Power line positioning method, device and equipment based on airborne radar and storage medium

Abstract

The invention discloses a power line positioning method, device and equipment based on an airborne radar and a storage medium, the method simplifies an algorithm for a specific scene of low-altitude unmanned aerial vehicle obstacle avoidance, and the method can obtain the distance between an unmanned aerial vehicle and a power line without modeling the power line. And the operation speed and the positioning precision are relatively high. Based on a mathematical statistics method of a probability statistics histogram, a natural breakpoint method is used for automatically searching an optimal height threshold value for ground filtering, and excessive segmentation of a power line caused by a fixed height threshold value in a complex terrain is avoided. According to the feature of noise point distribution of the point cloud data, the DBSCAN algorithm is adopted to remove noise points, compared with a single filtering method, the DBSCAN algorithm has high adaptability, clustering can be carried out on the point cloud data, core points, boundary points and noise points in the point cloud data are classified, obvious outliers are removed, and the accuracy of a final calculation result is guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field of autonomous obstacle avoidance for low-altitude unmanned aerial vehicles, and in particular to a power line positioning method, device, equipment and storage medium based on airborne radar with fast computing speed and high positioning accuracy. Background Art

[0002] Point cloud power line extraction is a technology involving laser point cloud data processing, target clustering and segmentation, which is mainly used to identify and extract the structure of power lines from point cloud data. This usually involves complex algorithm processing, including filtering, segmentation, clustering and other steps to accurately identify the position and shape of power lines from a large amount of point cloud data.

[0003] The current power line reconstruction / positioning methods mainly include threshold segmentation method, principal component analysis method, fast Euclidean clustering algorithm, and reconstruction methods based on point primitives, pixel primitives, and grid primitives. Most of these algorithms reconstruct and position power lines based on the statistical laws of point clouds.

[0004] However, traditional point cloud power line extraction algorithms generally process all the acquired spatial point clouds to reconstruct the power lines in space. However, this type of post-processing reconstruction algorithm generally requires a large amount of data to be processed, has a slow processing speed, difficult algorithm design, a complex reconstruction process, and is prone to information loss. It cannot meet the needs of fast and accurate positioning of power lines during UAV flight. Summary of the invention

[0005] In view of the above problems, the present invention provides a method, device, equipment and storage medium for locating power lines based on airborne radar to overcome the above problems or at least partially solve the above problems. The method can quickly and accurately locate small power lines in space.

[0006] The present invention provides the following scheme:

[0007] A power line positioning method based on airborne radar, comprising:

[0008] Obtaining an original point cloud of power lines collected by an airborne laser radar of the UAV, and removing point cloud data outside a region of interest in the original point cloud to obtain a first point cloud;

[0009] Fusing the first point cloud with the posture information of the UAV to obtain a corrected second point cloud;

[0010] A probability statistical histogram is used to obtain the distribution probability of the number of point clouds at different heights, and the distribution probability is used to perform adaptive elevation filtering on the second point cloud to remove the ground point cloud and retain the aerial power line point cloud to obtain a third point cloud;

[0011] Using a density clustering algorithm after determining the MinPts parameter to remove isolated noise point clouds contained in the third point cloud to obtain a fourth point cloud;

[0012] Screening the fourth point cloud based on a random sampling consistency algorithm to obtain a screened target point cloud of power lines;

[0013] Fitting the target point cloud based on the least squares method to obtain a straight line equation;

[0014] The linear equation is calculated to obtain the position information between the UAV and the power line.

[0015] Preferably, an area within the target range of the UAV flight heading is determined as the region of interest.

[0016] Preferably: a probability distribution histogram of different heights of the second point cloud is counted;

[0017] Determine the number of categories;

[0018] According to the classification number and the probability distribution histogram, a height threshold is adaptively generated using a natural breakpoint method;

[0019] The ground point cloud and the power line point cloud are divided and classified according to the height threshold, and the point cloud data below the height threshold is eliminated to obtain the third point cloud.

[0020] Preferably: the Euclidean distance formula is used to calculate the features of the unknown data and the features corresponding to each data in the third point cloud to obtain the distance distribution matrix of the third point cloud; the distance distribution matrix is ​​arranged in ascending order to obtain the KNN distribution matrix of the third point cloud;

[0021] Take the average distance of K value by column to get the Eps parameter list;

[0022] The core points are traversed with the Eps parameter as the neighborhood to find data points that are density-reachable with the core points, and clustered into clusters. The Eps and MinPts parameters corresponding to the K value when the number of clusters is stable and the density threshold is minimum are selected as the optimal parameters.

[0023] Preferably: the search speed of the density clustering algorithm is accelerated by establishing a tree data structure.

[0024] Preferably, the step of screening the fourth point cloud based on a random sampling consistency algorithm to obtain a screened target point cloud of power lines comprises:

[0025] Get the total number of each iteration;

[0026] Obtaining a current straight line and its number of inner points in each iteration, wherein the current straight line is obtained by fitting two points randomly selected from the fourth point cloud, and the number of inner points is the number of points whose distance to the current straight line is within a preset distance threshold;

[0027] Determine the maximum number of iterations for each iteration according to the inlier point ratio of the current straight line, where the inlier point ratio is the ratio of the number of inlier points to the total number of points of the fourth point cloud;

[0028] When the maximum number of iterations is less than the total number of iterations, the corresponding current straight line is determined to be a suboptimal straight line, and the point cloud corresponding to the suboptimal straight line is used as the target point cloud.

[0029] Preferably, the interior point ratio is obtained, and the interior point ratio is calculated according to the following formula:

[0030]

[0031] Where t is the ratio of internal points, M is the number of internal points of the current line, and N is the total number of points in the point cloud data to be fitted;

[0032] The confidence level is obtained by calculating the confidence level according to the following formula:

[0033] p=1-(1-t 2 )

[0034] Among them, p is the confidence level, k is the maximum number of iterations of the current internal point ratio;

[0035] The number of iterations k of the random sampling consensus algorithm is calculated by the following formula:

[0036]

[0037] Where: p represents the confidence level.

[0038] A power line positioning device based on airborne radar, used in the above-mentioned power line positioning method based on airborne radar, the device comprising:

[0039] A first point cloud acquisition unit is used to acquire an original point cloud of power lines collected by an airborne laser radar of the UAV, and remove point cloud data outside a region of interest in the original point cloud to obtain a first point cloud;

[0040] A second point cloud acquisition unit, configured to fuse the first point cloud with the posture information of the UAV to obtain a corrected second point cloud;

[0041] A third point cloud acquisition unit is used to obtain the distribution probability of the number of point clouds at different heights by using a probability statistical histogram, so as to use the distribution probability to perform adaptive elevation filtering on the second point cloud to remove the ground point cloud and retain the aerial power line point cloud to obtain a third point cloud;

[0042] A fourth point cloud acquisition unit, configured to remove isolated noise point clouds contained in the third point cloud by using a density clustering algorithm after determining the MinPts parameter to obtain a fourth point cloud;

[0043] A target point cloud acquisition unit, configured to screen the fourth point cloud based on a random sampling consistency algorithm to obtain a target point cloud of the power lines after screening;

[0044] A fitting unit, used for fitting the target point cloud based on the least square method to obtain a straight line equation;

[0045] A position information calculation unit is used to calculate the straight line equation to obtain the position information between the UAV and the power line.

[0046] A power line positioning device based on airborne radar, the device comprising a processor and a memory:

[0047] The memory is used to store program code and transmit the program code to the processor;

[0048] The processor is used to execute the above-mentioned power line positioning method based on airborne radar according to the instructions in the program code.

[0049] A computer-readable storage medium, the computer-readable storage medium is used to store program codes, the program codes are used to execute the above-mentioned power line positioning method based on airborne radar.

[0050] According to the specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0051] The embodiments of the present application provide a power line positioning method, device, equipment and storage medium based on airborne radar. The method simplifies the algorithm for the specific scenario of low-altitude UAV obstacle avoidance. The method does not require modeling of the power lines to obtain the distance between the UAV and the power lines, and has a faster operating speed and positioning accuracy.

[0052] When determining the point cloud data, the mathematical statistics method based on the probability statistical histogram uses the natural breakpoint method to automatically find the optimal height threshold for ground filtering. It has good versatility and will not cause the problem of excessive segmentation of power lines and intermittent power lines in complex terrain due to the fixed height threshold.

[0053] In the method of denoising point cloud, the DBSCAN algorithm is used to remove noise points according to the characteristics of the noise point distribution in the point cloud data. Compared with a single filtering method, the DBSCAN algorithm has strong adaptability and can cluster the point cloud data into clusters, classify the core points, boundary points, and noise points in the point cloud data, remove obvious outliers, and ensure the accuracy of the final calculation results.

[0054] For power line fitting, the RANSAC and least squares fitting method is noise-insensitive. After filtering and denoising the original point cloud, the number of power line point clouds represented is already small, but it can basically represent the distribution of the power lines. Therefore, the algorithm does not need to set a large number of iterations, which makes the algorithm fitting fast, real-time, and able to quickly and accurately locate the power lines.

[0055] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0057] Figure 1 is a flow chart of a power line positioning method based on airborne radar provided by an embodiment of the present invention;

[0058] Figure 2 is an overall algorithm flow chart provided by an embodiment of the present invention;

[0059] Figure 3 is a flow chart of fitting point cloud data based on the least squares method provided by an embodiment of the present invention;

[0060] Figure 4 is a schematic diagram of point cloud data distribution before and after the fusion drone attitude correction provided by an embodiment of the present invention;

[0061] Figure 5 It is a schematic diagram of the result after filtering by the DBSCAN algorithm provided in an embodiment of the present invention;

[0062] Figure 6 This is a comparison diagram of the fitting effects of the random sampling consistency combined with the least squares method and the least squares method alone provided in an embodiment of the present invention;

[0063] Figure 7 is a schematic diagram of a power line positioning device based on airborne radar provided in an embodiment of the present invention;

[0064] Figure 8 It is a schematic diagram of a power line positioning device based on airborne radar provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0065] The technical scheme in the embodiment of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiment of the present invention. Obviously, the described embodiment is only a part of the embodiment of the present invention, not all of the embodiments. Based on the embodiment of the present invention, all other embodiments obtained by ordinary technicians in this field belong to the scope of protection of the present invention.

[0066] See also Figure 1 , a power line positioning method based on airborne radar is provided in an embodiment of the present invention, such as Figure 1 As shown, the method may include:

[0067] S101: Obtain the original point cloud of the power lines collected by the drone's airborne laser radar, and remove the point cloud data outside the area of ​​interest in the original point cloud to obtain a first point cloud; in specific implementation, the embodiment of the present application can provide a method for determining that the area within the flight heading target range of the drone is the area of ​​interest.

[0068] S102: fusing the first point cloud with the posture information of the drone to obtain a corrected second point cloud;

[0069] S103: using a probability statistical histogram to obtain the distribution probability of the number of point clouds at different heights, and using the distribution probability to perform adaptive elevation filtering on the second point cloud to remove the ground point cloud and retain the aerial power line point cloud to obtain a third point cloud; in specific implementation, the embodiment of the present application can provide

[0070] Counting probability distribution histograms of different heights of the second point cloud;

[0071] Determine the number of categories;

[0072] According to the classification number and the probability distribution histogram, a height threshold is adaptively generated using a natural breakpoint method;

[0073] The ground point cloud and the power line point cloud are divided and classified according to the height threshold, and the point cloud data below the height threshold is eliminated to obtain the third point cloud.

[0074] S104: Using a density clustering algorithm after determining the MinPts parameter to remove the isolated noise point cloud contained in the third point cloud to obtain a fourth point cloud; in specific implementation, the embodiment of the present application can provide

[0075] Using the Euclidean distance formula to calculate the features of the unknown data and the features corresponding to each data in the third point cloud, to obtain a distance distribution matrix of the third point cloud; arranging the distance distribution matrix in ascending order to obtain a KNN distribution matrix of the third point cloud;

[0076] Take the average distance of K value by column to get the Eps parameter list;

[0077] The core points are traversed with the Eps parameter as the neighborhood to find data points that are density-reachable with the core points, and clustered into clusters. The Eps and MinPts parameters corresponding to the K value when the number of clusters is stable and the density threshold is minimum are selected as the optimal parameters.

[0078] S105: Filter the fourth point cloud based on the random sampling consistency algorithm to obtain the target point cloud of the filtered power lines; in order to improve the search speed, the embodiment of the present application can also provide a tree data structure to accelerate the search speed of the density clustering algorithm.

[0079] In a specific implementation, the embodiment of the present application may provide that the fourth point cloud is screened based on the random sampling consistency algorithm, and the target point cloud of the power line after the screening is obtained includes:

[0080] Get the total number of each iteration;

[0081] Obtaining a current straight line and its number of inner points in each iteration, wherein the current straight line is obtained by fitting two points randomly selected from the fourth point cloud, and the number of inner points is the number of points whose distance to the current straight line is within a preset distance threshold;

[0082] Determine the maximum number of iterations for each iteration according to the inlier point ratio of the current straight line, where the inlier point ratio is the ratio of the number of inlier points to the total number of points of the fourth point cloud;

[0083] When the maximum number of iterations is less than the total number of iterations, the corresponding current straight line is determined to be a suboptimal straight line, and the point cloud corresponding to the suboptimal straight line is used as the target point cloud.

[0084] The interior point ratio is obtained, and the interior point ratio is calculated according to the following formula:

[0085]

[0086] Where t is the ratio of internal points, M is the number of internal points of the current line, and N is the total number of points in the point cloud data to be fitted;

[0087] The confidence level is obtained by calculating the confidence level according to the following formula:

[0088] p=1-(1-t2)k

[0089] Among them, p is the confidence level, k is the maximum number of iterations of the current internal point ratio;

[0090] The number of iterations k of the random sampling consensus algorithm is calculated by the following formula:

[0091]

[0092] Where: p represents the confidence level.

[0093] S106: Fitting the target point cloud based on the least squares method to obtain a straight line equation;

[0094] S107: Calculate the straight line equation to obtain the position information between the UAV and the power line.

[0095] The power line positioning method based on airborne radar provided in the embodiment of the present application is aimed at the needs of fast and accurate positioning and obstacle avoidance of small overhead power lines in the wild during the flight of the drone, and the relative distance between the drone and the power line is obtained based on the effective point cloud data. The probability statistical histogram is introduced for adaptive elevation filtering, the density clustering algorithm is used to remove isolated noise point clouds, and the random sampling consistency algorithm is used to obtain effective point cloud data. This method can achieve fast and accurate positioning of small power lines in space.

[0096] The method provided in the embodiments of the present application is described in detail below.

[0097] The overall algorithm flow of the method provided in the embodiment of the present application is as follows Figure 2 As shown, the specific implementation steps are:

[0098] For the original point cloud input by LiDAR (laser radar), the region of interest RoI is set within a certain range of the flight heading (for example, the width direction), and the point cloud data outside the region of interest RoI in the original point cloud is removed to obtain the first point cloud; the region of interest can set its direction and boundary value according to the scene requirements to meet the purpose of detecting different areas. In actual applications, the region of interest is set to the area within 2m in front of and on both sides of the drone.

[0099] The first point cloud is fused with the attitude information of the drone to obtain the corrected second point cloud; the laser radar is set on the drone, and it can continuously collect point cloud data related to the power line during the flight of the drone. By fusing the original point cloud data with the attitude information of the drone, the coordinates of the point cloud data can be preliminarily corrected. The correction result is as follows Figure 4 The attitude information includes the pitch angle, yaw angle and roll angle of the drone. The correction method is to rotate the point cloud in the opposite direction of the drone's attitude so that the ground point cloud is parallel to the flight direction of the drone.

[0100] For the second point cloud obtained in the above steps, the probability distribution histogram of different heights is statistically analyzed, and the height threshold is adaptively generated using the natural breakpoint method (set N = 2), and the point cloud data below the threshold is eliminated to obtain the third point cloud. The height threshold can be used to indicate the relationship between each point in the second point cloud and the actual position of the power line. Since there is a large elevation difference between the ground and the power line, it can be intuitively reflected in the probability statistical histogram, showing two humps and one valley, so it is easy to distinguish the aerial point cloud that can be used to indicate the position of the power line and the ground point cloud that needs to be eliminated.

[0101] The probability density function is one of the core concepts of probability theory. It is used to describe the probability distribution of continuous random variables and is a universal expression of probability calculation. When studying a random variable, it is not only necessary to see what values ​​it can take, but more importantly, the probability of it taking various values, which corresponds to the discretized frequency in actual use. It can also be understood that the probability density function is the frequency of the universal mathematical expression, while the frequency in statistics is the expression after discretization. The two are essentially the same, so the probability density function can be approximated by a histogram.

[0102] The probability distribution histogram uses the height of the rectangle to represent the frequency or frequency of the corresponding group. It can clearly show the frequency distribution of each group and easily show the difference in frequency between groups. This graphical representation makes the data distribution clear at a glance and very intuitive.

[0103] The probability distribution histogram can easily represent a large amount of data. The area of ​​each small rectangle represents the frequency of each group, and the area reflects the frequency of the data falling into each group. This representation method makes it possible to intuitively show the data distribution when processing a large amount of data, and even the data pattern that is not clear in the data table can be clearly displayed.

[0104] Although the probability distribution histogram reflects the frequency of data falling into each group in the form of area, this does not mean that the information of the original data is lost. On the contrary, this graphical representation is an information simplification strategy that helps analysts quickly understand the overall distribution of the data while retaining the key features of the original data. It is particularly suitable for use in scenarios with a large number of point clouds in the method provided in this application for probability statistics.

[0105] The natural breakpoint method is a flexible data analysis method that makes the analysis more effective by dividing large data sets into different subsets. The core idea of ​​this method is to maximize the similarity within each group and maximize the difference between external groups. Unlike clustering methods, the natural breakpoint method not only focuses on the similarity within the group, but also takes into account the range and number of elements between each group as close as possible. The principle of the natural breakpoint method is to find a set of groups in which the difference between each group is the largest, but the difference within each group is the smallest, which is called the "maximum difference-minimum difference" principle. It uses variance analysis or other measures to measure the difference between groups and makes adaptive adjustments based on the data distribution of each group, which is the so-called "adaptive" feature. The natural breakpoint method does not require the user to specify any specific input or output variables, but automatically finds the most effective grouping method, so it can help users complete the analysis quickly and accurately.

[0106] The natural breakpoint method has a wide range of application scenarios. It is suitable for situations where the data distribution characteristics are unclear and can most easily obtain ideal results. For example, in geography, the natural breakpoint method is often used to classify carbon emissions into different regional categories to reveal spatial distribution characteristics. In addition, the natural breakpoint method is also widely used in map making. By adjusting the sum of the variances of all values ​​in each category, the variance within the group is as small as possible, the variance between classes is as large as possible, and the segmentation points appear where the values ​​change dramatically.

[0107] Based on the third point cloud data in step 1, the DBSCAN algorithm parameters are obtained, and the third point cloud data is divided into different clusters; the DBSCAN algorithm is used to filter out the noise of the third point cloud data, and the Kd-tree tree is used to accelerate the search to obtain the point cloud data after removing the isolated noise.

[0108] Get the parameters of the DBSCAN algorithm. The specific steps are:

[0109] Get the KNN distribution matrix of the point cloud dataset;

[0110] Take the average distance of K value by column to get the Eps parameter list;

[0111] Taking Eps as the neighborhood, the expected value of the number of data points in the neighborhood of the core point is calculated, and the MinPts parameter is generated using the mathematical expectation method.

[0112] The MinPts parameter defines the minimum number of neighbors required for a data point to become a core object in the DBSCAN algorithm. The choice of this parameter is crucial to the effectiveness of clustering because it affects which points are considered core points, border points, or noise points.

[0113] In the specific implementation, the Euclidean distance formula is used to calculate the features of the unknown data and the features corresponding to each data in the data set D to obtain the distance distribution matrix of the data set D; then the distance distribution matrix is ​​arranged in ascending order to obtain the KNN distribution matrix, and the average value of the column is taken as the average distance of the K value to obtain the Eps parameter list.

[0114] Substitute the threshold parameters into the DBACAN clustering algorithm, read the point cloud dataset D, traverse the core points with Eps as the neighborhood, find data points with density reachable from the core points, cluster them, and select the Eps and MinPts parameters corresponding to the K value when the number of clusters is stable and the density threshold is minimum as the optimal parameters.

[0115] The DBSCAN algorithm is used to remove the isolated noise points contained in the third point cloud to obtain the fourth point cloud, and a Kd-tree is established to accelerate the search. Kd-tree (short for k-dimensional tree) is a tree data structure that stores instance points in k-dimensional space for fast retrieval. It is mainly used for searching key data in multidimensional space (such as range search and nearest neighbor search). KD tree is a special case of binary space segmentation tree. The DBSCAN algorithm parameters are obtained by filtering the second point cloud data, and the point cloud data is divided into different clusters; the DBSCAN algorithm defines a cluster as the maximum set of density-connected points, divides areas with sufficient density into clusters, and finds clusters of arbitrary shapes in noisy spatial data sets.

[0116] The point cloud data in the fourth point cloud is screened based on the random sampling consensus algorithm (RANSAC), the target point cloud corresponding to the screened power lines is obtained, and the data in the target point cloud is fitted based on the least squares method. The specific steps are as follows: Figure 3 As shown:

[0117] Get the total number of each iteration;

[0118] The current straight line and its number of inner points of each iteration are obtained. The current straight line is fitted by randomly selecting 2 points from the point cloud data to be fitted. The number of inner points is the number of points whose distance to the current straight line is within a preset distance threshold.

[0119] Randomly select two points from the target point cloud and determine the straight line; calculate the distance from the remaining points to the straight line; and determine whether the inner point is obtained based on whether the distance is less than a given threshold.

[0120] In a specific implementation, the maximum number of iterations k for each iteration is determined according to the interior point ratio of the current straight line, where the interior point ratio is the ratio of the number of interior points to the total number of points in the point cloud data to be fitted;

[0121] The interior point ratio is obtained, and the interior point ratio is calculated according to the following formula:

[0122]

[0123] Where t is the ratio of internal points, M is the number of internal points of the current line, and N is the total number of points in the point cloud data to be fitted;

[0124] The confidence level is obtained by calculating the confidence level according to the following formula:

[0125] p=1-(1-t2)k

[0126] Among them, p is the confidence level, k is the maximum number of iterations of the current internal point ratio;

[0127] When the maximum number of iterations is less than the total number of iterations, the corresponding current straight line is determined to be a suboptimal straight line.

[0128] The maximum number of iterations is determined by the following formula:

[0129]

[0130] In the formula, p is the confidence level.

[0131] After counting the number of internal points, determine whether it is less than the number of iterations k;

[0132] If it is less than the number of iterations k, two points are randomly selected from the target point cloud again and a new straight line is determined for iteration.

[0133] If it is not less than the number of iterations k, the point cloud set with the largest number of internal points is selected and the parameters of the spatial straight line equation are obtained by fitting using the least squares method.

[0134] The fitted straight line equation is calculated to obtain the relative position information such as the spatial distance and orientation between the UAV and the power line.

[0135] The power line positioning method based on airborne radar provided in the embodiment of the present application simplifies the algorithm for the specific scenario of low-altitude UAV obstacle avoidance. This method can obtain the distance between the UAV and the power line without modeling the power line, and has a faster operating speed and positioning accuracy.

[0136] When determining the point cloud data, the mathematical statistics method based on the probability statistical histogram uses the natural breakpoint method to automatically find the optimal height threshold for ground filtering. It has good versatility and will not cause the problem of excessive segmentation of power lines and intermittent power lines in complex terrain due to the fixed height threshold.

[0137] In the method of denoising point cloud, such as Figure 5As shown in the figure, according to the characteristics of the noise point distribution in the point cloud data, the DBSCAN algorithm is used to remove the noise points. Compared with a single filtering method, the DBSCAN algorithm has strong adaptability and can cluster the point cloud data into clusters, classify the core points, boundary points, and noise points in the point cloud data, remove obvious outliers, and ensure the accuracy of the final calculation results.

[0138] For power line fitting, such as Figure 6 As shown in the figure, DBSCAN has stronger anti-interference ability, and the method of fitting with RANSAC and least squares method is noise-insensitive. After the original point cloud is filtered and denoised, the number of power line point clouds represented is already small, but it can basically represent the distribution of power lines. Therefore, the algorithm does not need to set a large number of iterations, which makes the algorithm fitting fast, real-time, and able to quickly and accurately locate the power lines.

[0139] See also Figure 7 The embodiment of the present application can also provide a power line positioning device based on airborne radar, such as Figure 7 As shown, for executing the above-mentioned power line positioning method based on airborne radar, the device may include:

[0140] The first point cloud acquisition unit 701 is used to acquire the original point cloud of the power line collected by the airborne laser radar of the UAV, and remove the point cloud data outside the area of ​​interest in the original point cloud to obtain the first point cloud;

[0141] A second point cloud acquisition unit 702 is used to fuse the first point cloud with the posture information of the UAV to obtain a corrected second point cloud;

[0142] The third point cloud acquisition unit 703 is used to obtain the distribution probability of the number of point clouds at different heights by using a probability statistical histogram, so as to use the distribution probability to perform adaptive elevation filtering on the second point cloud to remove the ground point cloud and retain the aerial power line point cloud to obtain the third point cloud;

[0143] A fourth point cloud acquisition unit 704 is used to remove isolated noise point clouds contained in the third point cloud by using a density clustering algorithm after determining the MinPts parameter to obtain a fourth point cloud;

[0144] A target point cloud acquisition unit 705 is used to filter the fourth point cloud based on a random sampling consistency algorithm to obtain a target point cloud of the filtered power lines;

[0145] A fitting unit 706, configured to fit the target point cloud based on a least square method to obtain a straight line equation;

[0146] The position information calculation unit 707 is used to calculate the straight line equation to obtain the position information between the UAV and the power line.

[0147] The embodiment of the present application may also provide a power line positioning device based on an airborne radar, the device comprising a processor and a memory:

[0148] The memory is used to store program code and transmit the program code to the processor;

[0149] The processor is used to execute the steps of the above-mentioned power line positioning method based on airborne radar according to the instructions in the program code.

[0150] like Figure 8 As shown, a power line positioning device based on airborne radar provided in an embodiment of the present application may include: a processor 10, a memory 11, a communication interface 12 and a communication bus 13. The processor 10, the memory 11 and the communication interface 12 all communicate with each other through the communication bus 13.

[0151] In the embodiment of the present application, the processor 10 may be a central processing unit (CPU), an application specific integrated circuit, a digital signal processor, a field programmable gate array or other programmable logic devices.

[0152] The processor 10 may call a program stored in the memory 11. Specifically, the processor 10 may execute operations in an embodiment of the power line positioning method based on airborne radar.

[0153] The memory 11 is used to store one or more programs, which may include program codes, and the program codes include computer operation instructions. In the embodiment of the present application, the memory 11 at least stores programs for implementing the following functions:

[0154] Obtaining an original point cloud of power lines collected by an airborne laser radar of the UAV, and removing point cloud data outside a region of interest in the original point cloud to obtain a first point cloud;

[0155] Fusing the first point cloud with the posture information of the UAV to obtain a corrected second point cloud;

[0156] A probability statistical histogram is used to obtain the distribution probability of the number of point clouds at different heights, so as to use the distribution probability to perform adaptive elevation filtering on the second point cloud to remove the ground point cloud and retain the aerial power line point cloud to obtain a third point cloud;

[0157] Using a density clustering algorithm after determining the MinPts parameter to remove isolated noise point clouds contained in the third point cloud to obtain a fourth point cloud;

[0158] Screening the fourth point cloud based on a random sampling consistency algorithm to obtain a screened target point cloud of power lines;

[0159] Fitting the target point cloud based on the least squares method to obtain a straight line equation;

[0160] The linear equation is calculated to obtain the position information between the UAV and the power line.

[0161] In addition, the memory 11 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device or other volatile solid-state storage device.

[0162] The communication interface 12 may be an interface of a communication module, and is used to connect to other devices or systems.

[0163] Of course, it should be noted that Figure 8 The structure shown does not constitute a limitation on the power line positioning device based on airborne radar in the embodiment of the present application. In actual applications, the power line positioning device based on airborne radar may include: Figure 8 More or fewer components than shown, or combinations of certain components.

[0164] The embodiment of the present application may also provide a computer-readable storage medium, wherein the computer-readable storage medium is used to store program code, and the program code is used to execute the steps of the above-mentioned power line positioning method based on airborne radar.

[0165] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0166] It can be known from the description of the above implementation methods that those skilled in the art can clearly understand that the present application can be implemented by means of software plus a necessary general hardware platform. Based on such an understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product can be stored in a storage medium such as ROM / RAM, a magnetic disk, an optical disk, etc., and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in the various embodiments of the present application or certain parts of the embodiments.

[0167] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can refer to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system or system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can refer to the partial description of the method embodiment. The system and system embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without creative work.

[0168] The above description is only a preferred embodiment of the present invention and is not intended to limit the protection scope of the present invention. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention are included in the protection scope of the present invention.

Claims

1. A power line positioning method based on airborne radar, It is characterized in that include: Obtaining an original point cloud of power lines collected by an airborne laser radar of the UAV, and removing point cloud data outside a region of interest in the original point cloud to obtain a first point cloud; Fusing the first point cloud with the posture information of the UAV to obtain a corrected second point cloud; A probability statistical histogram is used to obtain the distribution probability of the number of point clouds at different heights, and the distribution probability is used to perform adaptive elevation filtering on the second point cloud to remove the ground point cloud and retain the aerial power line point cloud to obtain a third point cloud; Using a density clustering algorithm after determining the MinPts parameter to remove isolated noise point clouds contained in the third point cloud to obtain a fourth point cloud; Screening the fourth point cloud based on a random sampling consistency algorithm to obtain a screened target point cloud of power lines; Fitting the target point cloud based on the least squares method to obtain a straight line equation; The linear equation is calculated to obtain the position information between the UAV and the power line.

2. The power line positioning method based on airborne radar according to claim 1, It is characterized in that Determine the area within the target range of the UAV flight heading as the region of interest.

3. The power line positioning method based on airborne radar according to claim 1, It is characterized in that Counting probability distribution histograms of different heights of the second point cloud; Determine the number of categories; According to the classification number and the probability distribution histogram, a height threshold is adaptively generated using a natural breakpoint method; The ground point cloud and the power line point cloud are divided and classified according to the height threshold, and the point cloud data below the height threshold is eliminated to obtain the third point cloud.

4. The power line positioning method based on airborne radar according to claim 1, It is characterized in that Using the Euclidean distance formula to calculate the features of the unknown data and the features corresponding to each data in the third point cloud, to obtain a distance distribution matrix of the third point cloud; arranging the distance distribution matrix in ascending order to obtain a KNN distribution matrix of the third point cloud; Take the average distance of K value by column to get the Eps parameter list; The core points are traversed with the Eps parameter as the neighborhood to find data points that are density-reachable with the core points, and clustered into clusters. The Eps and MinPts parameters corresponding to the K value when the number of clusters is stable and the density threshold is minimum are selected as the optimal parameters.

5. The power line positioning method based on airborne radar according to claim 1, It is characterized in that The search speed of the density clustering algorithm is accelerated by the established tree data structure.

6. The power line positioning method based on airborne radar according to claim 1, It is characterized in that The filtering the fourth point cloud based on the random sampling consistency algorithm to obtain the filtered target point cloud of the power line comprises: Get the total number of each iteration; Obtaining a current straight line and its number of inner points in each iteration, wherein the current straight line is obtained by fitting two points randomly selected from the fourth point cloud, and the number of inner points is the number of points whose distance to the current straight line is within a preset distance threshold; Determine the maximum number of iterations for each iteration according to the inlier point ratio of the current straight line, where the inlier point ratio is the ratio of the number of inlier points to the total number of points of the fourth point cloud; When the maximum number of iterations is less than the total number of iterations, the corresponding current straight line is determined to be a suboptimal straight line, and the point cloud corresponding to the suboptimal straight line is used as the target point cloud.

7. The power line positioning method based on airborne radar according to claim 6, It is characterized in that The interior point ratio is obtained, and the interior point ratio is calculated according to the following formula: Where t is the ratio of internal points, M is the number of internal points of the current line, and N is the total number of points in the point cloud data to be fitted; The confidence level is obtained by calculating the confidence level according to the following formula: p=1-(1-t 2 ) k Among them, p is the confidence level, k is the maximum number of iterations of the current internal point ratio; The number of iterations k of the random sampling consensus algorithm is calculated by the following formula: Where: p represents the confidence level.

8. A power line positioning device based on airborne radar, It is characterized in that The device is used to execute the power line positioning method based on airborne radar according to any one of claims 1 to 7, the device comprising: A first point cloud acquisition unit is used to acquire an original point cloud of power lines collected by an airborne laser radar of the UAV, and remove point cloud data outside a region of interest in the original point cloud to obtain a first point cloud; A second point cloud acquisition unit, configured to fuse the first point cloud with the posture information of the UAV to obtain a corrected second point cloud; A third point cloud acquisition unit is used to obtain the distribution probability of the number of point clouds at different heights by using a probability statistical histogram, so as to perform adaptive elevation filtering on the second point cloud by using the distribution probability to obtain a third point cloud; A fourth point cloud acquisition unit, configured to remove isolated noise point clouds contained in the third point cloud by using a density clustering algorithm after determining the MinPts parameter to obtain a fourth point cloud; A target point cloud acquisition unit, configured to screen the fourth point cloud based on a random sampling consistency algorithm to obtain a target point cloud of the power lines after screening; A fitting unit, used for fitting the target point cloud based on the least square method to obtain a straight line equation; A position information calculation unit is used to calculate the straight line equation to obtain the position information between the UAV and the power line.

9. A power line positioning device based on airborne radar, It is characterized in that The device comprises a processor and a memory: The memory is used to store program code and transmit the program code to the processor; The processor is used to execute the power line positioning method based on airborne radar according to any one of claims 1-7 according to the instructions in the program code.

10. A computer-readable storage medium, It is characterized in that The computer-readable storage medium is used to store program codes, and the program codes are used to execute the power line positioning method based on airborne radar as described in any one of claims 1-7.

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