Three-dimensional point cloud edge extraction method based on grid numerical field, medium and equipment

Abstract

The invention relates to the technical field of three-dimensional scanning, in particular to a three-dimensional point cloud edge extraction method based on a grid numerical field, and the method comprises the steps: S1, adding a grid attribute to each point cloud in a three-dimensional point cloud model; s2, performing X thinning on the three-dimensional point cloud model to obtain a point cloud set P1; s3, clustering according to the grid attributes to obtain a plurality of grid clusters; s4, calculating to obtain an outsourcing convex polygon of a single grid cluster, and obtaining a vertex point cloud set; s5, when X is less than 100%, if no discrete grid exists in the vertex point cloud set, determining that the vertex point cloud set is a grid cluster point cloud edge, otherwise, enabling X to be equal to 2X, and entering S6; when X is greater than or equal to 100%, the vertex point cloud set is a grid cluster point cloud edge; s6, subtracting P1 from the three-dimensional point cloud model to obtain P2; performing X dilution on the P2 to obtain P3; and S7, adding the points in the P3 into the outsourcing convex polygon to obtain a new vertex point cloud set, and returning to S5. The calculation amount is reduced, and the edge extraction precision is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of three-dimensional scanning technology, and in particular to a three-dimensional point cloud edge extraction method based on a grid numerical field. Background Art

[0002] In recent years, with the rapid development of remote sensing technology, LiDAR and 3D scanning technology, 3D point cloud models have become an important data source in many fields such as geographic information system (GIS), urban planning, architectural design, and cultural relics protection. With its high precision and rich geometric information, 3D point cloud models have shown great potential in terrain analysis, object recognition, etc. However, how to effectively extract useful information from these complex 3D data, especially in the case of large-scale data sets, is still a technical problem.

[0003] In 3D point cloud data processing, it is often necessary to combine point clouds with gridded numerical fields to analyze specific properties, such as terrain changes, light intensity, etc. Each grid in the numerical field carries attribute information about the area, such as height, density, temperature, etc. This combination can help better understand the 3D scene and provide a basis for further data analysis. After cluster analysis, it is often necessary to extract the edges of the 3D point cloud corresponding to the clustered grids. Existing methods are often based on some characteristics of the point cloud itself and lack mapping with grid attributes. Summary of the invention

[0004] The purpose of the present invention is to provide a three-dimensional point cloud edge extraction method based on a grid numerical field for efficiently and accurately extracting edges. The specific technical solution is as follows:

[0005] The present invention provides a three-dimensional point cloud edge extraction method based on a grid numerical field, comprising the following steps:

[0006] S1: Construct a grid data field and a three-dimensional point cloud model of a certain area; superimpose the grid data field and the three-dimensional point cloud model of the area by using feature point matching or least squares method, that is, add corresponding grid attributes to each point cloud in the three-dimensional point cloud model;

[0007] S2: Thinning the 3D point cloud model to obtain a thinned 3D point cloud set P1, where the thinning rate is X;

[0008] S3: clustering the grids in the grid data field according to the grid attributes using a clustering algorithm to obtain several grid clusters;

[0009] S4: Use the convex hull algorithm to calculate the outer convex polygon corresponding to the single grid cluster, and obtain the vertex point cloud set of the outer convex polygon;

[0010] S5: judgment: when X < 100%, if all meshes corresponding to the vertex point cloud set have at least a common edge with other meshes, then the vertex point cloud set is the point cloud edge corresponding to the extracted mesh cluster, otherwise, let X = 2X and enter S6; when X ≥ 100%, then the vertex point cloud set is the point cloud edge corresponding to the extracted mesh cluster;

[0011] S6: subtract the thinned three-dimensional point cloud set P1 from the three-dimensional point cloud model to obtain the point cloud set P2; and thin the point cloud set P2 according to the thinning rate X to obtain the thinned three-dimensional point cloud set P3;

[0012] S7: Use the incremental convex hull algorithm to add the points in the thinned three-dimensional point cloud set P3 one by one to the outer convex polygon in S4 to update the outer convex polygon, obtain the updated vertex point cloud set, and return to S5.

[0013] Optionally, the steps between S3 and S4 further include:

[0014] Number each grid in the grid data field to obtain a number set Setg of the grids in a single grid cluster; and retrieve all point clouds in the single grid cluster from the thinned three-dimensional point cloud set P1 according to the number set Setg to form a point cloud set P of the grid cluster. t ;

[0015] In S7, if the points in the three-dimensional point cloud set P3 after thinning are located within the outer convex polygon, there is no need to update the outer convex polygon. If the points in the three-dimensional point cloud set P3 after thinning are located outside the outer convex polygon, it is necessary to update the outer convex polygon in S4 by inserting new edges or vertices.

[0016] Optionally, the S4 includes:

[0017] S4.1. Construct a coordinate projection mapping function and use the coordinate projection mapping function to transform the point cloud set P t The point cloud coordinates in are projected from the spatial coordinates to the plane coordinates to obtain the point cloud set P represented by the plane coordinates. tf ;

[0018] S4.2. Use the convex hull algorithm to obtain the point cloud set P tf The vertex set P of the outer convex polygon vertex ;

[0019] S4.3. Construct the inverse function of the coordinate projection mapping function and use the inverse function of the coordinate projection mapping function to transform the vertex set P vertex The point cloud coordinates in are projected from the plane coordinates to the space coordinates to obtain the vertex point cloud set P represented by the space coordinates. vk .

[0020] Optionally, the S4.2 includes:

[0021] S4.2.1. Prepare data and set point cloud set P tf The plane coordinates of the midpoint cloud are expressed as (x i ,y i ), where i is the point cloud set P tf The point cloud number in ;

[0022] S4.2.2. Find the lowest point and the point cloud set P tf The point with the lowest y value in the convex hull is used as the starting point for constructing the convex hull; if there are multiple such points, the point with the smallest x value is selected as the starting point, where: the coordinates of the starting point are marked as (x0, y0);

[0023] S4.2.3. Calculate the vector, the starting point and the point cloud set P respectively tf The vector v between the points other than the starting point i ;

[0024] S4.2.4. Calculate the polar angle and calculate each vector v i The polar angle to the positive x-axis, where: the polar angle is the counterclockwise angle from the positive x-axis to the vector;

[0025] S4.2.5. Sort all non-starting points in order of polar angle from smallest to largest, where: if there are two or more points with the same polar angle, the point farther from the starting point should be placed first;

[0026] S4.2.6. Construct the convex hull by using Graham scanning method or Jarvis wrapping method, that is, enclosing the convex polygon, and obtain the vertex point cloud set P of the enclosing convex polygon vertex .

[0027] Optionally, in S4.2.4, the specific calculation formula of the polar angle is as follows:

[0028] θ i =atan2(y i -y0,x i -x0);

[0029] Where: θ i v i Polar angle from the positive x-axis.

[0030] Optionally, between S4 and S5, the following steps are also included:

[0031] The mesh number of each point cloud in the vertex point cloud set is extracted, and the mesh number set of the vertex point cloud is obtained after removing the repeated mesh numbers. The meshes corresponding to the mesh number set of the vertex point cloud are all the meshes corresponding to the vertex point cloud set.

[0032] Optionally, in S2, X≤10%.

[0033] The present invention also provides a readable storage medium having computer program instructions stored thereon, and when the computer program instructions are executed by a processor, the three-dimensional point cloud edge extraction method based on grid numerical field as described above is implemented.

[0034] The present invention also provides an electronic device, comprising: at least one processor, at least one memory and computer program instructions stored in the memory. When the computer program instructions are executed by the processor, the three-dimensional point cloud edge extraction method based on grid numerical field as described above is implemented.

[0035] The present invention superimposes the grid numerical field with the three-dimensional point cloud model, giving the point cloud points the attribute information of the grid, and realizes accurate edge extraction based on the attribute. This combination effectively improves the edge extraction capability of complex terrain and structures, and is suitable for multiple fields such as geographic information systems and environmental monitoring.

[0036] By introducing an incremental convex hull algorithm, we avoid recalculating the convex hull for the entire point set each time, and instead dynamically maintain the convex hull set and gradually add new points for updating. This method can greatly reduce the amount of calculation, especially in multiple iterative calculations, which can significantly shorten the processing time and improve efficiency.

[0037] The clustering algorithm is used to cluster the grids, so that the edge extraction operation can be concentrated on a specific area to avoid global processing. At the same time, the original 3D point cloud model is simplified through the thinning algorithm to reduce the redundant data of the point cloud, thereby accelerating the subsequent calculation process; and the mechanism of dynamic adjustment of the thinning rate ensures that even at a higher thinning rate, enough key edge points can be retained, improving the overall processing efficiency.

[0038] In addition to the above-described purposes, features and advantages, the present invention has other purposes, features and advantages. The present invention will be further described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] The drawings constituting a part of this application are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0040] Figure 1 is a schematic diagram of a three-dimensional point cloud model in an embodiment of the present invention;

[0041] Figure 2 Schematic diagram of a grid cluster obtained by clustering in an embodiment of the present invention. DETAILED DESCRIPTION

[0042] The embodiments of the present invention are described in detail below with reference to the accompanying drawings, but the present invention can be implemented in many different ways as defined and covered by the claims.

[0043] In one embodiment, see Figure 1 and Figure 2 , a three-dimensional point cloud edge extraction method based on grid numerical field includes the following steps:

[0044] S1: Construct a grid data field and a three-dimensional point cloud model of a certain area; superimpose the grid data field and the three-dimensional point cloud model of the area by using feature point matching or least squares method, that is, add corresponding grid attributes to each point cloud in the three-dimensional point cloud model; the grid attributes include temperature, humidity, altitude, light intensity, pollution concentration and any other physical quantity or abstract quantity and grid number.

[0045] A grid data field refers to a structured way of representing spatial data, which organizes data by dividing a continuous space into a series of discrete units (i.e., grids). Each grid unit carries information about specific attributes in the area, which can be physical quantities (such as temperature, humidity, altitude, etc.) or abstract quantities (such as light intensity, pollution concentration, etc.). Grid data fields are widely used in many fields such as geographic information systems (GIS), weather forecasting, environmental monitoring, and urban planning. The grids include both regular grids and irregular grids. The attributes of the grids can be represented by floating-point numbers. Each grid has its own number, which is generally a positive integer.

[0046] A 3D point cloud terrain model is a digital model that represents the terrain surface through 3D coordinates. It consists of a large number of points, each of which has precise 3D coordinates (X, Y, Z), and these points together depict the 3D shape of the terrain. The 3D coordinates refer to the coordinates in the spatial rectangular 3D coordinate system, and the reference ellipsoid can be determined according to the actual situation.

[0047] The process of superimposing the 3D point cloud terrain model with the grid data field is to match each point in the 3D point cloud model with the grid cell in the grid data field where it is located, so as to give each point cloud point the attribute information carried by the grid cell. The main purpose of this process is to combine the geometric information of the point cloud model with the attribute information of the grid data field to achieve more comprehensive data analysis and more accurate decision support. One grid may correspond to multiple point clouds.

[0048] The three-dimensional point cloud terrain model and the grid data field can be superimposed by solving the transformation matrix using techniques such as feature point matching or least squares method; or the method in invention patent ZL202011110192.3 can be used. The ultimate goal is to achieve the mapping relationship between the grid and the point cloud, that is, to add an attribute Sp to the point cloud, which represents the corresponding grid number.

[0049] S2: Thinning the three-dimensional point cloud model to obtain a thinned three-dimensional point cloud set P1, where: the thinning rate is X, X≤10%;

[0050] S3: clustering the grids in the grid data field according to the grid attributes using a clustering algorithm to obtain a number of grid clusters; wherein the grid attributes are selected according to actual needs, and in the embodiment, the grid attribute is light intensity, and a number of grids with light intensity greater than a certain threshold are selected to form a cluster. The effect that can be achieved is that the light intensity of the grids in this cluster is greater than the threshold, and these grids are all connected together.

[0051] Also included between S3 and S4:

[0052] Number each grid in the grid data field to obtain a number set Setg of the grids in a single grid cluster; and retrieve all point clouds in the single grid cluster from the thinned three-dimensional point cloud set P1 according to the number set Setg to form a point cloud set P of the grid cluster. t ;

[0053] In S7, if the points in the three-dimensional point cloud set P3 after thinning are located within the outer convex polygon, there is no need to update the outer convex polygon. If the points in the three-dimensional point cloud set P3 after thinning are located outside the outer convex polygon, it is necessary to update the outer convex polygon in S4 by inserting new edges or vertices.

[0054] S4: Calculate the outer convex polygon corresponding to the grid cluster using the convex hull algorithm, and obtain the vertex point cloud set of the outer convex polygon;

[0055] S4 includes:

[0056] S4.1. Construct a coordinate projection mapping function and use the coordinate projection mapping function to transform the point cloud set P t The point cloud coordinates in are projected from the spatial coordinates to the plane coordinates to obtain the point cloud set P represented by the plane coordinates (x, y). tf ;

[0057] S4.2. Use the convex hull algorithm to obtain the point cloud set P tf The vertex set P of the outer convex polygon vertex ;

[0058] S4.2 includes:

[0059] S4.2.1. Prepare data and set point cloud set P tf The plane coordinates of the midpoint cloud are expressed as (x i ,y i ), where i is the point cloud set P tf The point cloud number in ;

[0060] S4.2.2. Find the lowest point and the point cloud set P tf The point with the lowest y value in the convex hull is used as the starting point for constructing the convex hull; if there are multiple such points, the point with the smallest x value is selected as the starting point, where: the coordinates of the starting point are marked as (x0, y0);

[0061] S4.2.3. Calculate the vector, the starting point and the point cloud set P respectively tf The vector v between the points other than the starting point i ;

[0062] S4.2.4. Calculate the polar angle and calculate each vector v i The polar angle to the positive x-axis, where: the polar angle is the counterclockwise angle from the positive x-axis to the vector;

[0063] The specific calculation formula of the polar angle is as follows:

[0064] θ i =atan2(y i -y0,x i -x0);

[0065] Where: θ i v i Polar angle from the positive x-axis.

[0066] S4.2.5. Sort all non-starting points in order of polar angle from smallest to largest; where: if there are two or more points with the same polar angle, the point farther from the starting point should be placed in front;

[0067] S4.2.6. Construct the convex hull by using Graham scanning method or Jarvis wrapping method, that is, enclosing the convex polygon, and obtain the vertex point cloud set P of the enclosing convex polygon vertex .

[0068] In S4.2.6, the specific steps for using the Graham scanning method are as follows:

[0069] Select point cloud set P tf The two points with the smallest mid-polar angle are used as the initial stack;

[0070] Add point cloud set P one by one in polar angle order tf Points outside the initial stack;

[0071] For each newly added point p i , check whether the last three points on the top of the stack form a left turn, that is, the area of ​​the triangle formed is greater than 0. If not, pop the stack vertices until the left turn condition is met; i Push into the stack;

[0072] When all points have been processed, the remaining points in the stack are the vertex set P of the convex hull. vertex .

[0073] S4.3. Construct the inverse function of the coordinate projection mapping function and use the inverse function of the coordinate projection mapping function to transform the vertex set P vertex The point cloud coordinates in are projected from the plane coordinates to the space coordinates to obtain the vertex point cloud set P represented by the space coordinates. vk .

[0074] Between S4 and S5, there are also:

[0075] The mesh number of each point cloud in the vertex point cloud set is extracted, and the mesh number set of the vertex point cloud is obtained after removing the repeated mesh numbers. The meshes corresponding to the mesh number set of the vertex point cloud are all the meshes corresponding to the vertex point cloud set.

[0076] S5: Judgment: When X < 100%, if all meshes corresponding to the vertex point cloud set have at least a common edge with other meshes, then the vertex point cloud set is the point cloud edge corresponding to the extracted mesh cluster; otherwise, let X = 2X and enter S6; when X ≥ 100%, then the vertex point cloud set is the point cloud edge corresponding to the extracted mesh cluster; among them, the method of image recognition can be used to identify whether the mesh has a common edge with other meshes

[0077] S6: subtract the thinned three-dimensional point cloud set P1 from the three-dimensional point cloud model to obtain the point cloud set P2; and thin the point cloud set P2 according to the thinning rate X to obtain the thinned three-dimensional point cloud set P3;

[0078] S7: Use the incremental convex hull algorithm to add the points in the thinned three-dimensional point cloud set P3 one by one to the outer convex polygon in S4 to update the outer convex polygon, obtain the updated vertex point cloud set, and return to S5.

[0079] In S7, if the points in the thinned point cloud set P3 are located inside the outer convex polygon, there is no need to update the outer convex polygon. If the points in the thinned point cloud set P3 are located outside the outer convex polygon, it is necessary to update the outer convex polygon by inserting new edges or vertices.

[0080] A common way to determine whether a point lies inside the convex hull is to use the ray casting method or the triangle covering method.

[0081] Regarding the ray intersection method, this method involves emitting a ray from the point to be tested in any direction and counting the number of times the ray intersects the edge of the convex hull. If the number of times the ray intersects the edge of the convex hull is an even number (including zero), then the point is outside the convex hull; if it is an odd number, then the point is inside the convex hull.

[0082] The specific steps are as follows:

[0083] Select a direction to emit a ray from the point to be measured.

[0084] Count the number of intersections of the ray with all edges that form the convex hull.

[0085] If the number of intersections is odd, the point is inside the convex hull; if it is even, the point is outside the convex hull.

[0086] Regarding the triangle covering method, the convex hull is decomposed into a series of triangles, and then the test point is checked whether it is inside any triangle. This method is applicable to the convex hull in three-dimensional space.

[0087] For the two-dimensional case, a simpler method can be used to determine whether a point is inside a polygon. This can be done by checking the orientation of each triangle, specifically, checking if there is at least one triangle such that the point is to the left (counterclockwise). If this is true for all triangles, then the point is inside the convex hull.

[0088] This embodiment also provides a readable storage medium having computer program instructions stored thereon. When the computer program instructions are executed by a processor, the three-dimensional point cloud edge extraction method based on the grid numerical field as described above is implemented.

[0089] It should be noted that the device 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. In addition, in the accompanying drawings of the device embodiments provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, which may be specifically implemented as one or more communication buses or signal lines. A person of ordinary skill in the art may understand and implement it without paying any creative effort.

[0090] This embodiment also includes an electronic device, including: at least one processor, at least one memory, and computer program instructions stored in the memory. When the computer program instructions are executed by the processor, the three-dimensional point cloud edge extraction method based on grid numerical field as described above is implemented.

[0091] Exemplarily, the computer program may be divided into one or more modules / units, which are stored in the memory and executed by the processor to implement the present invention. The one or more modules / units may be a series of computer program instruction segments capable of implementing specific functions, which are used to describe the execution process of the computer program in the electronic device.

[0092] The electronic device may be a computing device such as a mobile phone, a desktop computer, a notebook, a PDA, a cloud server, etc. The electronic device may include, but is not limited to, a processor and a memory. For example, the electronic device may also include an input / output device, a network access device, a bus, etc.

[0093] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the electronic device, and uses various interfaces and lines to connect various parts of the entire electronic device.

[0094] The memory can be used to store the computer program and / or module, and the processor implements the computer program by running or executing the computer program and / or module stored in the memory, and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area can store data created according to the use of the mobile phone (such as audio data, a phone book, etc.), etc. In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (SecureDigital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0095] Wherein, if the module / unit integrated in the electronic device is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Wherein, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium, etc. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0096] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A three-dimensional point cloud edge extraction method based on grid numerical field, characterized in that: The steps include: S1: Construct a grid data field and a three-dimensional point cloud model of a certain area; superimpose the grid data field and the three-dimensional point cloud model of the area by using feature point matching or least squares method, that is, add corresponding grid attributes to each point cloud in the three-dimensional point cloud model; S2: Thinning the 3D point cloud model to obtain a thinned 3D point cloud set P1, where the thinning rate is X; S3: clustering the grids in the grid data field according to the grid attributes using a clustering algorithm to obtain several grid clusters; S4: Use the convex hull algorithm to calculate the outer convex polygon corresponding to the single grid cluster, and obtain the vertex point cloud set of the outer convex polygon; S5: judgment: when X < 100%, if all meshes corresponding to the vertex point cloud set have at least a common edge with other meshes, then the vertex point cloud set is the point cloud edge corresponding to the extracted mesh cluster, otherwise, let X = 2X and enter S6; when X ≥ 100%, then the vertex point cloud set is the point cloud edge corresponding to the extracted mesh cluster; S6: subtract the thinned three-dimensional point cloud set P1 from the three-dimensional point cloud model to obtain the point cloud set P2; and thin the point cloud set P2 according to the thinning rate X to obtain the thinned three-dimensional point cloud set P3; S7: Use the incremental convex hull algorithm to add the points in the thinned three-dimensional point cloud set P3 one by one to the outer convex polygon in S4 to update the outer convex polygon, obtain the updated vertex point cloud set, and return to S5.

2. The three-dimensional point cloud edge extraction method based on grid numerical field according to claim 1 is characterized in that: The S3 and S4 also include: Number each grid in the grid data field to obtain a number set Setg of the grids in a single grid cluster; and retrieve all point clouds in the single grid cluster from the thinned three-dimensional point cloud set P1 according to the number set Setg to form a point cloud set P of the grid cluster. t ; In S7, if the points in the three-dimensional point cloud set P3 after thinning are located within the outer convex polygon, there is no need to update the outer convex polygon. If the points in the three-dimensional point cloud set P3 after thinning are located outside the outer convex polygon, it is necessary to update the outer convex polygon in S4 by inserting new edges or vertices.

3. The three-dimensional point cloud edge extraction method based on grid numerical field according to claim 2 is characterized in that: The S4 includes: S4.

1. Construct a coordinate projection mapping function and use the coordinate projection mapping function to transform the point cloud set P t The point cloud coordinates in are projected from the spatial coordinates to the plane coordinates to obtain the point cloud set P represented by the plane coordinates. tf ; S4.

2. Use the convex hull algorithm to obtain the point cloud set P tf The vertex set P of the outer convex polygon vertex ; S4.

3. Construct the inverse function of the coordinate projection mapping function and use the inverse function of the coordinate projection mapping function to transform the vertex set P vertex The point cloud coordinates in are projected from the plane coordinates to the space coordinates to obtain the vertex point cloud set P represented by the space coordinates. vk .

4. The three-dimensional point cloud edge extraction method based on grid numerical field according to claim 3 is characterized in that: The S4.2 includes: S4.2.

1. Prepare data and set point cloud set P tf The plane coordinates of the midpoint cloud are expressed as (x i ,y i ), where i is the point cloud set P tf The point cloud number in ; S4.2.

2. Find the lowest point and the point cloud set P tf The point with the lowest y value in the convex hull is used as the starting point for constructing the convex hull; if there are multiple such points, the point with the smallest x value is selected as the starting point, where: the coordinates of the starting point are marked as (x0, y0); S4.2.

3. Calculate the vector, the starting point and the point cloud set P respectively tf The vector v between the points other than the starting point i ; S4.2.

4. Calculate the polar angle and calculate each vector v i The polar angle to the positive x-axis, where: the polar angle is the counterclockwise angle from the positive x-axis to the vector; S4.2.

5. Sort all non-starting points in order of polar angle from smallest to largest, where: if there are two or more points with the same polar angle, the point farther from the starting point should be placed first; S4.2.

6. Construct the convex hull by using Graham scanning method or Jarvis wrapping method, that is, enclosing the convex polygon, and obtain the vertex point cloud set P of the enclosing convex polygon vertex .

5. The three-dimensional point cloud edge extraction method based on grid numerical field according to claim 4 is characterized in that: In S4.2.4, the specific calculation formula for the polar angle is as follows: θ i =here2(y i −y0,x i -x0)? Where: θ i v i Polar angle from the positive x-axis.

6. The three-dimensional point cloud edge extraction method based on grid numerical field according to any one of claims 2 to 5, characterized in that: The S4 and S5 also include: The mesh number of each point cloud in the vertex point cloud set is extracted, and the mesh number set of the vertex point cloud is obtained after removing the repeated mesh numbers. The meshes corresponding to the mesh number set of the vertex point cloud are all the meshes corresponding to the vertex point cloud set.

7. The three-dimensional point cloud edge extraction method based on grid numerical field according to any one of claims 1 to 5, characterized in that: The grid attributes include temperature, humidity, altitude, light intensity, pollution concentration, and any one of other physical quantities or abstract quantities and a grid number.

8. The three-dimensional point cloud edge extraction method based on grid numerical field according to any one of claims 1 to 5, characterized in that: In S2, X≤10%.

9. A readable storage medium, characterized in that: Computer program instructions are stored thereon, and when the computer program instructions are executed by a processor, a three-dimensional point cloud edge extraction method based on a grid numerical field as described in any one of claims 1 to 8 is implemented.

10. An electronic device, characterized in that: include: At least one processor, at least one memory and computer program instructions stored in the memory, when the computer program instructions are executed by the processor, a three-dimensional point cloud edge extraction method based on a grid numerical field as described in any one of claims 1 to 8.

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