Point cloud coverage verification method and apparatus, electronic device and storage medium

Abstract

Embodiments of the present disclosure provide a point cloud coverage verification method and apparatus, an electronic device, and a storage medium. The method comprises: performing rasterization processing on a polygonal region corresponding to a target region to undergo detection, to obtain a first grid point set; performing rasterization processing on point cloud data of the target region, to obtain a second grid point set; on the basis of the first grid point set, the second grid point set and grid coordinates, determining a section covered by a point cloud and a section not covered by the point cloud in the polygonal region, and calculating the point cloud coverage rate of the polygonal region; determining whether the point cloud coverage rate of the polygonal region is less than a preset threshold value, and if so, on the basis of a grid point set of the section not covered by the point cloud, determining a section for which acquisition is to be performed again. The point cloud coverage verification method provided by the embodiments of the present disclosure avoids unnecessary point cloud data acquisition being repeated multiple times, while ensuring that the acquired data is complete, thereby improving acquisition efficiency.

Description

Point cloud coverage verification method, device, electronic device and storage medium Technical Field

[0001] The embodiments of the present disclosure relate to the field of autonomous driving technology, and more particularly to a point cloud coverage verification method, device, electronic device, and storage medium. Background Art

[0002] In the field of autonomous driving, LiDAR or vision sensors can capture point cloud data of objects in three-dimensional space, assisting autonomous vehicles in positioning and obstacle perception. Currently, most autonomous driving companies use point cloud data collected by LiDAR or vision sensors to build high-precision maps to assist autonomous driving.

[0003] However, when collecting high-precision map data, there may be problems with missing point cloud data in the collection area due to some reasons. For example, detailed route planning of the collection area is not carried out before collection, resulting in an unreasonable collection route and incomplete coverage of the collection area, or the point cloud data cannot be uploaded normally during the collection process, resulting in missing collected point cloud data.

[0004] Furthermore, in specialized autonomous driving environments, such as mining areas, the terrain of the working area is constantly changing. For example, excavators digging in the loading area cause changes in the excavation area, while soil transport vehicles dump soil in the discharge area. HD maps of these changing areas require timely updates to ensure their reliability.

[0005] In existing technologies, when HD maps are missing data or when the HD map of a specific area needs to be updated, multiple acquisitions are usually performed on the same area. However, such repeated acquisitions are inefficient and lead to unnecessary repeated acquisitions, resulting in a waste of resources. Summary of the Invention

[0006] The embodiments of the present disclosure provide a point cloud coverage verification method, device, electronic device and storage medium, which can verify the point cloud coverage of the area to be collected, thereby efficiently collecting the area to be collected and avoiding the problem of repeated collection.

[0007] In a first aspect, an embodiment of the present disclosure provides a point cloud coverage verification method, comprising:

[0008] Performing rasterization processing on the polygonal area corresponding to the target area to be measured to obtain a first grid point set;

[0009] Performing rasterization processing on the point cloud data of the target area to be measured to obtain a second grid point set; wherein the second grid point set corresponds to the grid coordinates of the first grid point set;

[0010] Determining a point cloud covered area and a point cloud uncovered area of ​​the polygonal area according to the first grid point set, the second grid point set, and the grid coordinates;

[0011] determining a point cloud coverage rate based on a comparison of the number of grid point sets in the point cloud coverage area and the number of the first grid point set;

[0012] Determine whether the point cloud coverage is less than a preset threshold; if so, determine a re-collected area based on a grid point set in an area not covered by the point cloud.

[0013] Furthermore, the area of ​​the first grid point set occupied by the second grid points is the point cloud covered area, and the area of ​​the first grid point set not occupied by the second grid points is the point cloud uncovered area.

[0014] Furthermore, determining the point cloud covered area and the point cloud uncovered area includes:

[0015] determining whether each second grid point in the second grid point set belongs to the first grid point set; if so, marking the corresponding first grid point in the first grid point set;

[0016] The first grid point set is traversed, and all marked first grid point sets are the point cloud coverage area, and all unmarked first grid point sets are the point cloud uncovered area.

[0017] Furthermore, the step of performing rasterization processing on the polygonal area corresponding to the target area to be measured to obtain a first grid point set includes:

[0018] Obtain the minimum circumscribed rectangular area surrounding the polygonal target area;

[0019] Performing rasterization processing on the minimum circumscribed rectangular area to obtain a minimum circumscribed rectangular area grid point set;

[0020] The first grid point set is obtained by removing the grid points outside the polygonal target area from the minimum circumscribed rectangular area grid point set.

[0021] Furthermore, determining the re-collected area based on the grid point set of the area not covered by the point cloud includes:

[0022] Performing convex hull processing on the area point set in the uncovered area of ​​the point cloud to obtain a minimum convex hull set;

[0023] The intersection of the minimum convex hull set and the target area is obtained to obtain the re-collected area.

[0024] Furthermore, the re-collected area is collected, and the point cloud coverage is recalculated until the point cloud coverage is not less than the preset threshold.

[0025] In a second aspect, an embodiment of the present disclosure provides a point cloud coverage verification device, comprising:

[0026] A first rasterization module is used to perform rasterization processing on a polygonal area corresponding to the target area to be measured to obtain a first grid point set;

[0027] A second rasterization module is configured to perform rasterization processing on the point cloud data of the target area to be measured to obtain a second grid point set; wherein the second grid point set corresponds to the grid coordinates of the first grid point set;

[0028] a processing module, configured to determine a point cloud covered area and a point cloud uncovered area of ​​the polygonal area based on the first grid point set, the second grid point set, and the grid coordinates;

[0029] a calculation module, determining a point cloud coverage rate based on a comparison between the number of grid point sets in the point cloud coverage area and the number of the first grid point set;

[0030] The judgment module is used to judge whether the coverage rate of the point cloud is less than a preset threshold; if so, determine the area to be re-collected based on the grid point set of the area not covered by the point cloud.

[0031] Furthermore, the device further includes a convex hull processing module for performing convex hull processing on the regional point set of the uncovered area of ​​the point cloud to obtain a minimum convex hull set;

[0032] The processing module is used to obtain the re-collected area according to the intersection of the minimum convex hull set and the polygonal area.

[0033] In a third aspect, an embodiment of the present disclosure provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, can implement any of the aforementioned point cloud coverage verification methods.

[0034] In a fourth aspect, an embodiment of the present disclosure provides an electronic device, comprising: one or more processors; a storage unit for storing one or more programs, which, when executed by the one or more processors, enables the one or more processors to implement the point cloud coverage verification method described in any one of the preceding items.

[0035] The point cloud coverage verification method and device provided by the embodiments of the present disclosure calculate the point cloud coverage rate of the target area to be measured, and determine whether re-collection is required based on the point cloud coverage rate of the target area to be measured. While ensuring the integrity of the collected data, multiple repeated and unnecessary point cloud data collection is avoided, thereby improving collection efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] FIG1 is a schematic diagram of a flow chart of a point cloud coverage verification method according to an embodiment of the present disclosure;

[0037] FIG2 is a schematic diagram of rasterizing polygons corresponding to a target area to be measured according to an embodiment of the present disclosure;

[0038] FIG3 is a schematic diagram of storing laser radar point cloud data according to an embodiment of the present disclosure;

[0039] FIG4 is a schematic diagram of rasterizing point cloud data of a target area to be measured according to an embodiment of the present disclosure;

[0040] FIG5 is a flow chart of a method for calculating a re-acquisition area according to an embodiment of the present disclosure;

[0041] FIG6 is a schematic structural diagram of a point cloud coverage verification device according to an embodiment of the present disclosure; and

[0042] FIG7 is a schematic structural diagram of an electronic device according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0043] Exemplary embodiments will be described in detail herein, with examples illustrated in the accompanying drawings. In the following description, when referring to the drawings, identical numerals in different figures represent identical or similar elements, unless otherwise indicated. The embodiments described in the following exemplary embodiments are not intended to represent all possible embodiments consistent with the present disclosure. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present disclosure, as detailed in the appended claims.

[0044] The terms used in this disclosure are for the purpose of describing specific embodiments only and are not intended to limit the disclosure. As used in this disclosure and the appended claims, the singular forms "a," "an," "the," and "the" are intended to include the plural forms, unless the context clearly indicates otherwise. It should also be understood that the term "and / or" as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items.

[0045] It should be understood that although the terms first, second, third, etc. may be used in this disclosure to describe various information, such information should not be limited to these terms. These terms are merely used to distinguish information of the same type from one another. For example, first information may also be referred to as second information, and similarly, second information may also be referred to as first information, without departing from the scope of this disclosure. Depending on the context, the term "if" as used herein may be interpreted as "when...", "when...", or "in response to determining."

[0046] A point cloud coverage verification method and device according to an embodiment of the present disclosure will be described in detail below with reference to the accompanying drawings.

[0047] FIG1 is a flow chart of a method for verifying point cloud coverage provided by an embodiment of the present disclosure. As shown in FIG1 , the method for verifying point cloud coverage includes the following steps:

[0048] S101, performing rasterization processing on a polygonal area corresponding to the target area to be measured to obtain a first grid point set;

[0049] Typically, the target area to be measured is a polygonal area. Here, the polygon 20 in FIG. 2 is used as an example to illustrate the polygonal area corresponding to the target area to be measured.

[0050] As shown in FIG2 , the steps of rasterizing the polygonal target area 20 in FIG2 are as follows:

[0051] First, the minimum circumscribed rectangular area 21 surrounding the polygonal target area 20 is obtained;

[0052] Then, the minimum circumscribed rectangular area 21 is rasterized using squares of a fixed size to obtain a grid point set of the minimum circumscribed rectangular area 21;

[0053] Then, the grid points outside the polygonal target area 20 in the grid point set of the minimum circumscribed rectangular area 21 are removed to obtain a first grid point set consisting of several grid points 201 inside the polygonal target area 20 to be measured; each grid point 201 has a corresponding horizontal coordinate and vertical coordinate on the map.

[0054] S102, performing rasterization processing on the point cloud data of the target area to be measured to obtain a second grid point set;

[0055] Here, point cloud data refers to a collection of three-dimensional point data on the surface of a target, obtained using a measuring instrument. Measuring instruments may include, but are not limited to, laser radar, millimeter-wave radar, ultrasonic radar, and cameras. In the disclosed embodiments, using laser radar as an example, point cloud data of the environment is collected to generate a laser point cloud map. This laser point cloud map stores the raw point cloud scanned by the sensor.

[0056] As shown in Figure 3, the point cloud data acquired by the LiDAR is stored in a storage medium, such as an image, based on its geographic coordinates. For example, for any 3D point rp in space, the point cloud data acquired by the LiDAR includes its Cartesian coordinates (xp, yp, zp).

[0057] Laser point cloud rasterization is to process the area scanned by the laser radar using a grid. Each grid point cloud represents a small area in space and contains a part of the point cloud. Point cloud rasterization processing is divided into two-dimensional rasterization and three-dimensional rasterization. In the embodiment of the present disclosure, two-dimensional rasterization is used for explanation.

[0058] In the disclosed embodiment, assuming the ground is relatively flat, that is, the z-axis fluctuations of the ground scanning points are small, the laser radar scanning area is divided into a grid, and the scanned point cloud data is projected onto a grid plane 30 as shown in Figure 4 . Grid plane 30 includes a second grid point set consisting of a number of grid points 301 ; each grid point 301 has a corresponding horizontal and vertical coordinate on the map, and the coordinates of the second grid point set in Figure 4 correspond to the coordinates of the first grid point set in Figure 2 .

[0059] S103: Determine a point cloud covered area and a point cloud uncovered area of ​​the polygonal area based on the first grid point set, the second grid point set, and the grid coordinates;

[0060] When collecting high-precision map data, due to some reasons, there may be missing point cloud data, so each grid point 301 in the grid plane 30 in Figure 4 is not completely covered by point cloud data.

[0061] In the embodiment of the present disclosure, first, the grid points 301 in the grid plane 30 that are covered by the point cloud data are marked, while the grid points 301 in the grid plane 30 that are not covered by the point cloud data are not marked;

[0062] Then, based on the relationship between the coordinates of the first grid point set and the second grid point set, according to the mark of the grid point 301 in the grid plane 30, all the grid points inside the first grid point set are traversed to obtain the grid point set covered by the point cloud inside the first grid point set and the grid point set not covered by the point cloud inside the first grid point set, thereby obtaining the point cloud covered area and the point cloud uncovered area of ​​the polygonal area 20.

[0063] S104, determining a point cloud coverage rate based on a comparison of the number of grid point sets in the point cloud coverage area and the number of the first grid point sets;

[0064] In step S103, the first grid point set contained in the polygonal target area to be measured is traversed, the number of grids covered by the point cloud and the number of grids not covered by the point cloud in the first grid point set are calculated, and the point cloud coverage rate of the polygonal target area to be measured 20 is calculated using the following formula (1):

[0065] Point cloud coverage = number of covered grid points / number of all grid points inside the polygon (1)

[0066] S105 , determining whether the point cloud coverage of the polygonal target area to be measured is less than a preset threshold; if so, determining a re-collected area based on the grid point set of the area not covered by the point cloud.

[0067] After calculating the point cloud coverage in step S104, a determination is made as to whether the point cloud coverage is less than a preset threshold, such as 80%-90%. If the point cloud coverage is less than the threshold, a re-collected area is determined based on the grid point sets within the first grid point set that are not covered by the point cloud. It should be noted that this threshold is set by those skilled in the art based on the requirements of high-precision maps for autonomous driving, and may be adjusted as needed by those skilled in the art.

[0068] The point cloud coverage verification method provided by the embodiment of the present disclosure first determines the point cloud coverage of the target area to be measured when the point cloud data of the target area to be measured is missing. When the point cloud coverage is less than a preset threshold, the uncovered area of ​​the point cloud is re-determined, thereby avoiding unnecessary repeated collection and improving the collection efficiency.

[0069] Further, referring to FIG5 , which is a flowchart of a method for calculating a re-collection area according to an embodiment of the present disclosure, the method for calculating a re-collection area includes:

[0070] S501, performing convex hull processing on the uncovered grid point set in the polygon of the target area to be measured, and solving the minimum convex hull set;

[0071] S502: traverse the minimum convex hull set solved in step S501, and calculate the intersection with the polygonal area 20 to be measured, to obtain the area that needs to be re-collected in the polygonal area 20 to be measured;

[0072] S503, sending the information of the area requiring re-collection obtained in step S502 to the collection terminal;

[0073] S504: Merge the point cloud data re-collected by the acquisition terminal with the point cloud data in step S102 and rasterize them, then continue to execute steps S103 and S104 to determine the point cloud coverage rate; if the point cloud coverage rate is still less than the predetermined threshold, continue to execute steps S501, S502, S503, S504, S102, S103, S104, and S105 until the point cloud coverage rate reaches the preset threshold. In this case, there is no need to calculate the re-collected area, and the acquisition is completed.

[0074] The method for recalculating the acquisition area provided by the disclosed embodiments involves acquiring uncovered areas of the point cloud, calculating the updated point cloud coverage, determining whether the updated point cloud coverage meets a preset threshold, and repeating these steps until acquisition ends when the point cloud coverage reaches the preset threshold. This method only acquires uncovered areas of the point cloud where the point cloud coverage does not meet the preset threshold, avoiding multiple unnecessary acquisitions and improving acquisition efficiency.

[0075] FIG6 is a schematic diagram of a point cloud coverage verification device according to an embodiment of the disclosure. The point cloud coverage verification device 600 includes:

[0076] A first rasterization module 601 is configured to perform rasterization processing on a polygonal area corresponding to the target area to be measured, to obtain a first grid point set;

[0077] A second rasterization module 602 is configured to perform rasterization processing on the point cloud data of the target area to be measured to obtain a second grid point set; wherein the second grid point set corresponds to the grid coordinates of the first grid point set;

[0078] A processing module 603 is configured to determine a point cloud covered area and a point cloud uncovered area of ​​the polygonal area based on the first grid point set, the second grid point set, and the grid coordinates;

[0079] The calculation module 604 determines the point cloud coverage rate based on a comparison between the number of grid point sets in the point cloud coverage area and the number of the first grid point set;

[0080] The judgment module 605 is used to judge whether the coverage of the point cloud is less than a preset threshold; if so, determine the area to be re-collected based on the grid point set of the area not covered by the point cloud.

[0081] Furthermore, the device 600 also includes a communication module 606. When the judgment module 605 determines whether the point cloud coverage is less than a preset threshold and needs to be re-collected, the communication module 606 sends information about the area that needs to be re-collected to a collection terminal (not shown in the figure), such as a collection vehicle or an unmanned mining vehicle; and after the re-collection at the collection terminal is completed, the communication module 606 receives the re-collected point cloud data sent by the collection terminal.

[0082] Furthermore, the apparatus 600 further includes a convex hull processing module (not shown in the figure) for performing convex hull processing on the regional point set in the uncovered area of ​​the point cloud to obtain a minimum convex hull set;

[0083] The processing module 603 is configured to obtain the re-collected area according to the intersection of the minimum convex hull set and the polygonal area.

[0084] The point cloud coverage verification device provided by the embodiment of the present disclosure first determines the point cloud coverage of the target area to be measured when the point cloud data of the target area to be measured is missing. When the point cloud coverage is less than a preset threshold, the point cloud uncovered area is re-determined and the point cloud uncovered area is re-collected, thereby avoiding unnecessary repeated collection and improving the collection efficiency.

[0085] Figure 7 is a schematic diagram of the structure of an electronic device according to an embodiment of the present disclosure. As shown in Figure 7, electronic device 700 according to this embodiment includes: a processor 701, a memory 702, and a computer program 703 stored in the memory 702 and executable on the processor 701. When the processor 701 executes the computer program 703, the steps of the aforementioned method embodiments are implemented. Alternatively, when the processor 701 executes the computer program 703, the functions of the modules / units in the aforementioned device embodiments are implemented.

[0086] For example, the computer program 703 may be divided into one or more modules / units, which are stored in the memory 702 and executed by the processor 701 to implement the present disclosure. The one or more modules / units may be a series of computer program instruction segments capable of implementing specific functions, and the instruction segments are used to describe the execution process of the computer program 703 in the electronic device 700.

[0087] Electronic device 700 can be a desktop computer, laptop, PDA, cloud server, or other electronic device. Electronic device 700 may include, but is not limited to, a processor 701 and a memory 702. Those skilled in the art will appreciate that FIG7 is merely an example of electronic device 700 and does not limit electronic device 700. It may include more or fewer components than shown, or a combination of certain components, or different components. For example, the electronic device may also include input / output devices, network access devices, buses, and the like.

[0088] The processor 701 may be a central processing unit (CPU), or 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.

[0089] Memory 702 can be an internal storage unit of electronic device 700, such as a hard drive or memory of electronic device 700. Memory 702 can also be an external storage device of electronic device 700, such as a plug-in hard drive, a Smart Media Card (SMC), a Secure Digital (SD) card, a flash memory card, etc. equipped with electronic device 700. Furthermore, memory 702 can include both an internal storage unit of electronic device 700 and an external storage device. Memory 702 is used to store computer programs and other programs and data required by the electronic device. Memory 702 can also be used to temporarily store data that has been output or is about to be output.

[0090] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this disclosure. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0091] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant description of other embodiments.

[0092] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this disclosure.

[0093] In the embodiments provided in the present disclosure, it should be understood that the disclosed devices / electronic devices and methods can be implemented in other ways. For example, the device / electronic device embodiments described above are merely schematic. For example, the division of modules or units is merely a logical function division. In actual implementation, there may be other division methods. Multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed may be through some interfaces, indirect coupling or communication connection of devices or units, which may be electrical, mechanical or other forms.

[0094] Units described as separate components may or may not be physically separate, and 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.

[0095] In addition, the functional units in the various embodiments of the present disclosure may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0096] If the integrated module / unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present disclosure can implement all or part of the processes in the above-mentioned method embodiments by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, the computer program can implement the steps of each of the above-mentioned method embodiments. The computer program can include computer program code, which can be in source code form, object code form, executable file, or some intermediate form. Computer-readable media can include: any entity or device capable of carrying computer program code, recording media, USB flash drives, mobile hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signals, telecommunications signals, and software distribution media. It should be noted that the content included in the computer-readable medium can be appropriately increased or decreased based on the requirements of legislation and patent practice in a jurisdiction. For example, in some jurisdictions, based on legislation and patent practice, computer-readable media does not include electric carrier signals and telecommunications signals.

[0097] The above embodiments are only used to illustrate the technical solutions of the present disclosure, rather than to limit them. Although the present disclosure has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present disclosure, and should all be included in the scope of protection of the present disclosure.

Claims

1. A point cloud coverage verification method, characterized in that: The method comprises: Performing rasterization processing on the polygonal area corresponding to the target area to be measured to obtain a first grid point set; Performing rasterization processing on the point cloud data of the target area to be measured to obtain a second grid point set; wherein the second grid point set corresponds to the grid coordinates of the first grid point set; Determine a point cloud covered area and a point cloud uncovered area of ​​the polygonal area according to the first grid point set, the second grid point set and the grid coordinates; Determining the point cloud coverage rate according to a comparison between the number of grid point sets in the point cloud coverage area and the number of the first grid point sets; Determine whether the point cloud coverage is less than a preset threshold; if so, determine the area to be re-collected based on the grid point set of the area not covered by the point cloud.

2. The method according to claim 1, characterized in that The area in the first grid point set occupied by the second grid points is the point cloud covered area, and the area in the first grid point set not occupied by the second grid points is the point cloud uncovered area.

3. The method according to claim 2, characterized in that The determining the point cloud covered area and the point cloud uncovered area comprises: Determine whether each second grid point in the second grid point set belongs to the first grid point set; if so, mark the corresponding first grid point in the first grid point set; The first grid point set is traversed, all marked first grid point sets are the area covered by the point cloud, and all unmarked first grid point sets are the area not covered by the point cloud.

4. The method according to claim 1, characterized in that: The rasterizing of the polygonal area corresponding to the target area to be measured to obtain a first grid point set includes: Obtain the minimum circumscribed rectangular area surrounding the polygonal target area; Performing rasterization processing on the minimum circumscribed rectangular area to obtain a set of grid points of the minimum circumscribed rectangular area; The first grid point set is obtained by removing the grid points outside the polygonal target area from the minimum circumscribed rectangular area grid point set.

5. The method according to claim 1, characterized in that The step of determining the re-collected area according to the grid point set of the area not covered by the point cloud comprises: Performing convex hull processing on the area point set of the area not covered by the point cloud to obtain a minimum convex hull set; The intersection of the minimum convex hull set and the target area is obtained to obtain the re-collected area.

6. The method according to claim 1, characterized in that The re-collected area is collected, and the point cloud coverage is recalculated until the point cloud coverage is not less than the preset threshold.

7. A point cloud coverage verification device, characterized in that: The device comprises: A first rasterization module is used to perform rasterization processing on a polygonal area corresponding to the target area to be measured to obtain a first grid point set; A second rasterization module, configured to perform rasterization processing on the point cloud data of the target area to be measured to obtain a second grid point set; wherein the second grid point set corresponds to the grid coordinates of the first grid point set; A processing module, configured to determine a point cloud covered area and a point cloud uncovered area of ​​the polygonal area according to the first grid point set, the second grid point set and the grid coordinates; A calculation module, which determines the point cloud coverage rate according to a comparison between the number of grid point sets in the point cloud coverage area and the number of the first grid point sets; The judgment module is used to judge whether the coverage rate of the point cloud is less than a preset threshold; if so, determine the area to be re-collected according to the grid point set of the area not covered by the point cloud.

8. The device according to claim 7, characterized in that The device also includes a convex hull processing module, which is used to perform convex hull processing on the regional point set of the area not covered by the point cloud to obtain a minimum convex hull set; The processing module is used to obtain the re-collected area according to the intersection of the minimum convex hull set and the polygonal area.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, it can implement the point cloud coverage verification method according to any one of claims 1 to 6.

10. An electronic device, characterized in that: include: one or more processors; A storage unit, used to store one or more programs, which, when executed by the one or more processors, enable the one or more processors to implement the point cloud coverage verification method according to any one of claims 1 to 6.

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