WO2025091537A1 - Method and apparatus for checking degree of coverage of point cloud boundary line, electronic device and storage medium - Google Patents

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

The embodiments of the present disclosure relate to the field of autonomous driving technology, and in particular to a point cloud boundary line coverage verification method, device, electronic device, and storage medium.

Background Art

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

However, when collecting high-precision map data, there may be a problem of missing point cloud data in the collection area due to some reasons. For example, detailed route planning is not carried out for the collection area 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 point cloud data.

At the same time, in special autonomous driving environments, such as mining areas, the terrain of the mining work area will continue to change, especially the changes in the boundary lines of the mining work area, such as: the boundary line of the road, the boundary line of the spoil dump, the spoil line, the boundary line of the loading area, etc. The changes in the above boundary lines need to be updated in time on the high-precision map to ensure the reliability of the high-precision map of the mining area.

In the prior art, when there is data missing in the high-precision map or the boundary line needs to be updated, the boundary line is usually collected repeatedly. However, such repeated collection has low collection efficiency and there are problems of multiple unnecessary repeated collections, resulting in a waste of resources.

Summary of the invention

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

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

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.

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

Optionally, the acquiring the line to be detected of the boundary line includes: acquiring a collection trajectory line of the boundary line, and performing densification processing on the collection trajectory line to obtain the line to be detected.

Optionally, the neighborhood search is a radius search.

Furthermore, determining 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.

Optionally, the clustering is Euclidean clustering.

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.

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

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.

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.

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.

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 decide whether re-collection is needed according to the point cloud coverage rate of the boundary line to be measured. Under the premise of ensuring the integrity of the collected data, it avoids multiple repeated and unnecessary point cloud data collection and improves the collection efficiency.

BRIEF DESCRIPTION OF THE DRAWINGS

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

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

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

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

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;

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

FIG7 is a schematic diagram of the point cloud coverage of the spoil dump retaining wall line in a mine working area;

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

FIG. 9 is a schematic diagram of the structure of an electronic device according to an embodiment of the present disclosure.

DETAILED DESCRIPTION

Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present disclosure. Instead, they are merely examples of devices and methods consistent with some aspects of the present disclosure as detailed in the appended claims.

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

It should be understood that although the terms first, second, third, etc. may be used in the present disclosure to describe various information, such information should not be limited to these terms. These terms are only used to distinguish the same type of information from each other. For example, without departing from the scope of the present disclosure, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the word "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining".

A method and device 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.

FIG1 is a schematic diagram of a method for checking the coverage of a point cloud boundary line provided by an embodiment of the present disclosure. As shown in FIG1 , the method 100 for checking the coverage of a point cloud boundary line comprises the following steps:

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

In the embodiment of the present disclosure, taking a mining area as an example, there may be the following boundary lines in the mining area, such as the boundary line of the road, the boundary line of the spoil dump, the spoil line, the boundary line of the loading area, etc. In practical applications, the above boundary lines may be historical vector lines stored in the high-precision map, or they may be newly generated boundary lines that need to be re-collected and stored in the high-precision map. The following will explain how to extract the boundary lines to be detected based on these two situations.

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

Vector line refers to a data type used to represent the geometry and location of linear features in a geographic information system (GIS). It is a line segment composed of a series of continuous coordinate points and can be used to represent various geographic features, such as rivers, roads, and boundaries. Vector lines have directionality and length, and can describe the shape and topological relationship of the line by connecting coordinate points.

In the case where the historical vector line of the 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.

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 steps of:

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

S202, obtaining 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;

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 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.

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:

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

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

For newly generated boundary lines that need to be re-collected and stored in high-precision maps, a collection vehicle or an autonomous mining car is usually required to collect information about the boundary line. During the specific collection, the driver or the autonomous driving system will plan the collection trajectory in advance, and then the collection vehicle or the autonomous mining car will collect data along the collection trajectory. In general, the collection trajectory is the trajectory of the collection vehicle or the autonomous mining car along the newly generated boundary line.

As shown in FIG3 , it 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 comprises the steps of:

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

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

S303, clipping 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 line to be detected of the newly generated boundary line, wherein each point in the second data point set contains its corresponding coordinates.

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

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 the interpolation processing algorithm commonly used in the art, such as equal-pitch interpolation processing, etc., which is not specifically limited in the embodiment of the present disclosure.

Continuing to refer to FIG. 1 , 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 execute the steps:

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

In the field of autonomous driving, such as in mining areas, collection vehicles or autonomous mining vehicles carry sensor equipment for real-time perception of environmental information. The above sensors are used to obtain a set of three-dimensional point data of the surface of the target to be measured, i.e., point cloud data. Here, the measuring instrument may include but is not limited to laser radar, millimeter wave radar, ultrasonic radar, camera equipment, etc.

Whether it is a boundary line with a historical vector line in the high-precision map or a newly generated boundary line, the point cloud map corresponding to the high-precision map will store the point cloud data of the boundary line. The specific point cloud collection and storage method is already a well-known technology in the art and will not be repeated here.

After the point cloud data corresponding to the line to be detected is obtained 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.

4 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:

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

S402, judging whether the number of point clouds in the neighborhood of each point is greater than a preset number, so as to determine the validity of the point cloud coverage of each point; wherein the valid points on the detection line are point cloud coverage points, and the invalid points on the line to be detected are point cloud uncovered points;

S403, traversing each point on the line to be detected, and determining the validity of the point cloud coverage of each point until the traversal is completed.

In the embodiment of the present disclosure, taking FIG. 5 as an example, an example of performing a neighborhood search on a point cloud of a line to be detected is described.

As shown in Figure 5, assume that point A, point B, and point C are points on the line to be detected, and the gray points represent point cloud data. With length d as the radius, perform neighborhood searches on points A, B, and C respectively, and count the number of point clouds within the radius d of points A, B, and C respectively. As shown in Figure 5, there is no point cloud data within the radius d of point A, four point cloud data within the radius d of point B, and one point cloud data within the radius d of point C. If the validity is determined by the number of point clouds being greater than or equal to three, then point B on the line to be detected is a valid point cloud coverage point, and points A and C are invalid point cloud uncovered points.

It should be noted that, in practical applications, the length of the radius of the specific neighborhood search and the number of point clouds for judging the validity of the point cloud can be selected by those skilled in the art as needed, and the embodiments of the present disclosure do not impose specific restrictions on this. In addition, those skilled in the art can select other methods for neighborhood search, and the embodiments of the present disclosure do not impose restrictions on this.

Step S103, 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 and the number of the data points of the line to be detected;

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

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

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.

Step S104, determining whether the point cloud coverage is less than a preset threshold; if so, determining a re-collected area based on the uncovered points of the point cloud.

After calculating the point cloud coverage of the line to be detected according to step S103, determine whether the point cloud coverage of the line to be detected is less than a preset threshold; for example, 80%-90%. If the point cloud coverage is less than the threshold, the area to be re-collected is determined based on the set of uncovered points. It should be noted that the preset threshold of the point cloud coverage is set by technicians in this field according to the requirements of high-precision maps in the field of autonomous driving, and technicians in this field can adjust the preset threshold as needed.

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 point cloud uncovered area is re-determined, thereby avoiding unnecessary repeated collection and improving the collection efficiency.

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, as shown in FIG6 , the method for calculating a re-collection area comprises the following steps:

S601, clustering the uncovered point set in the boundary line to be measured, for example, Euclidean clustering, to obtain a cluster point set;

S602: Send the cluster point set to the collection terminal;

S603, merging the point cloud data re-collected by the collection terminal with the point cloud data in step S102;

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 preset 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.

The method for recalculating the acquisition area provided by the embodiment of the present disclosure collects the uncovered area of the point cloud, calculates the updated point cloud coverage, determines whether the updated point cloud coverage meets the preset threshold, and repeats the above steps until the acquisition ends when the point cloud coverage reaches the preset threshold. The method only collects the uncovered area of the point cloud whose point cloud coverage does not meet the preset threshold, avoids multiple unnecessary repeated acquisitions, and improves the acquisition efficiency.

Refer to Figure 7, which is a schematic diagram of the point cloud coverage of the spoil dump retaining wall line in a certain mine working area. Among them, the black point set is the vector line information of the spoil dump retaining wall line last time, and the white point set in the rectangular box is the area that needs to be collected after recalculating according to the point cloud boundary line coverage verification method of the embodiment of the present disclosure, wherein the vector line of the spoil dump retaining wall line is interpolated with a spacing of 0.1m, and after recollection, the point cloud coverage of the spoil dump retaining wall line reaches 100%.

As shown in FIG8 , it is a schematic diagram of the structure of a point cloud boundary line coverage verification device provided in the disclosed embodiment. The point cloud boundary line coverage verification device 800 includes:

An extraction module 801 is used to extract a line to be detected of a boundary line, wherein the line to be detected includes a number of data points;

The processing module 802 is 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 803, configured to determine 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 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 according to the uncovered points of the point cloud.

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.

Furthermore, the device 800 further includes a clustering module (not shown in the figure), which is used to cluster the uncovered point set of the point cloud, such as Euclidean clustering, to obtain a cluster point set;

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

The point cloud boundary line coverage verification device provided by the embodiment of the present disclosure first judges 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.

FIG9 is a schematic diagram of the structure of an electronic device according to an embodiment of the present disclosure. As shown in FIG9 , the electronic device 900 of 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 in the above-mentioned various method embodiments are implemented. Alternatively, when the processor 901 executes the computer program 903, the functions of each module/unit in the above-mentioned various device embodiments are implemented.

Exemplarily, 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 complete the present disclosure. The one or more modules/units may be a series of computer program instruction segments capable of completing specific functions, which are used to describe the execution process of the computer program 903 in the electronic device 900.

The electronic device 900 may be a desktop computer, a notebook, a PDA, a cloud server, or other electronic device. The electronic device 900 may include, but is not limited to, a processor 901 and a memory 902. Those skilled in the art may understand that FIG. 9 is only an example of the electronic device 900 and does not constitute a limitation on the electronic device 900. The electronic device 900 may include more or fewer components than shown in the figure, or may combine certain components, or may include different components. For example, the electronic device may also include input and output devices, network access devices, buses, etc.

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, etc.

The memory 902 may be an internal storage unit of the electronic device 900, for example, a hard disk or memory of the electronic device 900. The memory 902 may also be an external storage device of the electronic device 900, for example, a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the electronic device 900. Further, the memory 902 may also include both an internal storage unit of the electronic device 900 and an external storage device. The memory 902 is used to store computer programs and other programs and data required by the electronic device. The memory 902 may also be used to temporarily store data that has been output or is to be output.

Those skilled in the art can clearly understand that, for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be 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 embodiments can be integrated into a processing unit, or each unit can exist physically separately, 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 the present 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, which will not be repeated here.

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

Those of ordinary skill 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. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this disclosure.

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 only a logical function division. There may be other division methods in actual implementation. 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.

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

In addition, each functional unit in each embodiment of the present disclosure may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

If the integrated module/unit 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 this understanding, the present disclosure 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 computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. The computer program may include computer program code, and the computer program code may 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 computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium. 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, the computer-readable medium does not include electric carrier signals and telecommunication signals.

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 embodiments of the present disclosure, and should all be included in the protection scope of the present disclosure.

Claims (10)

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. The method according to claim 1 is characterized in that the extraction of the line to be detected of the boundary line includes: obtaining the historical vector line of the boundary line, and performing densification processing on the historical vector line to obtain the line to be detected. The method according to claim 1 is characterized in that the step of obtaining the line to be detected of the boundary line comprises: obtaining an acquisition trajectory line of the boundary line, and performing densification processing on the acquisition trajectory line to obtain the line to be detected. The method according to any one of claims 1 to 3 is characterized in that the neighborhood search is a radius search. The method according to any one of claims 1 to 3 is characterized in that determining the re-collected area based on the uncovered points of the point cloud comprises clustering the uncovered points of the point cloud to generate a set of clustered points. The method according to claim 5 is characterized in that the clustering is Euclidean clustering. The method according to claim 1 is characterized in that the method further comprises: collecting the re-collected area and recalculating the point cloud coverage until the point cloud coverage is not less than the preset threshold. 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. 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. An electronic device, comprising: 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.