Method and apparatus for checking degree of coverage of point cloud boundary line, electronic device and storage medium

Abstract

Provided in the embodiments of the present disclosure are a method and apparatus for checking the degree of coverage of a point cloud boundary line, an electronic device and a storage medium. The method comprises: extracting a line to be checked of a boundary line; acquiring point cloud data of the line to be checked, performing a neighborhood search on the point cloud data of each point on the line to be checked, and determining whether the number of point clouds in a neighborhood is greater than a preset threshold value so as to determine the validity of each point on the line to be checked; on the basis of a comparison between the number of points covered by the point clouds of the line to be checked and the number of data points of the line to be checked, determining the point cloud coverage rate of the line to be checked; determining whether the point cloud coverage rate is less than a preset threshold value; and, if so, on the basis of points not covered by the point clouds, determining an area to be reacquired. While ensuring complete acquired data, the method for checking the degree of coverage of a point cloud boundary line provided in the embodiments of the present disclosure avoids repeatedly and unnecessarily acquiring point cloud data multiple times and improves acquisition efficiency.

Description

Point cloud boundary line 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 method, device, electronic device, and storage medium for verifying point cloud boundary line coverage. 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] At the same time, in special autonomous driving environments, such as mining areas, the terrain of the mining work area will constantly change, especially the changes in the boundary lines of the mining work area, such as: the boundary lines of the road, the boundary lines of the spoil dump, the spoil line, the boundary lines of the loading area, etc. The changes in the above boundary lines need to be updated in a timely manner on the high-precision map to ensure the reliability of the high-precision map of the mining area.

[0005] In existing technologies, when HD maps have missing data or need to update boundary lines, boundary lines are usually collected repeatedly. However, such repeated collection is inefficient and involves unnecessary repeated collection, resulting in a waste of resources. Summary of the Invention

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

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

[0008] Extracting a line to be detected from a boundary line, wherein the line to be detected includes a plurality of data points;

[0009] Obtaining point cloud data of the line to be detected, performing a neighborhood search on the point cloud data of each point on the line to be detected, and determining the validity of each point on the line to be detected by judging whether the number of point clouds in the neighborhood is greater than a preset number; wherein the valid points on the line to be detected are point cloud covered points, and the invalid points on the line to be detected are point cloud uncovered points;

[0010] Determining the point cloud coverage rate of the line to be detected based on a comparison between the number of point cloud coverage points of the line to be detected and the number of data points of the line to be detected;

[0011] Determine whether the point cloud coverage is less than a preset threshold; if so, determine a re-collected area based on the uncovered points in the point cloud.

[0012] Optionally, extracting the line to be detected of the boundary line includes: acquiring a historical vector line of the boundary line, and performing densification processing on the historical vector line to obtain the line to be detected.

[0013] Optionally, obtaining the line to be detected of the boundary line includes: obtaining an acquisition trajectory line of the boundary line, and performing interpolation processing on the acquisition trajectory line to obtain the line to be detected.

[0014] Optionally, the neighborhood search is a radius search.

[0015] Furthermore, determining the re-collected area based on the uncovered points in the point cloud includes clustering the uncovered points in the point cloud to generate a cluster point set.

[0016] Optionally, the clustering is Euclidean clustering.

[0017] Furthermore, the method further includes: collecting the re-collected area and recalculating the point cloud coverage until the point cloud coverage is not less than the preset threshold.

[0018] In a second aspect, an embodiment of the present disclosure provides a point cloud boundary line coverage verification device, comprising:

[0019] An extraction module, configured to extract a line to be detected from a boundary line, wherein the line to be detected includes a plurality of data points;

[0020] a processing module, configured to obtain point cloud data of the line to be detected, perform a neighborhood search on the point cloud data of each point on the line to be detected, and determine the validity of each point on the line to be detected by judging whether the number of point clouds in the neighborhood is greater than a preset number; wherein valid points on the line to be detected are point cloud covered points, and invalid points on the line to be detected are point cloud uncovered points;

[0021] A calculation module, configured to determine the point cloud coverage of the line to be detected based on a comparison between the number of point cloud coverage points of the line to be detected and the number of data points of the line to be detected;

[0022] 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 uncovered points of the point cloud.

[0023] In a third aspect, an embodiment of the present disclosure provides 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 any of the aforementioned point cloud boundary line coverage verification methods.

[0024] 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 boundary line coverage verification method described in any one of the preceding items.

[0025] The point cloud boundary line coverage verification method and device provided by the embodiments of the present disclosure calculate the point cloud coverage rate of the boundary line to be measured, and determine whether re-collection is required based on the point cloud coverage rate of the boundary line 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

[0026] FIG1 is a schematic diagram of a flow chart of a method for verifying the coverage of a point cloud boundary line according to an embodiment of the present disclosure;

[0027] FIG2 is a schematic diagram of a process for calculating a line to be detected having a historical vector line according to an embodiment of the present disclosure;

[0028] FIG3 is a schematic diagram of a process for calculating a newly generated boundary line to be detected according to an embodiment of the present disclosure;

[0029] FIG4 is a schematic diagram of a process for determining whether a point on a line to be detected is valid according to an embodiment of the present disclosure;

[0030] FIG5 is a schematic diagram of performing a point cloud neighborhood search on a point on a line to be detected according to an embodiment of the present disclosure;

[0031] FIG6 is a flow chart of a method for calculating a re-collection area according to an embodiment of the present disclosure;

[0032] Figure 7 is a schematic diagram of the point cloud coverage of the spoil dump retaining wall line in a mine working area;

[0033] FIG8 is a schematic structural diagram of a point cloud boundary line coverage verification device according to an embodiment of the present disclosure; and

[0034] FIG9 is a schematic structural diagram of an electronic device according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0035] 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.

[0036] 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.

[0037] 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."

[0038] A method and apparatus for verifying the coverage of a point cloud boundary line according to an embodiment of the present disclosure will be described in detail below with reference to the accompanying drawings.

[0039] FIG1 is a flow chart of a method for verifying the coverage of a point cloud boundary line provided by an embodiment of the present disclosure. As shown in FIG1 , the method 100 for verifying the coverage of a point cloud boundary line includes the following steps:

[0040] S101, extracting a boundary line to be detected and obtaining a set of data points of the boundary line to be detected;

[0041] In the embodiments of this disclosure, taking a mining area as an example, the following boundary lines may exist within the mining area, such as road boundaries, spoil dump boundaries, spoil lines, and loading area boundaries. In actual applications, these boundary lines may be historical vector lines stored in the HD map, or they may be newly generated boundary lines that need to be re-collected and stored in the HD map. The following describes how to extract the boundary lines to be detected based on these two scenarios.

[0042] Refer to Figure 2, which is a schematic diagram of the process of extracting the lines to be detected from the historical vector lines of the boundary lines stored in the high-precision map.

[0043] A vector line is a data type used in geographic information systems (GIS) to represent the geometry and location of linear features. It consists of a series of continuous coordinate points and can be used to represent various geographic features, such as rivers, roads, and boundaries. A vector line has directionality and length, and its shape and topological relationships can be described by connecting coordinate points.

[0044] In the case where a historical vector line of a boundary line is already stored in the high-precision map, the historical vector line can be directly obtained from the high-precision map, and the line to be detected of the boundary line can be obtained based on the historical vector line.

[0045] As shown in FIG2 , it is a flow chart of a method 200 for calculating a line to be detected having a historical vector line according to an embodiment of the present disclosure. The method 200 comprises the following steps:

[0046] S201. Download the historical vector line of the boundary line from the historical data of the high-precision map;

[0047] S202: Obtain the corresponding starting point and end point on the historical vector line according to the position coordinates of the starting point and end point of the boundary line;

[0048] S203. Clip the points other than the starting point and the end point of the historical vector line to obtain a set of points between the starting point and the end point on the historical vector line, which is referred to herein as the first data point set; the line formed by the first data point set is the line to be detected of the boundary line, wherein each point in the first data point set contains its corresponding coordinates.

[0049] Furthermore, in practical applications, the points on the historical vector line corresponding to the boundary line to be detected may have sparse density and fail to meet the accuracy requirements of data processing. In this case, the method 200 further includes the steps of:

[0050] S204: After performing interpolation processing on the historical vector line, the boundary line to be detected is obtained. The specific interpolation processing method can adopt an interpolation processing algorithm commonly used in the art, such as equal-interval interpolation processing, etc., which is not specifically limited in the embodiment of the present disclosure.

[0051] 3 , which is a schematic diagram of a process for extracting lines to be detected from newly generated boundary lines.

[0052] For newly generated boundary lines that need to be recollected and stored in the HD map, a collection vehicle or autonomous mining vehicle is typically required to collect the boundary line information. During the collection process, the driver or autonomous driving system will pre-plan a collection trajectory, and the collection vehicle or autonomous mining vehicle will then collect data along this trajectory. Typically, this trajectory is the path the collection vehicle or autonomous mining vehicle follows along the newly generated boundary line.

[0053] FIG3 is a flow chart of a method 300 for calculating a newly generated boundary line to be detected according to an embodiment of the present disclosure. The method 300 includes the following steps:

[0054] S301, extracting the collection trajectory of the collection vehicle or the mining vehicle driven by the drone;

[0055] S302, obtaining the starting point and end point of the collection trajectory line;

[0056] S303. Clip the points other than the starting point and the end point of the acquisition trajectory line to obtain a set of vector line points corresponding to the acquisition trajectory line between the starting point and the end point, referred to herein as the second data point set; the line formed by the second data point set is the newly generated boundary line to be detected, wherein each point in the second data point set includes its corresponding coordinates.

[0057] Furthermore, in practical applications, the points on the acquisition trajectory corresponding to the newly generated boundary line to be detected may have a sparse density and fail to meet the accuracy requirements of data processing. Therefore, the method 300 further includes the steps of:

[0058] S304: After performing interpolation processing on the collected trajectory line, the newly generated boundary line to be detected is obtained. The specific interpolation processing method can adopt an interpolation processing algorithm commonly used in the art, such as equal-interval interpolation processing, etc., which is not specifically limited in the embodiment of the present disclosure.

[0059] Continuing with FIG1 , after obtaining the boundary line to be detected, the point cloud boundary line coverage verification method 100 of the embodiment of the present disclosure continues to perform the following steps:

[0060] S102, obtaining point cloud data of the line to be detected, performing a neighborhood search on the point cloud data of each point of the line to be detected, and determining the validity of each point by determining whether the number of point clouds in the neighborhood is greater than a preset number;

[0061] In the field of autonomous driving, such as in mining areas, mining vehicles or autonomous mining vehicles carry sensors for real-time environmental awareness. These sensors are used to obtain a collection of three-dimensional point data, or point cloud data, representing the surface of the target being measured. Measuring instruments include, but are not limited to, lidar, millimeter-wave radar, ultrasonic radar, and cameras.

[0062] Whether it's a boundary line with existing historical vector lines in the HD map or a newly generated boundary line, the point cloud map corresponding to the HD map will store the point cloud data for the boundary line. The specific point cloud collection and storage methods are well known in the art and will not be detailed here.

[0063] After obtaining the point cloud data corresponding to the line to be detected in step S102, a point cloud data neighborhood search is performed on each point on the line to be detected, so as to determine the validity of the point cloud coverage of each point on the line to be detected.

[0064] 4, which is a flow chart of determining whether a point on a line to be detected is valid according to an embodiment of the present disclosure. The method 400 includes the following steps:

[0065] S401, performing a neighborhood search on the point cloud data of each point of the line to be detected;

[0066] S402: Determine whether the number of point clouds in the neighborhood of each point is greater than a preset number to determine the validity of the point cloud coverage of each point; wherein valid points on the detection line are point cloud covered points, and invalid points on the line to be detected are point cloud uncovered points;

[0067] S403: Traverse each point on the line to be detected and determine the validity of the point cloud coverage of each point until the traversal is completed.

[0068] In the embodiment of the present disclosure, FIG. 5 is taken as an example to illustrate the neighborhood search of the point cloud of the line to be detected.

[0069] As shown in Figure 5, assume that points A, B, and C are points on the line to be inspected, with the gray dots representing point cloud data. Using length d as the radius, a neighborhood search is performed for each of points A, B, and C, counting the number of point clouds within radius d of each of them. As shown in Figure 5, there is no point cloud data within radius d of point A, four point cloud data within radius d of point B, and one point cloud data within radius d of point C. If the validity is determined by the number of three or more point clouds, then point B on the line to be inspected is a valid point cloud covered point, while points A and C are invalid uncovered points.

[0070] It should be noted that, in practical applications, the length of the specific neighborhood search radius and the number of point clouds used to determine the validity of the point cloud can be selected as needed by those skilled in the art, and are not specifically limited in the embodiments of the present disclosure. Furthermore, those skilled in the art can select other neighborhood search methods, which are also not limited in the embodiments of the present disclosure.

[0071] Step S103: determining the point cloud coverage of the line to be detected based on a comparison between the number of coverage points of the point cloud and the number of data points of the line to be detected;

[0072] After determining in step S102 whether the points on the line to be tested are covered points or uncovered points, in step S103, the point cloud coverage of the line to be tested is determined based on the comparison between the number of covered points of the line to be tested and the number of data points of the line to be tested. The point cloud coverage of the line to be tested is calculated using the following formula (1):

[0073] Point cloud coverage = number of points covered by the point cloud / number of data points of the line to be detected (1)

[0074] It should be noted that, in step S101, if the line to be detected is formed after interpolation processing, the number of data points of the line to be detected in the above formula (1) includes the original data points of the line to be detected and the interpolated data points.

[0075] Step S104: determine whether the point cloud coverage is less than a preset threshold; if so, determine the area to be re-collected based on the uncovered points in the point cloud.

[0076] After calculating the point cloud coverage of the line to be inspected in step S103, a determination is made as to whether the point cloud coverage of the line to be inspected is less than a preset threshold, for example, 80%-90%. If the point cloud coverage is less than the threshold, a re-collection area is determined based on the uncovered point set. It should be noted that the preset threshold for point cloud coverage is set by those skilled in the art based on the requirements of high-precision maps for autonomous driving and can be adjusted as needed by those skilled in the art.

[0077] The point cloud boundary line coverage verification method provided by the embodiment of the present disclosure first determines the point cloud coverage of the boundary line to be measured when the point cloud data of the boundary line to be measured is missing. When the point cloud coverage of the boundary line to be measured is less than a preset threshold, the uncovered area of ​​the point cloud is re-determined, thereby avoiding unnecessary repeated collection and improving the efficiency of collection.

[0078] Further, referring to FIG6 , which is a flow chart 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 the following steps:

[0079] S601, clustering the uncovered point set in the boundary line to be measured, such as Euclidean clustering, to obtain a cluster point set;

[0080] S602: Send the cluster point set to the collection terminal;

[0081] S603: Merge the point cloud data re-collected by the acquisition terminal with the point cloud data in step S102;

[0082] S604. Continue to execute steps S103, S104, and S105 to determine the point cloud coverage of the line to be detected; if the point cloud coverage is still less than the predetermined threshold, continue to execute steps S601, S602, S603, S604, S102, S103, S104, and S105 until the point cloud coverage of the line to be detected reaches the preset threshold. In this case, there is no need to calculate the re-collected area, and the collection is completed.

[0083] 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.

[0084] Refer to Figure 7, which shows a schematic diagram of the point cloud coverage of a spoil dump retaining wall in a mine working area. The black points represent the vector line information of the previous spoil dump retaining wall, and the white points within the rectangular box represent the area requiring acquisition, recalculated according to the point cloud boundary line coverage verification method of the disclosed embodiment. After reacquisition, the vector lines of the spoil dump retaining wall were interpolated with a 0.1m spacing, achieving 100% point cloud coverage.

[0085] FIG8 is a schematic diagram of a point cloud boundary line coverage verification device according to a disclosed embodiment. The point cloud boundary line coverage verification device 800 includes:

[0086] An extraction module 801 is used to extract a line to be detected from a boundary line, where the line to be detected includes a number of data points;

[0087] Processing module 802 is configured to obtain point cloud data of the line to be inspected, perform a neighborhood search on the point cloud data of each point on the line to be inspected, and determine the validity of each point on the line to be inspected by determining whether the number of point clouds in the neighborhood is greater than a preset number; wherein valid points on the line to be inspected are point cloud covered points, and invalid points on the line to be inspected are point cloud uncovered points;

[0088] A calculation module 803 is configured to determine the point cloud coverage of the line to be detected based on a comparison between the number of point cloud coverage points of the line to be detected and the number of data points of the line to be detected;

[0089] The judgment module 804 is used to judge whether the point cloud coverage is less than a preset threshold; if so, determine the area to be re-collected based on the uncovered points of the point cloud.

[0090] Furthermore, the device 800 also includes a communication module 805. When the judgment module 804 determines whether the point cloud coverage is less than a preset threshold and needs to be re-collected, the communication module 805 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 805 receives the re-collected point cloud data sent by the collection terminal.

[0091] Furthermore, the apparatus 800 further includes a clustering module (not shown in the figure) for clustering the uncovered point set of the point cloud, such as Euclidean clustering, to obtain a cluster point set;

[0092] The processing module 802 is configured to obtain points on the re-collected boundary line according to the cluster point set.

[0093] The point cloud boundary line coverage verification device provided by the embodiment of the present disclosure first determines the point cloud coverage of the boundary line to be measured when the point cloud data of the boundary line 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.

[0094] Figure 9 is a schematic diagram of the structure of an electronic device according to an embodiment of the present disclosure. As shown in Figure 9, electronic device 900 according to this embodiment includes: a processor 901, a memory 902, and a computer program 903 stored in the memory 902 and executable on the processor 901. When the processor 901 executes the computer program 903, the steps of the aforementioned method embodiments are implemented. Alternatively, when the processor 901 executes the computer program 903, the functions of the modules / units in the aforementioned device embodiments are implemented.

[0095] For example, the computer program 903 may be divided into one or more modules / units, which are stored in the memory 902 and executed by the processor 901 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 903 in the electronic device 900.

[0096] Electronic device 900 may be a desktop computer, laptop, PDA, cloud server, or other electronic device. Electronic device 900 may include, but is not limited to, a processor 901 and a memory 902. Those skilled in the art will appreciate that FIG9 is merely an example of electronic device 900 and does not limit the scope of electronic device 900. The electronic device 900 may include more or fewer components than shown, or may combine certain components or have different components. For example, the electronic device may also include input / output devices, network access devices, buses, and the like.

[0097] The processor 901 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.

[0098] Memory 902 can be an internal storage unit of electronic device 900, such as a hard drive or memory of electronic device 900. Memory 902 can also be an external storage device of electronic device 900, 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 900. Furthermore, memory 902 can include both an internal storage unit of electronic device 900 and an external storage device. Memory 902 is used to store computer programs and other programs and data required by the electronic device. Memory 902 can also be used to temporarily store data that has been output or is about to be output.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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 boundary line coverage verification method, characterized in that: The method comprises: Extracting a line to be detected of a boundary line, wherein the line to be detected includes a plurality of data points; Obtaining point cloud data of the line to be detected, performing a neighborhood search on the point cloud data of each point of the line to be detected, and determining the validity of each point on the line to be detected by judging whether the number of point clouds in the neighborhood is greater than a preset number; wherein the valid points on the line to be detected are point cloud covered points, and the invalid points on the line to be detected are point cloud uncovered points; Determining the point cloud coverage rate of the line to be detected based on a comparison between the number of the point cloud coverage points of the line to be detected and the number of data points of the line to be detected; Determine whether the point cloud coverage is less than a preset threshold; if so, determine the area to be re-collected based on the uncovered points of the point cloud.

2. The method according to claim 1, characterized in that The extracting of the to-be-detected line of the boundary line includes: acquiring the historical vector line of the boundary line, and performing densification processing on the historical vector line to obtain the to-be-detected line.

3. The method according to claim 1, characterized in that The method of obtaining the line to be detected of the boundary line includes: obtaining a collection trajectory line of the boundary line, and performing densification processing on the collection trajectory line to obtain the line to be detected.

4. The method according to any one of claims 1 to 3, characterized in that The neighborhood search is a radius search.

5. The method according to any one of claims 1 to 3, characterized in that The determining of the re-collected area according to the uncovered points of the point cloud includes clustering the uncovered points of the point cloud to generate a cluster point set.

6. The method according to claim 5, characterized in that The clustering is Euclidean clustering.

7. The method according to claim 1, characterized in that The method further includes: collecting the re-collected area and recalculating the point cloud coverage until the point cloud coverage is not less than the preset threshold.

8. A point cloud boundary line coverage verification device, characterized in that: The method comprises: An extraction module, used for extracting a line to be detected of a boundary line, wherein the line to be detected includes a plurality of data points; A processing module, used to obtain the point cloud data of the line to be detected, and perform a neighborhood search on the point cloud data of each point of the line to be detected, and determine the validity of each point on the line to be detected by judging whether the number of point clouds in the neighborhood is greater than a preset number; wherein the valid points on the line to be detected are point cloud covered points, and the invalid points on the line to be detected are point cloud uncovered points; A calculation module, used for determining the point cloud coverage rate of the line to be detected based on a comparison between the number of the point cloud coverage points of the line to be detected and the number of data points of the line to be detected; 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 uncovered points of the point cloud.

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 boundary line coverage verification method according to any one of claims 1 to 7.

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 boundary line coverage verification method according to any one of claims 1 to 7.

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