshold, the process returns to step S2.

[0042] Furthermore, S5 specifically includes:

[0043] S501. Calculate the cross-sectional model of the electric field line based on the effective electric field line model. The calculation formula is as follows:

[0044] ;

[0045] in, For any point on the effective power line model; R is the direction vector of the electric field line; R is the radius of the electric field line. Let be any point on the cross-section; θ is an angle parameter, ranging from [0, 2π), used to traverse all points on the cross-section to form a circular cross-section;

[0046] S502. Integrate the target power line model with the cross-sectional models at each location to obtain a complete three-dimensional power line model that includes spatial orientation and cross-sectional shape.

[0047] The present invention also provides a three-dimensional reconstruction device for missing power line point cloud data, based on the three-dimensional reconstruction method for missing power line point cloud data as described above, the device comprising:

[0048] The acquisition module is used to acquire the raw data of the three-dimensional point cloud of the power line, and to perform voxel sampling and ground point recognition on the raw data to obtain the target points of the power line.

[0049] The fitting module is used to fit and generate a basic three-dimensional catenary model based on the target points of the power line, and to supplement the interior points of the basic three-dimensional catenary model using a preset fitting residual model to obtain an initial three-dimensional model of the power line.

[0050] The selection module is used to repeatedly calculate the fitting module a set number of times to obtain multiple initial three-dimensional electric field models, and select the electric field model with the most interior points among the initial three-dimensional electric field models as the target electric field model.

[0051] The calculation module is used to treat all interior points in the target electric field model as a whole and calculate its feature vector. When the feature vector meets the preset threshold requirement, the target electric field model is determined to be a valid electric field model; otherwise, the process returns to the fitting module.

[0052] The integration module is used to calculate the cross-sectional model of the electric line based on the effective electric line model, and integrate it with the target electric line model to obtain a complete three-dimensional catenary model.

[0053] The beneficial effects of this invention are as follows:

[0054] This invention constructs a basic three-dimensional catenary model, combines it with a residual model to screen and supplement interior points that meet the characteristics, and after multiple rounds of iterative optimization, evaluates the effectiveness of the model through features such as linearity, continuity, and point-to-noise ratio. Finally, it integrates the cross-sectional model to obtain a complete three-dimensional model, which can effectively cope with point cloud missingness and noise interference, improve the model's fitting accuracy and completeness to the real shape of power lines, enhance the reliability of the model, and has a high degree of automation throughout the process, providing accurate and comprehensive three-dimensional model support for power line-related applications. Attached Figure Description

[0055] Figure 1 This is a flowchart illustrating the three-dimensional reconstruction method for missing power line point cloud data according to the present invention.

[0056] Figure 2 This is a schematic diagram of the distance-to-ground height features during ground point identification in this invention.

[0057] Figure 3 This is a schematic diagram of the interior point detection of the basic three-dimensional catenary model in this invention.

[0058] Figure 4 This is a comparative diagram of three-dimensional reconstruction of distribution network point cloud data in Example 1 of the present invention.

[0059] Figure 5 This is a schematic diagram comparing the effects of three-dimensional remodeling of the main network line in Example 2 of the present invention.

[0060] Figure 6 This is a schematic diagram of the structure of the three-dimensional reconstruction device for missing power line point cloud data according to the present invention.

[0061] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0062] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0063] Existing power line point cloud extraction and reconstruction algorithms primarily follow a classification-then-reconstruction approach. First, target region points are extracted through preprocessing, and local features such as linearity and flatness are calculated for these points. Then, deep learning models or unsupervised classification algorithms are used to extract the power line point cloud. Next, 3D reconstruction of individual power lines is performed based on the 3D point cloud data. However, the reconstruction accuracy of these algorithms currently depends entirely on the power line classification results. Traditional point feature-based power line classification algorithms lack sufficient accuracy when dealing with low-quality power line point clouds (those with obvious breaks or incomplete sections). This often leads to inaccurate 3D reconstructions of incomplete power lines, impacting power corridor safety inspections and digital grid construction.

[0064] Therefore, especially for scenarios involving incomplete power lines, this invention proposes an accurate 3D reconstruction method for power lines, which can achieve precise segmentation and reconstruction of power line point cloud data. It belongs to the field of 3D modeling of power lines in computer graphics. This invention is mainly used in the classification of point cloud data in outdoor scenes, enabling accurate extraction of power line point cloud data. It can be used for power channel safety inspection and 3D reconstruction of point cloud data.

[0065] This invention abandons the traditional approach of first extracting and then reconstructing 3D point clouds. By designing an optimized RANSAC model combined with a fitting evaluation mechanism, especially for incomplete power line data, it can achieve fast and efficient 3D reconstruction of power lines. Specifically,

[0066] This invention acquires raw data from 3D point clouds of power lines; voxel sampling is performed on the raw data to reduce the number of input point clouds and improve processing efficiency; based on the sampled point cloud data, a 3D catenary model with multiple descriptive parameters is constructed. The Random Sampling Detection (RANSAC) method is used, iteratively selecting multiple points as optimization points each time, and constructing a residual model. The Ceres optimization method is used for rapid convergence to obtain the descriptive parameters of the 3D catenary model for the optimization points, terminating when the maximum number of iterations is reached or the residual reaches the ideal range. Power line points within a 0.5m radius around the 3D catenary are extracted according to the model and moved into the extracted point cloud set. Residual model optimization is performed on the point cloud on the power line to obtain the optimal descriptive parameters. Random sampling is then performed again for the remaining point clouds. After all models are detected, the continuity of the power line point cloud is checked to determine if it is a true power line model and point cloud. 3D reconstruction is performed based on the fitted power line model. The cross-sectional area of ​​the power line points in the model is calculated, and the cross-sectional radius of the power line is used for 3D reconstruction. This invention can significantly improve the accuracy and efficiency of power line extraction, especially the accuracy of point cloud data extraction and reconstruction of fractured power lines, and can also greatly improve the realism of the resulting models. It plays an important role in improving the accuracy of 3D power line modeling, rapid clustering, and data reuse.

[0067] like Figure 1 As shown, this invention provides a method for three-dimensional reconstruction of missing power line point cloud data, including:

[0068] S1. Acquire raw data by collecting 3D point cloud data of power lines, and perform voxel sampling and ground point identification on the raw data to obtain target points of the power lines. Specifically, this includes the following steps:

[0069] S101. Acquire the raw data by collecting the 3D point cloud of the power line, obtain the 3D spatial bounding box of the raw data, construct the bounding box by voxelization, and assign the voxel position (row, column, layer) to each point cloud, as shown in the following formula:

[0070] ;

[0071] in, Represents the coordinates of a 3D point cloud The row, column, and layer of the voxel. The length, width, and height of a voxel unit. The minimum coordinates of the bounding box in 3D space; ceil() is the floor function;

[0072] S102. Iterate through all voxel units sequentially (each voxel unit may contain 0 or more point data). When the point cloud data in the voxel unit is 0, skip the voxel unit. When the voxel unit contains multiple point cloud data, perform random sampling to retain one as a sampling point to obtain the initial point cloud data. After voxelization, the point cloud retains the original geometric features of the ground features while the amount of point cloud data is greatly reduced compared to the original point cloud.

[0073] As in steps S101-S102, the original input data is sampled using voxelization to obtain extracted point cloud data, thereby reducing the number of input point clouds and improving efficiency.

[0074] S103. After voxelization, the initial point cloud data is processed by ground filtering to identify ground points. A two-dimensional grid is then formed based on these ground points. The ground elevation features of the point cloud within the two-dimensional grid are calculated. Points with a ground elevation greater than a set threshold (e.g., 4m) are designated as power line target points. Figure 2 As shown, the purpose of this step is the same as the voxelization sampling step: to reduce the number of input point clouds and improve efficiency. It should be noted that even if some electric field line point clouds are missed in this step, it will not have a significant impact on the model fitting accuracy.

[0075] S2. By optimizing the RANSAC algorithm, a basic three-dimensional catenary model is generated based on the target points of the power line. Then, the basic three-dimensional catenary model is supplemented with interior points using a preset fitting residual model to obtain an initial three-dimensional power line model. For example... Figure 3As shown, the specific steps include:

[0076] S201. Randomly select three points from the target points of the power line as seed points, and use the seed points to fit and generate a basic three-dimensional catenary model; wherein, the basic three-dimensional catenary model includes the curved part of the catenary and the horizontal part, and the specific expression is:

[0077] ;

[0078] In the upper part, a and b are the curvature parameters of the catenary in the x and y directions, respectively, which control the degree of bending of the catenary (the smaller the value, the more obvious the bending). is the translation parameter in the x and y directions, which determines the center position of the catenary in the horizontal plane; c is the vertical offset (usually corresponding to the z-axis), which is used to determine the reference height of the catenary; z describes the catenary-like curvature of the electric field line in the vertical direction with respect to the horizontal coordinates (x, y).

[0079] In the lower part, k is the slope of the straight line on the xy plane, describing the trend of the electric field line in the horizontal direction; d is the intercept of the straight line on the horizontal plane, determining the position of the electric field line in the horizontal direction; here y is the horizontal coordinate, describing the linear extension of the electric field line on the horizontal plane.

[0080] The specific fitting process is as follows: Three randomly selected seed points must satisfy the prerequisite that they belong to the target electric field line point cloud, and their three-dimensional coordinates (denoted as...) are... It contains key geometric information about the electric field lines: the horizontal direction (xy plane) and the catenary curvature characteristics in the vertical direction (z axis).

[0081] The basic 3D catenary model consists of two parts: a horizontal linear orientation and a vertical catenary curvature (as mentioned earlier). The coordinates of the three seed points are used to fit the parameters of these two parts respectively.

[0082] (1) Fitting the linear parameters in the horizontal direction

[0083] The horizontal model describes the projection of the electric field lines onto the xy-plane, and its parameters k (slope) and d (intercept) are determined as follows:

[0084] The horizontal coordinates of the 3 seed points ( , , Substitute the equations into the linear equation y=kx+d to form a system of equations;

[0085] Since the three points may have slight errors (not strictly collinear), the least squares method is used to solve for k and d, so that the sum of the vertical distances from the three points to the fitted line is minimized, and finally the horizontal baseline is determined.

[0086] (2) Fitting the parameters of the catenary in the vertical direction

[0087] The vertical model describes the catenary-like curvature of the electric field lines along the z-axis (a shape that naturally droops due to gravity), and its parameters (a and b are curvature parameters) are used. , (where c is the translation parameter and c is the vertical offset) is determined in the following way:

[0088] Since the catenary equation is a hyperbolic cosine function (nonlinear), nonlinear least squares fitting (such as the Levenberg-Marquardt algorithm) is used to iteratively solve the parameters, so that the deviation between the actual z coordinates of the three points and the z coordinates calculated by the model is minimized, and the catenary shape in the vertical direction is finally determined.

[0089] (3) Complete the horizontal direction parameters (a, b) and vertical direction parameters (a, b, ...). , After fitting the two parts (c), the two parts are combined to form a complete basic three-dimensional catenary model.

[0090] S202. Randomly select one or more points from the remaining target points of the power line after excluding the seed points as points to be optimized, and construct a residual model based on the points to be optimized. Then, filter and supplement interior points that conform to the geometric characteristics of the power line into the basic three-dimensional catenary model according to the residual model to obtain the initial three-dimensional model of the power line; wherein, the expression of the residual model is:

[0091] ;

[0092] in, is the residual (i.e., the quantized value of spatial deviation) of the i-th point to be optimized relative to the basic 3D catenary model; k is the slope parameter of the basic 3D catenary model on a certain projection plane (such as the xy plane), used to describe the tilt or curvature trend of the model; denoted as , where is the three-dimensional coordinate component of the i-th point to be optimized (here, x and y components are used, which is a simplification of model projection analysis); d is the intercept parameter of the basic three-dimensional catenary model on this projection plane. To convert the general form of a line into the formula for the perpendicular distance from a point to a line, to ensure... It represents the perpendicular distance from the point to be optimized to the projected line of the model, reflecting the geometric meaning of spatial deviation.

[0093] Specifically, the filtering and supplementation in S202 is performed in the following steps:

[0094] (1) After excluding the seed points from the target points of the power line, select the remaining points as the set of points to be optimized;

[0095] (2) For each point to be optimized The residual model is used to calculate the mathematical fitting residual between the residual model and the basic catenary model. And the geometric distance between it and the key feature points of the model is calculated using the spatial distance formula. Geometric distance The calculation formula is:

[0096] ;

[0097] in, The two-dimensional spatial distance from the point p to be optimized to the key geometric feature points (such as the catenary vertex and the center projection point) of the basic three-dimensional catenary model (usually focusing on the principal plane of the electric field line extension, such as the xz plane). Let x and z be the three-dimensional coordinate components of the point p to be optimized (the x and z components are used here, which is a simplification of the model projection analysis). The coordinate components represent the key geometric feature points of the basic 3D catenary model (such as the catenary vertices and the projection of the center onto the xz plane), where Actually, these are translation parameters. .

[0098] (3) Set the residual threshold And a spatial distance threshold r, to perform interior point filtering and supplementation, specifically:

[0099] like and If the point to be optimized is determined to be an interior point, it will be added to the basic three-dimensional catenary model for subsequent model parameter optimization.

[0100] like or Then further judgment If the point is within the range (r, 2r], it is marked as a potential noise point; otherwise, it is determined as an external point and excluded from the basic three-dimensional catenary model.

[0101] (4) Form a set of interior points from the points to be optimized that are determined to be interior points. The points to be optimized that are identified as potential noise points are combined into a noise point set. .

[0102] S3. Repeat step S2 a set number of times to obtain multiple initial three-dimensional electric field models (e.g., 500 times to obtain 500 initial three-dimensional electric field models), and select the group of electric field models with the most interior points among the initial three-dimensional electric field models as the target electric field model.

[0103] S4. Treat all interior points in the target power line model as a whole and calculate its feature vector. If the feature vector meets a preset threshold requirement, determine that the target power line model is a valid power line model; otherwise, return to step S2. Specifically, this includes the following steps:

[0104] S401. Treat all interior points in the target power line model as a whole to obtain the interior point set;

[0105] S402. Perform principal component analysis on the interior point set and extract the two eigenvalues ​​with the largest values. , Substitute the values ​​into the formula to calculate the linearity L. The formula is as follows:

[0106] ;

[0107] S403. Analyze the spatial distribution of the inlier set and calculate its effective coverage volume. and reference volume Substituting into the formula to calculate continuity C, the formula is:

[0108] ;

[0109] The formula measures the degree of continuous distribution of interior points in space. The closer C is to 1, the better the continuity of the electric field point cloud (without obvious breaks).

[0110] S404, Count the number of internal points and the number of noise points Substitute into the formula to calculate the point noise ratio Its formula is:

[0111] ;

[0112] The formula measures the ratio of interior points to noise points. The larger the value, the higher the proportion of effective power line points in the model and the lower the noise interference.

[0113] S405, linearity L, continuity C, and point noise ratio. The features are combined as feature vectors, and linearity threshold, continuity threshold, and point-to-noise ratio threshold are set. If all the feature vectors satisfy their corresponding thresholds, the target power line model is determined to be a valid power line model. If any feature in the feature vector does not satisfy its corresponding threshold, the process returns to step S2 to return all the current preliminary power line points (including inliers and points to be optimized) to the original point cloud and re-executes the iterative process of seed point selection → basic model construction → residual inlier supplementation until a model that satisfies the threshold is found.

[0114] S5. Calculate the cross-sectional model of the electric field line based on the effective electric field line model, and integrate it with the target electric field line model to obtain a complete three-dimensional catenary model. Specifically, this includes the following steps:

[0115] S501. Calculate the cross-sectional model of the electric field line based on the effective electric field line model. The calculation formula is as follows:

[0116] ;

[0117] in, For any point on the effective power line model; R is the direction vector of the electric field line; R is the radius of the electric field line. Let be any point on the cross-section; θ is an angle parameter, ranging from [0, 2π). By changing the value of θ, all points on the circle can be traversed, describing the specific position of each point on the circle. Specifically,

[0118] (1) Perform principal component analysis on the set of interior points to extract the eigenvectors corresponding to the largest eigenvalues, which will serve as the direction vectors of the electric field lines. ;

[0119] (2) Select multiple key nodes (such as catenary apex, endpoint, midpoint, etc.) from the set of inner points as the center reference points of the cross section to cover the entire length of the power line;

[0120] (3) For each reference point (x, y, z), traverse the angle θ∈[0, 2π), substitute it into the cross-section formula to calculate all points (X, Y, Z) on the cross-section, that is, by adjusting the direction vector Perform orthogonal decomposition to obtain two orthogonal components perpendicular to the direction of the electric field lines. Combine the radius R and the angle θ to generate points on the circular cross section.

[0121] (4) Calculate the diameter D of the electric field line. The cross-section is circular, and its diameter D = 2R. Here, R can be determined in the following way:

[0122] If R is a known design parameter in the formula, D = 2R can be calculated directly; if point cloud fitting is required, the minimum bounding circle can be fitted to the generated cross-sectional point cloud, and the radius R can be solved to obtain the diameter D.

[0123] S502. Integrate the target power line model with the cross-sectional models at each location to obtain a complete three-dimensional power line model that includes spatial orientation and cross-sectional shape.

[0124] Different specifications of power lines have different diameters. Therefore, in order to better simulate the real scene, this invention divides the three-dimensional model into two dimensions and combines them: a three-dimensional catenary model and a cross-section.

[0125] This invention constructs a basic three-dimensional catenary model, combines it with a residual model to screen and supplement interior points that meet the characteristics, and after multiple rounds of iterative optimization, evaluates the effectiveness of the model through features such as linearity, continuity, and point-to-noise ratio. Finally, it integrates the cross-sectional model to obtain a complete three-dimensional model, which can effectively cope with point cloud missingness and noise interference, improve the model's fitting accuracy and completeness to the real shape of power lines, enhance the reliability of the model, and has a high degree of automation throughout the process, providing accurate and comprehensive three-dimensional model support for power line-related applications.

[0126] The following specific examples will further illustrate this point.

[0127] Example 1, such as Figure 4 As shown

[0128] Step 1. After the raw point cloud data of the power distribution channel is collected as input, a voxel grid is constructed. Each voxel grid retains at most 1 point as a voxel point. The voxel grid is a (5cm, 5cm, 5cm) cube by default.

[0129] Step 2. Voxel points are identified by ground filtering, a two-dimensional grid is divided, the height feature of the point cloud above the ground within the two-dimensional grid is calculated, and point clouds with a height above the ground greater than 2m are used as power line target points.

[0130] Step 3. Randomly select three points from the target point of the power line as seed points to construct the original 3D model of the power line. The 3D model of the power line is as follows:

[0131] ;

[0132] Step 4. Randomly select target points for the power lines, add them as seed points, construct a fitting residual model, and add points that meet the requirements to the interior points. After traversing all target points for the power lines, obtain the iteratively completed 3D catenary model;

[0133] Step 5. Iterate the calculation of the model 500 times to obtain its corresponding 3D catenary model, and select the model with the most interior points to participate in subsequent calculations;

[0134] Step 6. Perform a secondary retrieval on the original point cloud data of the power distribution channel within a 0.5m radius around the 3D catenary model. Points within a 0.3m radius are marked as internal points, and points within a distance of 0.3m to 0.5m are marked as noise points.

[0135] Step 7. By calculating the linearity feature, continuity feature, and point-to-noise ratio feature of the interior points, a feature vector is formed. After satisfying the following feature requirements, the power line points of this model are moved into the extracted fitted point cloud and removed from the original power line point set.

[0136] Step 8. Move the extracted point cloud into the extracted fitted point cloud set and remove it from the original electric field point set;

[0137] Step 9. Calculate the cross-sectional area of ​​the electric field line using the data from the fitted point cloud. Calculate the radius of the electric field line using the least squares method. The radius of the fitted cross-sectional circle is 7cm.

[0138] Example 2, such as Figure 5 As shown

[0139] The difference between this example and example one is that the collected data is the main network data, and the calculated feature vectors, after failing the evaluation, all point clouds are returned to the original point cloud set, and step 2 is repeated. Then, three points are randomly selected from the voxel point cloud set (the already selected point set will not be selected again) to fit and construct a three-dimensional model of the power line.

[0140] Step 1. After collecting the raw point cloud data of the main power grid channel as input, construct a voxel grid, retaining at most one point as a seed point in each voxel grid. Set the voxel grid to 10cm, 10cm, 10cm to improve efficiency;

[0141] Step 2. Randomly select three points from the voxel point cloud data to construct the original 3D model of the electric field line. The 3D model of the electric field line is as follows:

[0142] ;

[0143] Step 3. Randomly select a voxel point, add it to the point to be optimized, construct a fitting residual model, and add points that meet the requirements to the interior points. After traversing all voxel points, obtain the iteratively completed 3D catenary model;

[0144] Step 4. Iterate the calculation of the model 500 times to obtain its corresponding 3D catenary model, and select the model with the most interior points to participate in subsequent calculations;

[0145] Step 5. Mark the original power channel point cloud within a 1m radius around the 3D catenary model. Points within a 0.5m radius are marked as inner points, and points within a 0.5m to 1m distance are marked as noise points.

[0146] Step 6. After calculating the linearity features, continuity features, and point-to-noise ratio features of the preliminary power line points to form a feature vector, if this feature vector does not meet the threshold requirement, all interior points will return the original power channel point cloud data, this model parameter will be invalid, and the number of subsequent iterations will be increased by 10% to 550.

[0147] Step 7. Re-select three random points to reconstruct the original 3D model of the electric field line. If the three selected seed points are all the same as the previously failed seed points, skip this step; otherwise, continue the iteration.

[0148] Step 8. Randomly select a voxel point, add it to the point to be optimized, construct a fitting residual model, and add points that meet the requirements to the interior points. After traversing all voxel points, obtain the iteratively completed 3D catenary model;

[0149] Step 9. Iterate the calculation of the model 500 times to obtain its corresponding 3D catenary model, and select the model with the most interior points to participate in subsequent calculations;

[0150] Step 10. Mark the original power channel point cloud within a 1m radius around the 3D catenary model. Points within a 0.5m radius are marked as inner points, and points within a 0.5m to 1m distance are marked as noise points.

[0151] Step 11. By calculating the linearity, continuity, and point-to-noise ratio features of the preliminary electric field points, a feature vector is formed. After meeting the following feature requirements, the preliminary electric field points of this model are moved into the extracted fitted point cloud and removed from the original electric field point set.

[0152] Step 12. Move the extracted point cloud into the extracted fitted point cloud set and remove it from the original electric field point set;

[0153] Step 13. Calculate the cross-sectional area of ​​the electric field line using the data from the fitted point cloud. Calculate the radius of the electric field line using the least squares method. The radius of the fitted cross-sectional circle is 7cm.

[0154] like Figure 6 As shown, the present invention also provides a three-dimensional reconstruction device for missing power line point cloud data. Based on the three-dimensional reconstruction method for missing power line point cloud data described above, the device includes:

[0155] Acquisition module 1 is used to acquire the raw data of the three-dimensional point cloud of the power line, and to perform voxel sampling and ground point recognition on the raw data to obtain the target point of the power line;

[0156] Fitting module 2 is used to fit and generate a basic three-dimensional catenary model based on the target points of the power line, and to supplement the interior points of the basic three-dimensional catenary model using a preset fitting residual model to obtain an initial three-dimensional model of the power line.

[0157] Module 3 is selected to repeatedly calculate the fitting module a set number of times to obtain multiple initial three-dimensional electric field models, and the electric field model with the most interior points among the initial three-dimensional electric field models is selected as the target electric field model.

[0158] The calculation module 4 is used to treat all interior points in the target electric field model as a whole and calculate its feature vector. When the feature vector meets the preset threshold requirement, the target electric field model is determined to be a valid electric field model; otherwise, the process returns to the fitting module.

[0159] Integration module 5 is used to calculate the cross-sectional model of the electric line based on the effective electric line model, and integrate it with the target electric line model to obtain a complete three-dimensional catenary model.

[0160] Each of the above modules is used to perform the respective steps in the above-described 3D reconstruction method for missing power line point cloud data. The specific implementation method is as described in the above-described method embodiments, and will not be repeated here.

[0161] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, apparatus, article, or method that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, apparatus, article, or method. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, apparatus, article, or method that includes that element.

[0162] The above description is merely a preferred embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structural or procedural transformations made based on the content of the present invention's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A method for three-dimensional reconstruction of missing power line point cloud data, characterized in that, include: S1. Collect the three-dimensional point cloud of the power line to obtain the raw data, and perform voxel sampling and ground point identification on the raw data to obtain the target point of the power line; S2. A basic three-dimensional catenary model is generated by fitting the target points of the power line, and the interior points of the basic three-dimensional catenary model are supplemented by a preset fitting residual model to obtain an initial three-dimensional model of the power line. S3. Repeat step S2 a set number of times to obtain multiple initial three-dimensional electric field models, and select the electric field model with the most interior points among the initial three-dimensional electric field models as the target electric field model. S4. Treat all interior points in the target power line model as a whole and calculate its feature vector. If the feature vector meets a preset threshold requirement, determine that the target power line model is a valid power line model; otherwise, return to step S2. Specifically, this includes: S401. Treat all interior points in the target power line model as a whole to obtain the interior point set; S402. Perform principal component analysis on the interior point set and extract the two eigenvalues ​​with the largest values. , Substitute the values ​​into the formula to calculate the linearity L. The formula is as follows: ； S403. Analyze the spatial distribution of the inlier set and calculate its effective coverage volume. and reference volume Substituting into the formula to calculate continuity C, the formula is: ； S404, Count the number of internal points and the number of noise points Substitute into the formula to calculate the point noise ratio Its formula is: ； S405, linearity L, continuity C, and point noise ratio. The features are combined as feature vectors, and linearity threshold, continuity threshold, and point noise ratio threshold are set. If all the feature vectors satisfy their corresponding thresholds, the target power line model is determined to be an effective power line model; if any feature in the feature vectors does not satisfy its corresponding threshold, the process returns to step S2. S5. Calculate the cross-sectional model of the electric field line based on the effective electric field line model, and integrate it with the target electric field line model to obtain a complete three-dimensional catenary model.

2. The three-dimensional reconstruction method for missing power line point cloud data according to claim 1, characterized in that, S1 specifically includes: S101. Acquire the raw data by collecting the 3D point cloud of the power line, obtain the 3D spatial bounding box of the raw data, construct the bounding box by voxelization, and assign the voxel position to each point cloud, as shown in the following formula: ； in, Represents the coordinates of a 3D point cloud The row, column, and layer of the voxel. The length, width, and height of a voxel unit; The minimum coordinates of the bounding box in 3D space; ceil() is the floor function; S102. Iterate through all voxel units in sequence. When the point cloud data in the voxel unit is 0, skip the voxel unit. When the voxel unit contains multiple point cloud data, perform random sampling to retain one as a sampling point to obtain the initial point cloud data. S103. Ground point identification is performed on the initial point cloud data through ground filtering, and a two-dimensional grid is divided according to the ground points. The ground height feature of the point cloud in the two-dimensional grid is calculated, and the point cloud with a ground height greater than a set threshold is used as the target point of the power line.

3. The three-dimensional reconstruction method for missing power line point cloud data according to claim 1, characterized in that, S2 specifically includes: S201. Randomly select three points from the target points of the power line as seed points, and use the seed points to fit and generate a basic three-dimensional catenary model; wherein, the basic three-dimensional catenary model includes the curved part of the catenary and the horizontal part, and the specific expression is: ； Where a and b are the curvature parameters of the catenary in the x and y directions, respectively; is the translation parameter in the x and y directions; c is the offset in the vertical direction; k is the slope of the line in the xy plane; d is the intercept of the line in the horizontal plane. S202. Randomly select one or more points from the remaining target points of the power line after excluding the seed points as points to be optimized, and construct a residual model based on the points to be optimized. Then, filter and supplement interior points that conform to the geometric characteristics of the power line into the basic three-dimensional catenary model according to the residual model to obtain the initial three-dimensional model of the power line; wherein, the expression of the residual model is: ； in, Let be the fitting residual of the i-th point to be optimized relative to the basic three-dimensional catenary model; Let be the three-dimensional coordinate components of the i-th point to be optimized; To convert the general form of a line into the formula for the perpendicular distance from a point to a line, to ensure... This represents the perpendicular distance from the point to be optimized to the projected line of the model.

4. The three-dimensional reconstruction method for missing power line point cloud data according to claim 3, characterized in that, S202 specifically includes: After excluding the seed points from the target points of the power lines, the remaining points are selected as the set of points to be optimized. For each point in the set of points to be optimized The residual model is used to calculate the fitting residual between the residual model and the basic catenary model. And the geometric distance between it and the key feature points of the model is calculated using the spatial distance formula. ; Set residual threshold And a spatial distance threshold r, to perform interior point filtering and supplementation, specifically: like and If the point to be optimized is determined to be an interior point, it will be added to the basic three-dimensional catenary model for subsequent model parameter optimization. like or Then further judgment If the point is within the range (r, 2r], it is marked as a potential noise point; otherwise, it is determined as an external point and excluded from the basic three-dimensional catenary model.

5. The three-dimensional reconstruction method for missing power line point cloud data according to claim 1, characterized in that, Geometric distance The calculation formula is: ； in, Let p be the two-dimensional spatial distance from the point to be optimized to the key geometric feature point of the basic three-dimensional catenary model; Let p be the three-dimensional coordinate components of the point to be optimized. Represents the coordinate components of key geometric feature points in the basic three-dimensional catenary model.

6. The three-dimensional reconstruction method for missing power line point cloud data according to claim 1, characterized in that, S5 specifically includes: S501. Calculate the cross-sectional model of the electric field line based on the effective electric field line model. The calculation formula is as follows: ； in, For any point on the effective power line model; R is the direction vector of the electric field line; R is the radius of the electric field line. Let be any point on the cross-section; θ is an angle parameter, ranging from [0, 2π), used to traverse all points on the cross-section to form a circular cross-section; S502. Integrate the target power line model with the cross-sectional models at each location to obtain a complete three-dimensional power line model that includes spatial orientation and cross-sectional shape.

7. A three-dimensional reconstruction device for missing power line point cloud data, characterized in that, The method for three-dimensional reconstruction of missing power line point cloud data based on any one of claims 1-6, the apparatus comprising: The acquisition module is used to acquire the raw data of the three-dimensional point cloud of the power line, and to perform voxel sampling and ground point recognition on the raw data to obtain the target points of the power line. The fitting module is used to fit and generate a basic three-dimensional catenary model based on the target points of the power line, and to supplement the interior points of the basic three-dimensional catenary model using a preset fitting residual model to obtain an initial three-dimensional model of the power line. The selection module is used to repeatedly calculate the fitting module a set number of times to obtain multiple initial three-dimensional electric field models, and select the electric field model with the most interior points among the initial three-dimensional electric field models as the target electric field model. The calculation module is used to treat all interior points in the target electric field model as a whole and calculate its feature vector. When the feature vector meets the preset threshold requirement, the target electric field model is determined to be a valid electric field model; otherwise, the process returns to the fitting module. The integration module is used to calculate the cross-sectional model of the electric line based on the effective electric line model, and integrate it with the target electric line model to obtain a complete three-dimensional catenary model.

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