Power line reconstruction method and device based on airborne laser radar scanning point cloud

Abstract

The invention discloses a power line reconstruction method and device based on airborne laser radar scanning point clouds, and the method comprises the steps: obtaining three-dimensional point cloud data scanned by an airborne laser radar, removing ground points in the three-dimensional point cloud data, and obtaining non-ground point cloud data; screening out coarsely screened power line point cloud data from the non-ground point cloud data according to the constructed three-dimensional geometric features of the power line and a first preset threshold set; and clustering and merging the coarsely screened power line point cloud data, and performing power line structure reconstruction on the merged point cloud data to obtain reconstructed power line point cloud data. The embodiment of the invention can improve the accuracy of power line detection and reconstruction, and can be widely applied to the technical field of power transmission line detection.

Description

Technical Field

[0001] The present invention relates to the technical field of power transmission line detection, and in particular to a method and device for power line reconstruction based on airborne laser radar scanning point cloud. Background Art

[0002] The stable operation of power transmission lines in power systems is closely linked to social production and public life. In recent years, with the continuous expansion of power grids, the geographical environments traversed by transmission lines have become increasingly complex, posing significant challenges to line maintenance and monitoring. Traditional manual inspection methods are not only inefficient but also pose significant safety risks. Against this backdrop, airborne LiDAR technology, with its all-weather operation, wide coverage, and efficient data collection, offers a new approach to intelligent power line monitoring. However, current technologies still require further improvement in the accuracy of power line detection and reconstruction in complex scenarios. Summary of the Invention

[0003] In view of this, an object of an embodiment of the present invention is to provide a method and device for power line reconstruction based on airborne laser radar scanning point cloud, which can improve the accuracy of power line detection and reconstruction.

[0004] On the one hand, an embodiment of the present invention provides a method for reconstructing power lines based on airborne laser radar scanning point clouds, comprising:

[0005] Acquire three-dimensional point cloud data scanned by an airborne laser radar, and remove ground points from the three-dimensional point cloud data to obtain non-ground point cloud data;

[0006] Filtering coarse power line point cloud data from the non-ground point cloud data according to the constructed three-dimensional geometric features of the power lines and a first preset threshold set;

[0007] The coarsely screened power line point cloud data are clustered and merged, and the power line structure is reconstructed on the merged various point cloud data to obtain reconstructed power line point cloud data.

[0008] Optionally, removing ground points from the three-dimensional point cloud data to obtain non-ground point cloud data includes:

[0009] Initializing and parameterizing a cloth simulation filter, and performing a cloth simulation filter on the three-dimensional point cloud data to remove ground points in the three-dimensional point cloud data to obtain non-ground point cloud data.

[0010] Optionally, screening out coarse power line point cloud data from the non-ground point cloud data according to the constructed three-dimensional geometric features of the power lines and a first preset threshold set includes:

[0011] Constructing a tree data structure for the non-ground point cloud data, and calculating a spherical neighborhood covariance matrix of each point in the non-ground point cloud data according to the tree data structure and a preset spherical radius;

[0012] Determine the linearity and the main direction ground parallelism of each point according to the spherical neighborhood covariance matrix of each point; the three-dimensional geometric features include linearity and main direction ground parallelism;

[0013] The coarse-screened power line point cloud data is screened out from the non-ground point cloud data according to the linearity of each point, the main direction ground parallelism and a first preset threshold set.

[0014] Optionally, clustering the coarsely screened power line point cloud data is performed by the following method:

[0015] The coarse-screened power line point cloud data are clustered using fast Euclidean clustering, and the classes whose projection lengths in the main directions of the power line point cloud data are less than a preset power line length threshold are removed.

[0016] Optionally, the clustered coarse-screened power line point cloud data is merged using the following method:

[0017] Perform line segment representation on the remaining various types of power line point cloud data, wherein the line segment representation includes endpoint coordinates, plumb degrees, and line segment center representation;

[0018] Determine the shortest endpoint distance, the angle between plumb degrees, and the curvature continuity test level based on the results of line segment characterization;

[0019] The clustered coarse-screened power line point cloud data are merged according to the shortest endpoint distance, the angle between the plumbs, the curvature continuity test level, and a second preset threshold set.

[0020] Optionally, the power line structure reconstruction is performed on the merged various point cloud data to obtain reconstructed power line point cloud data, including:

[0021] Project the merged point cloud data onto the main direction-plumb direction two-dimensional plane;

[0022] A hyperbolic cosine model is used to perform least square fitting on the projection points in the two-dimensional plane, sampling points are determined according to the fitted hyperbolic cosine model, and reconstructed power line point cloud data is determined according to the sampling points.

[0023] On the other hand, an embodiment of the present invention provides a power line reconstruction device based on airborne laser radar scanning point cloud, comprising:

[0024] The first module is used to obtain three-dimensional point cloud data scanned by an airborne laser radar, and remove ground points from the three-dimensional point cloud data to obtain non-ground point cloud data;

[0025] A second module is configured to filter out coarse power line point cloud data from the non-ground point cloud data based on the constructed three-dimensional geometric features of the power lines and a first preset threshold set;

[0026] The third module is used to cluster and merge the coarse-screened power line point cloud data, and reconstruct the power line structure of the merged various point cloud data to obtain reconstructed power line point cloud data.

[0027] On the other hand, an embodiment of the present invention provides a power line reconstruction device based on airborne laser radar scanning point cloud, comprising:

[0028] at least one processor;

[0029] at least one memory for storing at least one program;

[0030] When the at least one program is executed by the at least one processor, the at least one processor implements the above method.

[0031] On the other hand, an embodiment of the present invention provides a computer-readable storage medium storing a program executable by a processor. When the program is executed by the processor, it is used to perform the above method.

[0032] On the other hand, an embodiment of the present invention provides a power line reconstruction device based on an airborne laser radar scanning point cloud, comprising an airborne laser radar and a computer device connected to the airborne laser radar; wherein,

[0033] The airborne laser radar is used to scan three-dimensional point cloud data of the power lines and send the three-dimensional point cloud data to the computer device;

[0034] The computer device comprises:

[0035] at least one processor;

[0036] at least one memory for storing at least one program;

[0037] When the at least one program is executed by the at least one processor, the at least one processor implements the above method.

[0038] The implementation of the embodiment of the present invention includes the following beneficial effects: the embodiment removes ground points of the three-dimensional point cloud data scanned by the airborne lidar, reduces the number of points in the input point cloud, reduces the subsequent computational burden, and at the same time, reduces the interference of a large number of irrelevant ground points on the subsequent detection process, thereby improving the accuracy of detection. Coarse-screened power line point cloud data is screened from the non-ground point cloud data based on the constructed three-dimensional geometric features of the power lines and the first preset threshold set, the coarse-screened power line point cloud data is clustered and merged, the non-ground point cloud data is first coarsely screened and then finely screened, thereby improving the accuracy of detecting power line point cloud data in complex scenes, and reconstructing the power line structure of the various types of merged point cloud data, which can reconstruct the missing parts of the power lines and improve the accuracy of power line reconstruction. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 This is a schematic flow chart of the steps of a method for reconstructing power lines based on airborne laser radar scanning point clouds provided by an embodiment of the present invention;

[0040] Figure 2 This is three-dimensional point cloud data of power lines provided by an embodiment of the present invention;

[0041] Figure 3 This is coarse-screened power line point cloud data of three-dimensional power line point cloud data provided by an embodiment of the present invention;

[0042] Figure 4 This is point cloud data obtained by clustering coarsely screened power line point cloud data provided by an embodiment of the present invention;

[0043] Figure 5 This is point cloud data obtained by clustering and merging coarsely screened power line point cloud data provided by an embodiment of the present invention;

[0044] Figure 6 This is reconstructed power line point cloud data provided by an embodiment of the present invention;

[0045] Figure 7 This is a structural block diagram of a power line reconstruction device based on airborne laser radar scanning point cloud provided by an embodiment of the present invention;

[0046] Figure 8 This is a structural block diagram of another power line reconstruction device based on airborne laser radar scanning point cloud provided by an embodiment of the present invention;

[0047] Figure 9 This is a structural block diagram of another power line reconstruction device based on airborne laser radar scanning point cloud provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0048] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. The step numbers in the following embodiments are provided for ease of description only and do not limit the order of the steps. The order of execution of the steps in the embodiments can be adaptively adjusted based on the understanding of those skilled in the art.

[0049] It should be noted that, although the functional modules are divided in the device schematic diagram and the logical order is shown in the flow chart, in some cases, the steps shown or described can be performed in a different order than the module division in the device or the order in the flow chart. The terms "first", "second", etc. in the specification and claims and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or precedence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0050] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application pertains. The terms used herein are for the purpose of describing the embodiments of this application only and are not intended to limit this application.

[0051] Some technical terms in this embodiment are explained below.

[0052] The plumb plane, one of the three projected vertical planes, refers to the plane perpendicular to the horizontal plane, that is, the plane where the plumb line lies.

[0053] KD tree (k-dimensional tree) is an efficient data structure for organizing point data in k-dimensional space. It is widely used in scenarios such as nearest neighbor search, range query, and space partitioning.

[0054] CSF is a physics-based point cloud filtering method used to separate ground points from non-ground points (such as vegetation, buildings, and vehicles) in airborne or ground-based LiDAR point cloud data. Its core idea is to simulate the process of cloth covering a point cloud, treating ground points as the area where the cloth will eventually fit, while non-ground points are "blocked" from the cloth.

[0055] like Figure 1 As shown, an embodiment of the present invention provides a method for reconstructing power lines based on airborne laser radar scanning point clouds, comprising:

[0056] S100: Acquire three-dimensional point cloud data scanned by an airborne laser radar, remove ground points from the three-dimensional point cloud data, and obtain non-ground point cloud data.

[0057] Airborne LiDAR scans power lines for 3D point cloud data. This 3D point cloud data contains X, Y, and Z coordinates, typically defined as the local geoid. Specifically, the XY plane represents the local horizontal plane of the scanned area, with the Z axis perpendicular to the local horizontal plane and pointing toward the zenith. The 3D point cloud data for power lines includes both ground point cloud data and non-ground point cloud data. The method for removing ground points from the 3D point cloud data depends on the application and is not specifically limited in this embodiment.

[0058] S200 , screening out coarse power line point cloud data from the non-ground point cloud data according to the constructed three-dimensional geometric features of the power lines and a first preset threshold set.

[0059] The three-dimensional geometric features are determined based on actual applications and are not specifically limited in this embodiment. The values ​​of the first preset threshold set are determined based on the three-dimensional geometric features. Non-ground point cloud data whose three-dimensional geometric features of power lines meet the first preset threshold set is filtered out as coarse-screened power line point cloud data.

[0060] S300 , clustering and merging the coarsely screened power line point cloud data, and reconstructing the power line structure of the merged various point cloud data to obtain reconstructed power line point cloud data.

[0061] The coarse-screened power line point cloud data is clustered into multiple categories. The clustered coarse-screened power line point cloud data of each category is then merged and the power line structure is reconstructed according to relevant conditions, thereby obtaining reconstructed power line point cloud data. It should be noted that the clustering, merging, and structure reconstruction methods are determined based on actual applications and are not specifically limited in this embodiment.

[0062] Optionally, removing ground points from the three-dimensional point cloud data to obtain non-ground point cloud data includes:

[0063] S101. Initialize and set parameters for a cloth simulation filter, and perform a cloth simulation filter on the three-dimensional point cloud data to remove ground points in the three-dimensional point cloud data to obtain non-ground point cloud data.

[0064] Initializing the cloth simulation filter includes but is not limited to cloth initialization (generating a virtual cloth mesh on top of the point cloud), etc. Parameters include but are not limited to cloth stiffness, number of iterations, classification threshold, or cloth mesh resolution, etc.

[0065] Optionally, screening out coarse power line point cloud data from the non-ground point cloud data according to the constructed three-dimensional geometric features of the power lines and a first preset threshold set includes:

[0066] S201: Construct a tree data structure for the non-ground point cloud data, and calculate a spherical neighborhood covariance matrix of each point in the non-ground point cloud data according to the tree data structure and a preset spherical radius.

[0067] The tree data structure and the preset sphere radius are determined according to actual applications and are not specifically limited in this embodiment. Each point in the non-ground point cloud data corresponds to a sphere neighborhood covariance matrix.

[0068] S202. Determine the linearity and the main direction ground parallelism of each point according to the spherical neighborhood covariance matrix of each point; the three-dimensional geometric features include linearity and main direction ground parallelism.

[0069] First, the eigenvalues ​​of the spherical neighborhood covariance matrix are calculated, and then the linearity principal direction ground parallelism is calculated based on the eigenvalues ​​of the spherical neighborhood covariance matrix.

[0070] S203 , filtering out the coarse-screened power line point cloud data from the non-ground point cloud data according to the linearity of each point, the main direction ground parallelism, and a first preset threshold set.

[0071] The first preset threshold set includes, but is not limited to, a linearity threshold and a main direction ground parallelism threshold. Points whose linearity is not less than the linearity threshold and whose main direction ground parallelism is not greater than the main direction ground parallelism threshold are used as coarse-screened power line point cloud data. It should be noted that the linearity threshold and main direction ground parallelism threshold in the first preset threshold set are determined based on actual applications and are not specifically limited in this embodiment.

[0072] Optionally, clustering the coarsely screened power line point cloud data is performed by the following method:

[0073] S301 , clustering the coarsely screened power line point cloud data using fast Euclidean clustering, and removing the classes of power line point cloud data whose projection lengths in the main direction are less than a preset power line length threshold.

[0074] It should be noted that the preset power line length threshold is determined based on actual application and is not specifically limited in this embodiment. Fast Euclidean Clustering (FEC) is an efficient clustering algorithm optimized for large-scale point cloud data. It aims to significantly improve computational speed while maintaining high clustering accuracy by improving the spatial indexing, search strategy, and parallel processing of traditional Euclidean clustering methods.

[0075] Optionally, the clustered coarse-screened power line point cloud data is merged using the following method:

[0076] S302, performing line segment representation on the remaining various types of power line point cloud data, wherein the line segment representation includes endpoint coordinates, plumb degrees, and line segment center representation;

[0077] S303: Determine the shortest endpoint distance, the angle between plumb degrees, and the curvature continuity test level based on the result of the line segment characterization;

[0078] S304 : merging the clustered coarse-screened power line point cloud data according to the shortest endpoint distance, the angle between the plumbs, the curvature continuity test level, and a second preset threshold set.

[0079] First, each type of power line point cloud data is characterized by line segments, and the endpoint coordinates, segment length, plumb degree and segment center are determined; then, the shortest endpoint distance is calculated based on the endpoint coordinates, the angle between the plumbs is calculated based on the plumb degrees of the two point sets, and the curvature continuity test level is calculated based on the endpoints corresponding to the shortest distance points; the second preset threshold set includes but is not limited to the distance threshold, the angle threshold and the curvature threshold; finally, the classes whose shortest endpoint distance is less than the distance threshold, the angle between the plumb degrees is less than the angle threshold, and the curvature continuity test level is less than the curvature threshold are iteratively merged.

[0080] Optionally, the power line structure reconstruction is performed on the merged various point cloud data to obtain reconstructed power line point cloud data, including:

[0081] S305, projecting the merged point cloud data onto a two-dimensional plane in the main direction-plumb direction;

[0082] S306 , performing least square fitting on the projection points using a hyperbolic cosine model in the two-dimensional plane, determining sampling points according to the fitted hyperbolic cosine model, and determining reconstructed power line point cloud data according to the sampling points.

[0083] Specifically, the principal direction of each type of point cloud data is calculated, and a two-dimensional plane is determined based on the principal direction-plumb direction. The merged point cloud data is then projected onto the principal direction-plumb direction two-dimensional plane. A hyperbolic cosine model is then used to perform a least squares fit on the projected points within the two-dimensional plane to determine the model parameters of the hyperbolic cosine model. Sampling points are determined based on the fitted hyperbolic cosine model, and the reconstructed power line point cloud data is determined based on the sampling points. It should be noted that the sampling method for the sampling points is determined based on the actual application and is not specifically limited in this embodiment.

[0084] The following describes the power line reconstruction process using a specific example, using the WHU-ALS dataset 0813 scene interception region as a specific implementation scheme.

[0085] Step 1: Read WHU-ALS-0813 scan point cloud data like Figure 2 shown.

[0086] Step 2: Initialize the CSF method, set the cloth resolution parameter to 0.5, filter out the ground points in the original point cloud data, and output non-ground points

[0087] Step 3: Construct the point set KD tree data structure.

[0088] Step 4: Take the sphere radius as 1.5 and use the KD tree structure to calculate the point set The spherical neighborhood covariance matrix of each point in . Point p in k As an example, let the sphere neighborhood point set be Then point p k The covariance matrix of is calculated as follows:

[0089]

[0090] in, Represents the center of the neighborhood point set.

[0091] Step 5: Calculate the three-dimensional geometric features of each point. Note point p k The eigenvalues ​​of the covariance matrix are The eigenvector corresponding to the maximum eigenvalue is The linearity of this point Parallelism to the main direction ground Characterized by:

[0092]

[0093] Among them, u z Represents the unit vector in the positive direction of the z-axis of the geodetic horizontal coordinate system. Select points that satisfy the linearity of not less than 0.8 and the parallelism to the ground of not more than 0.2 as the coarse screening points of the power lines, such as Figure 3 As shown, Figure 3 The red points indicate the coarse screening points of the power lines.

[0094] Step 6: Set clustering parameters: sphere index radius is 1.5, number of nearest neighbor indexes is 20, and minimum projection length is 5. Use FEC method to cluster the power line coarse screening points, such as Figure 4 As shown, Figure 4 Different colors represent different classes.

[0095] Step 7: Let the point set of a certain type be Calculates the line segment representation of this class. Endpoint coordinates The calculation is as follows:

[0096]

[0097] Segment length The calculation is as follows:

[0098]

[0099] Plumb The calculation is as follows:

[0100]

[0101] Among them, cross(u,v) represents the cross product of vector u and v, Represents a point set The eigenvector corresponding to the largest eigenvalue of the covariance matrix. The calculation is as follows:

[0102]

[0103] Step 8: Set the distance threshold to 1.5, the plumbness threshold to 0.95, and the curvature threshold to 0.95. Set the point set Random point set and the remaining point set Perform the following iterative merge:

[0104] Step 8.1: Get the current iteration point set calculate and The shortest endpoint distance D ik :

[0105]

[0106] Step 8.2: Calculate the plumb cosine V of the two point sets ik :

[0107]

[0108] Step 8.3: If D ik ≤1.5 and V ik ≥0.95, take the shortest endpoint distance corresponding to the endpoint, assuming and Calculate the curvature continuity test level O according to the following rules ik :

[0109] like Let vector

[0110] otherwise,

[0111] O ik =|(q1) T q2 / (‖q1‖·‖q2‖)|

[0112] Step 8.4: If O ik ≥0.95, merge point set and Return to step 8.1. If no point set is merged in the current iteration, add the last merged point set to the new point set Q, delete all merged point sets, and return to step 8 until the remaining point set is empty. The result is as follows: Figure 5 As shown, Figure 5 The same color indicates that they belong to the same category after merging.

[0113] Step 9: Set the number of sampling points to 100 and perform the following reconstruction operation on all point sets in Q:

[0114] Step 9.1: Calculate the current point set Q k The main direction

[0115] Step 9.2: Project all points in the point set to the direction defined by the main direction and u z The plane of composition;

[0116] Step 9.3: Define the hyperbolic cosine model as y = a k +c k ·cosh((xb k ) / c k ), and the least squares method is used to calculate the optimal model parameters (a k ,b k ,c k );

[0117] Step 9.4: Calculate the coordinates of uniform sampling points using the optimal model parameters

[0118] Step 9.5: Reproject the sampled points back to 3D coordinates

[0119] Step 10: Output all reconstructed power line point cloud data, such as Figure 6 As shown, Figure 6 The same color indicates the same category.

[0120] The implementation of the embodiment of the present invention includes the following beneficial effects: the embodiment removes ground points of the three-dimensional point cloud data scanned by the airborne lidar, reduces the number of points in the input point cloud, reduces the subsequent computational burden, and at the same time, reduces the interference of a large number of irrelevant ground points on the subsequent detection process, thereby improving the accuracy of detection. Coarse-screened power line point cloud data is screened from the non-ground point cloud data based on the constructed three-dimensional geometric features of the power lines and the first preset threshold set, the coarse-screened power line point cloud data is clustered and merged, the non-ground point cloud data is first coarsely screened and then finely screened, thereby improving the accuracy of detecting power line point cloud data in complex scenes, and reconstructing the power line structure of the various types of merged point cloud data, which can reconstruct the missing parts of the power lines and improve the accuracy of power line reconstruction.

[0121] See Figure 7 The embodiment of the present invention provides a power line reconstruction device based on airborne laser radar scanning point cloud, comprising:

[0122] The first module is used to obtain three-dimensional point cloud data scanned by an airborne laser radar, and remove ground points from the three-dimensional point cloud data to obtain non-ground point cloud data;

[0123] A second module is configured to filter out coarse power line point cloud data from the non-ground point cloud data based on the constructed three-dimensional geometric features of the power lines and a first preset threshold set;

[0124] The third module is used to cluster and merge the coarse-screened power line point cloud data, and reconstruct the power line structure of the merged various point cloud data to obtain reconstructed power line point cloud data.

[0125] It can be seen that the contents of the above method embodiments are all applicable to the present device embodiments. The functions specifically implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0126] See Figure 8 The embodiment of the present invention provides a power line reconstruction device based on airborne laser radar scanning point cloud, comprising:

[0127] at least one processor;

[0128] at least one memory for storing at least one program;

[0129] When the at least one program is executed by the at least one processor, the at least one processor implements the above method.

[0130] Among them, the memory is a non-transient computer-readable storage medium that can be used to store non-transient software programs and non-transient computer executable programs. The memory may include a high-speed random access memory and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory optionally includes a remote memory remotely arranged relative to the processor, and these remote memories can be connected to the processor via a network. Examples of the above-mentioned network include but are not limited to the Internet, an intranet, a local area network, a mobile communication network and a combination thereof.

[0131] It can be seen that the contents of the above method embodiments are all applicable to the present device embodiments. The functions specifically implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0132] In addition, the embodiments of the present application further disclose a computer program product or computer program, which is stored in a computer-readable storage medium. The processor of a computer device can read the computer program from the computer-readable storage medium, and the processor executes the computer program, so that the computer device performs the above-mentioned method. Similarly, the contents of the above-mentioned method embodiment are all applicable to the present storage medium embodiment, and the functions specifically implemented by the present storage medium embodiment are the same as those of the above-mentioned method embodiment, and the beneficial effects achieved are also the same as those achieved by the above-mentioned method embodiment.

[0133] An embodiment of the present invention further provides a computer-readable storage medium, which stores a program executable by a processor. The program executable by the processor is used to implement the above method when executed by the processor.

[0134] It is understood that all or some steps, systems in the disclosed method above can be implemented as software, firmware, hardware and appropriate combinations thereof. Some physical components or all physical components can be implemented as software by a processor, such as a central processing unit, a digital signal processor or a microprocessor, or implemented as hardware, or implemented as an integrated circuit, such as an application specific integrated circuit. Such software can be distributed on a computer-readable medium, and the computer-readable medium can include a computer storage medium (or non-transitory medium) and a communication medium (or temporary medium). As known to those of ordinary skill in the art, the term computer storage medium is included in any method or technology for storing information (such as computer-readable instructions, data structures, program modules or other data) and is volatile and non-volatile, removable and non-removable. Computer storage media includes but is not limited to RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disk (DVD) or other optical disk storage, magnetic cassette, magnetic tape, disk storage or other magnetic storage device, or any other medium that can be used to store desired information and can be accessed by a computer. Furthermore, as is well known to those skilled in the art, communication media typically embodies computer-readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transport mechanism, and may include any information delivery media.

[0135] See Figure 9 , an embodiment of the present invention provides a power line reconstruction device based on an airborne laser radar scanning point cloud, comprising an airborne laser radar and a computer device connected to the airborne laser radar; wherein,

[0136] The airborne laser radar is used to scan three-dimensional point cloud data of the power lines and send the three-dimensional point cloud data to the computer device;

[0137] The computer device comprises:

[0138] at least one processor;

[0139] at least one memory for storing at least one program;

[0140] When the at least one program is executed by the at least one processor, the at least one processor implements the above method.

[0141] Specifically, the airborne laser radar includes but is not limited to a laser ranging module, a positioning and attitude module, and a scanning module, etc.; and the computer equipment can be different types of electronic devices, including but not limited to desktop computers, laptops and other terminals.

[0142] It can be seen that the contents of the above method embodiments are all applicable to the present device embodiments. The functions specifically implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0143] It should be understood that in this application, "at least one (item)" means one or more, and "plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the previous and next associated objects are in an "or" relationship. "At least one of the following items" or similar expressions refers to any combination of these items, including any combination of single items or plural items. For example, at least one of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0144] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0145] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0146] The above is a specific description of the preferred implementation of the present invention, but the invention is not limited to the embodiments. Those skilled in the art can make various equivalent modifications or substitutions without violating the spirit of the present invention. These equivalent modifications or substitutions are all included in the scope defined by the claims of this application.

Claims

1. A method for power line reconstruction based on airborne laser radar scanning point cloud, characterized in that: include: Acquire three-dimensional point cloud data scanned by an airborne laser radar, and remove ground points from the three-dimensional point cloud data to obtain non-ground point cloud data; Filtering coarse power line point cloud data from the non-ground point cloud data according to the constructed three-dimensional geometric features of the power lines and a first preset threshold set; The coarsely screened power line point cloud data are clustered and merged, and the power line structure is reconstructed on the merged various point cloud data to obtain reconstructed power line point cloud data.

2. The method according to claim 1, characterized in that Removing ground points from the three-dimensional point cloud data to obtain non-ground point cloud data includes: Initializing and parameterizing a cloth simulation filter, and performing a cloth simulation filter on the three-dimensional point cloud data to remove ground points in the three-dimensional point cloud data to obtain non-ground point cloud data.

3. The method according to claim 1, characterized in that Screening out coarse power line point cloud data from the non-ground point cloud data according to the constructed three-dimensional geometric features of the power lines and a first preset threshold set includes: Constructing a tree data structure for the non-ground point cloud data, and calculating a spherical neighborhood covariance matrix of each point in the non-ground point cloud data according to the tree data structure and a preset spherical radius; Determine the linearity and the main direction ground parallelism of each point according to the spherical neighborhood covariance matrix of each point; the three-dimensional geometric features include linearity and main direction ground parallelism; The coarse-screened power line point cloud data is screened out from the non-ground point cloud data according to the linearity of each point, the main direction ground parallelism and a first preset threshold set.

4. The method according to claim 1, wherein The coarse-screened power line point cloud data is clustered by the following method: The coarse-screened power line point cloud data are clustered using fast Euclidean clustering, and the classes whose projection lengths in the main directions of the power line point cloud data are less than a preset power line length threshold are removed.

5. The method according to claim 4, characterized in that The clustered coarse-screened power line point cloud data is merged using the following method: Perform line segment representation on the remaining various types of power line point cloud data, wherein the line segment representation includes endpoint coordinates, plumb degrees, and line segment center representation; Determine the shortest endpoint distance, the angle between plumb degrees, and the curvature continuity test level based on the results of line segment characterization; The clustered coarse-screened power line point cloud data are merged according to the shortest endpoint distance, the angle between the plumbs, the curvature continuity test level, and a second preset threshold set.

6. The method according to claim 1, characterized in that The power line structure is reconstructed on the merged various point cloud data to obtain reconstructed power line point cloud data, including: Project the merged point cloud data onto the main direction-plumb direction two-dimensional plane; A hyperbolic cosine model is used to perform least square fitting on the projection points in the two-dimensional plane, sampling points are determined according to the fitted hyperbolic cosine model, and reconstructed power line point cloud data is determined according to the sampling points.

7. A power line reconstruction device based on airborne laser radar scanning point cloud, characterized in that: include: The first module is used to obtain three-dimensional point cloud data scanned by an airborne laser radar, and remove ground points from the three-dimensional point cloud data to obtain non-ground point cloud data; A second module is configured to filter out coarse power line point cloud data from the non-ground point cloud data based on the constructed three-dimensional geometric features of the power lines and a first preset threshold set; The third module is used to cluster and merge the coarse-screened power line point cloud data, and reconstruct the power line structure of the merged various point cloud data to obtain reconstructed power line point cloud data.

8. A power line reconstruction device based on airborne laser radar scanning point cloud, characterized in that: include: at least one processor; at least one memory for storing at least one program; When the at least one program is executed by the at least one processor, the at least one processor implements the method according to any one of claims 1 to 6.

9. A computer-readable storage medium storing a program executable by a processor, characterized in that: The processor-executable program is used to perform the method according to any one of claims 1 to 6 when executed by the processor.

10. A power line reconstruction device based on airborne laser radar scanning point cloud, characterized in that: It includes an airborne laser radar and a computer device connected to the airborne laser radar; wherein, The airborne laser radar is used to scan three-dimensional point cloud data of the power lines and send the three-dimensional point cloud data to the computer device; The computer device comprises: at least one processor; at least one memory for storing at least one program; When the at least one program is executed by the at least one processor, the at least one processor implements the method according to any one of claims 1 to 6.

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