CN117274289A

CN117274289A - Point cloud boundary line coverage verification method and device, electronic equipment and storage medium

Info

Publication number: CN117274289A
Application number: CN202311431074.6A
Authority: CN
Inventors: 蒋成; 王方建; 李雪松
Original assignee: Beijing Yikong Zhijia Technology Co Ltd
Current assignee: Beijing Yikong Zhijia Technology Co Ltd
Priority date: 2023-10-31
Filing date: 2023-10-31
Publication date: 2023-12-22

Abstract

The embodiment of the disclosure provides a point cloud boundary line coverage verification method, a device, electronic equipment and a storage medium, wherein the method comprises the following steps: extracting a to-be-detected line of the boundary line; acquiring point cloud data of a to-be-detected line, performing neighborhood search on the point cloud data of each point of the to-be-detected line, and determining the effectiveness of each point on the to-be-detected line by judging whether the number of point clouds in the neighborhood is larger than a preset threshold value; determining the point cloud coverage rate of the to-be-detected line according to the comparison of the number of the point cloud coverage points of the to-be-detected line and the number of the data points of the to-be-detected line; judging whether the cloud coverage rate of the points is smaller than a preset threshold value or not; if yes, determining a re-collected area according to the uncovered points of the point cloud. The point cloud boundary line coverage verification method provided by the embodiment of the disclosure avoids repeated and unnecessary point cloud data acquisition and improves acquisition efficiency on the premise of ensuring the integrity of acquired data.

Description

Point cloud boundary line coverage verification method and device, electronic equipment and storage medium

Technical Field

The embodiment of the disclosure relates to the technical field of automatic driving, in particular to a point cloud boundary line coverage verification method, a point cloud boundary line coverage verification device, electronic equipment and a storage medium.

Background

In the field of autopilot, a laser radar or a vision sensor can acquire point cloud data of an object in a three-dimensional space, and assist an autopilot vehicle to realize positioning and obstacle perception. Currently, most autopilot companies employ point cloud data collected by lidar or vision sensors to construct high-precision maps to assist autopilot.

However, when high-precision map data is acquired, there may be a problem of missing point cloud data of an acquisition area due to some reasons, for example, detailed route planning is not performed on the acquisition area before acquisition, which results in unreasonable acquisition route and incomplete coverage of the acquisition area, or the point cloud data cannot be uploaded normally during acquisition, which results in missing point cloud data.

At the same time, in special autopilot environments, such as mining areas, the topography of the mining area work area may change continuously, in particular the borderline of the mining area work area, for example: the boundary line of the road, the boundary line of the dumping site, the boundary line of the dumping line and the boundary line of the loading area, etc., and the change of the boundary line needs to be updated on the high-precision map in time so as to ensure the reliability of the high-precision map of the mining area.

In the prior art, when there is a data loss in the high-precision map or updating of the boundary line is required, the boundary line is generally repeatedly acquired for a plurality of times. However, such repeated collection is inefficient and there are problems in that the repeated collection is unnecessarily repeated a plurality of times, resulting in waste of resources.

Disclosure of Invention

The embodiment of the disclosure provides a point cloud boundary line coverage verification method, a device, electronic equipment and a storage medium, which can verify the point cloud coverage of a boundary line to be collected, so that the boundary line to be collected is efficiently collected, and the problem of repeated collection is avoided.

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

extracting a to-be-detected line of the boundary line, wherein the to-be-detected line comprises a plurality of data points;

acquiring point cloud data of the to-be-detected line, performing neighborhood search on the point cloud data of each point of the to-be-detected line, and determining the effectiveness of each point on the to-be-detected line by judging whether the number of point clouds in the neighborhood is larger than a preset number; the effective points on the line to be detected are point cloud covered points, and the ineffective points on the line to be detected are point cloud uncovered points;

determining the point cloud coverage rate of the to-be-detected line according to the comparison of the number of the point cloud coverage points of the to-be-detected line and the number of the data points of the to-be-detected line;

judging whether the point cloud coverage rate is smaller than a preset threshold value or not; if yes, determining a re-collected area according to the point cloud uncovered points.

Optionally, the extracting the to-be-detected line of the boundary line includes: and obtaining the history vector line of the boundary line, and performing interpolation processing on the history vector line to obtain the to-be-detected line.

Optionally, the obtaining the to-be-detected line of the boundary line includes: and acquiring an acquisition track line of the boundary line, and performing interpolation processing on the acquisition track line to obtain the to-be-detected line.

Optionally, the neighborhood search is a radius search.

Further, determining the re-collected area according to the point cloud uncovered points comprises clustering the point cloud uncovered points to generate a cluster point set.

Optionally, the cluster is an euclidean cluster.

Further, the method further comprises: and collecting the re-collected area, and re-calculating the point cloud coverage rate until the point cloud coverage rate is not smaller than the preset threshold value.

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

the extraction module is used for extracting a to-be-detected line of the boundary line, wherein the to-be-detected line comprises a plurality of data points;

the processing module is used for acquiring the point cloud data of the to-be-detected line, carrying out neighborhood search on the point cloud data of each point of the to-be-detected line, and determining the effectiveness of each point on the to-be-detected line by judging whether the number of the point clouds in the neighborhood is larger than a preset number or not; the effective points on the line to be detected are point cloud covered points, and the ineffective points on the line to be detected are point cloud uncovered points;

the computing module is used for determining the point cloud coverage rate of the to-be-detected line according to comparison of the number of the point cloud coverage points of the to-be-detected line and the number of the data points of the to-be-detected line;

the judging module is used for judging whether the point cloud coverage rate is smaller than a preset threshold value or not; if yes, determining a re-collected area according to the point cloud uncovered points.

In a third aspect, an embodiment of the present disclosure provides a computer readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, is capable of implementing a point cloud boundary line coverage verification method according to any one of the preceding claims.

In a fourth aspect, an embodiment of the present disclosure provides an electronic device, including: one or more processors; and the storage unit is used for storing one or more programs, and when the one or more programs are executed by the one or more processors, the one or more processors can realize the point cloud boundary line coverage verification method.

According to the point cloud boundary line coverage verification method and device, whether re-acquisition is needed or not is determined according to the point cloud coverage of the boundary line to be detected by calculating the point cloud coverage of the boundary line to be detected, and on the premise that the acquired data is complete, repeated and unnecessary point cloud data acquisition is avoided, and the acquisition efficiency is improved.

Drawings

Fig. 1 is a flow chart of a point cloud boundary line coverage verification method according to an embodiment of the disclosure;

FIG. 2 is a flow chart of calculating a to-be-detected line with a history vector line according to an embodiment of the disclosure;

FIG. 3 is a schematic flow chart of calculating a newly generated boundary line to be detected in an embodiment of the disclosure;

FIG. 4 is a schematic flow chart of judging whether a point on a line to be detected is valid or not according to an embodiment of the disclosure;

FIG. 5 is a schematic diagram of performing a point cloud neighborhood search on points on a line to be detected according to an embodiment of the disclosure;

FIG. 6 is a flow chart of a method of computing a reacquisition area in accordance with an embodiment of the disclosure;

FIG. 7 is a schematic view of the point cloud coverage of the dump wall line of a mine work area;

fig. 8 is a schematic structural diagram of a point cloud boundary line coverage verification device according to an embodiment of the disclosure; and

fig. 9 is a schematic structural diagram of an electronic device according to an embodiment of the disclosure.

Detailed Description

Reference will now be made in detail to exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, the same numbers in different drawings refer to the same or similar elements, unless otherwise indicated. The implementations described in the following exemplary examples are not representative of all implementations consistent with the present disclosure. Rather, they are merely examples of apparatus and methods consistent with some aspects of the present disclosure as detailed in the accompanying claims.

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

It should be understood that although the terms first, second, third, etc. may be used in this disclosure to describe various information, these information should not be limited to these terms. These terms are only used to distinguish one type of information from 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 the present disclosure. The word "if" as used herein may be interpreted as "at … …" or "at … …" or "responsive to a determination", depending on the context.

A method and apparatus for checking coverage of a point cloud boundary line according to embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings.

Fig. 1 is a schematic flow chart of a point cloud boundary line coverage verification method provided in an embodiment of the disclosure. As shown in fig. 1, the point cloud boundary line coverage verification method 100 includes the steps of:

s101, extracting a to-be-detected line of a boundary line to obtain a set of data points of the to-be-detected line;

in the embodiment of the present disclosure, taking a mining area as an example, there may be boundary lines at the mining area, such as boundary lines of roads, boundary lines of a dump, boundary lines of a loading area, and the like. In practical applications, the boundary line may be a history vector line stored in the high-precision map, or a newly generated boundary line, which needs to be collected again and stored in the high-precision map, and according to the two cases, how to extract the to-be-detected line of the boundary line will be described separately.

Referring to fig. 2, a flow chart of extracting a to-be-detected line of a boundary line is shown for a history vector line stored in a high-definition map.

Vector lines refer to the type of data used to represent the geometry and location of linear elements in a Geographic Information System (GIS). It is a line segment composed of a series of successive coordinate points, and can be used to represent various geographical elements such as rivers, roads, boundary lines, etc. The vector line has directivity and length, and the shape and topological relation of the line shape can be described by the connection of coordinate points.

In the case that the history vector line of the boundary line is already stored in the high-precision map, the history vector line can be directly obtained from the high-precision map, and the to-be-detected line of the boundary line can be obtained according to the history vector line.

Fig. 2 is a flow chart of a method 200 for computing a to-be-detected line with a history vector line according to an embodiment of the disclosure. The method 200 comprises the steps of:

s201, downloading a history vector line of the boundary line from history data of a high-precision map;

s202, acquiring corresponding starting points and end points on the history vector line according to the position coordinates of the starting points and the end points of the boundary line;

s203, clipping points except the starting point and the end point of the historical vector line to obtain a set of points positioned on the historical vector line between the starting point and the end point, which is called a first data point set; the line formed by the first data point set is the to-be-detected line of the boundary line, wherein each point in the first data point set contains corresponding coordinates.

Further, in practical application, the points on the history vector line corresponding to the to-be-detected line of the boundary line may have sparse density, and may not meet the requirement of data processing precision. In this case, the method 200 further comprises the steps of:

s204, after the history vector line is subjected to interpolation processing, the to-be-detected line of the boundary line is obtained. The specific interpolation processing manner may be an interpolation processing algorithm commonly used in the art, for example, equidistant interpolation processing, which is not limited in this embodiment of the disclosure.

Referring to fig. 3, a flow chart of the to-be-detected line for which the boundary line needs to be extracted for the newly generated boundary line is schematically shown.

For newly generated borderlines, where it is necessary to re-collect and store in a high-precision map, it is often necessary to collect information about the borderlines by a collection truck or an autonomous mining truck. During specific collection, a driver or an automatic driving system plans a collection track line in advance, and then a collection vehicle or an automatic driving mine car performs data collection along the collection track line. Typically, the collection track line is a track along which the collection truck or the autonomous mining truck travels along the newly generated boundary line.

As shown in fig. 3, a flowchart of a method 300 for calculating a newly generated boundary line to be detected is shown in an embodiment of the disclosure. The method 300 comprises the steps of:

s301, extracting a collection track line of a collection vehicle or a mine car driven by an unmanned aerial vehicle;

s302, acquiring a starting point and an ending point of the acquisition track line;

s303, clipping points except the starting point and the end point of the acquisition track line to obtain a set of vector line points corresponding to the acquisition track line between the starting point and the end point, which is called a second data point set; the line formed by the second data point set is the newly generated boundary line to be detected line, wherein each point in the second data point set contains corresponding coordinates.

Further, in practical application, the points on the acquisition track line corresponding to the to-be-detected line of the newly generated boundary line may have sparse density, and may not meet the requirement of data processing precision. Thus, the method 300 further comprises the steps of:

s304, after the acquisition track line is subjected to interpolation processing, the newly generated boundary line to-be-detected line is obtained. The specific interpolation processing manner may be an interpolation processing algorithm commonly used in the art, for example, equidistant interpolation processing, which is not limited in this embodiment of the disclosure.

With continued reference to fig. 1, after obtaining the to-be-detected line of the boundary line, the point cloud boundary line coverage verification method 100 of the embodiment of the present disclosure continues to execute the steps of:

s102, acquiring point cloud data of the to-be-detected line, performing neighborhood search on the point cloud data of each point of the to-be-detected line, and determining the effectiveness of each point by judging whether the number of point clouds in the neighborhood is larger than a preset number;

in the field of autopilot, such as mining areas, collection vehicles or autopilot mining vehicles carry sensor devices for sensing environmental information in real time. The sensor is used for obtaining a three-dimensional point data set, namely point cloud data, of the appearance surface of the object to be measured. Here, the measuring instrument may include, but is not limited to, a laser radar, a millimeter wave radar, an ultrasonic radar, an image pickup apparatus, and the like.

The point cloud data of the boundary line is stored in the point cloud map corresponding to the high-definition map, regardless of whether the boundary line of the history vector line exists in the high-definition map or the newly generated boundary line. Specific point cloud collection and storage methods are already known in the art and are not described herein.

After the point cloud data corresponding to the to-be-detected line is obtained in step S102, performing a traversal point cloud data neighborhood search on each point on the to-be-detected line, thereby judging the validity of the point cloud coverage of each point of the to-be-detected line.

Referring specifically to fig. 4, a flow chart of determining whether a point on a line to be detected is valid according to an embodiment of the disclosure is shown. The method 400 includes the steps of:

s401, carrying out neighborhood search on the point cloud data of each point of the to-be-detected line;

s402, judging whether the number of the point clouds in the neighborhood of each point is larger than a preset number or not to determine the effectiveness of the point cloud coverage of each point; the effective points on the detection line are point cloud covered points, and the ineffective points on the line to be detected are point cloud uncovered points;

s403, traversing each point on the line to be detected, and judging the effectiveness of point cloud coverage of each point until the traversing is finished.

In the embodiment of the disclosure, taking fig. 5 as an example, a neighborhood search is performed on a point cloud to be detected.

As shown in fig. 5, assume that a yellow dot a, a blue dot B, and a green dot C are points on the detection line, and a red dot represents point cloud data. With the length d as the radius, the neighborhood search is performed on the points A, B, C, and the number of point clouds in the radius d of the point A, B, C is counted. As shown in fig. 5, there is no point cloud data within the radius d of the point a, there are four point cloud data within the radius d of the point B, and there is one point cloud data within the radius d of the point C. If the number of the three point clouds is greater than or equal to the three point clouds, judging whether the point clouds are effective, wherein the point B on the to-be-detected line is an effective point cloud coverage point, and the point A, C is an ineffective point cloud uncovered point.

It should be noted that, in practical application, the length of the radius of the specific neighborhood search and the number of point clouds for determining the effectiveness of the point clouds may be selected by those skilled in the art as required, which is not specifically limited in the embodiments of the present disclosure. And, those skilled in the art may select other ways to perform the neighborhood search, which is not limited in the embodiments of the present disclosure.

Step S103, determining the point cloud coverage rate of the to-be-detected line according to the comparison of the number of the point cloud coverage points and the number of the data points of the to-be-detected line;

after determining that the point on the to-be-detected line is the point cloud covered point and the point cloud uncovered point in step S102, in step S103, the point cloud coverage of the to-be-detected line is determined according to the comparison between the number of the point cloud covered points of the to-be-detected line and the number of the data points of the to-be-detected line. The point cloud coverage rate of the to-be-detected line is calculated by adopting the following formula (1);

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

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

Step S104, judging whether the point cloud coverage rate is smaller than a preset threshold value or not; if yes, determining a re-collected area according to the point cloud uncovered points.

After calculating the point cloud coverage rate of the to-be-detected line according to step S103, judging whether the point cloud coverage rate of the to-be-detected line is smaller than a preset threshold value; for example 80% -90%. If the point cloud coverage is less than the threshold, determining a re-acquired area according to the set of uncovered points. It should be noted that, the preset threshold value of the point cloud coverage rate is set by a person skilled in the art according to the requirement of the high-precision map in the automatic driving field, and the person skilled in the art can adjust the preset threshold value according to the requirement.

According to the point cloud boundary line coverage verification method provided by the embodiment of the disclosure, when the point cloud data of the boundary line to be detected is missing, the point cloud coverage of the boundary line to be detected is judged first, and when the point cloud coverage of the boundary line to be detected is smaller than the preset threshold value, the point cloud uncovered area is redetermined, so that unnecessary repeated collection is avoided, and the collection efficiency is improved.

Further, referring to fig. 6, a flowchart of a method for calculating a reacquisition area according to an embodiment of the present disclosure is shown. As shown in fig. 6, the method for calculating the re-acquisition area includes the steps of:

s601, clustering uncovered point sets in the boundary line to be detected, such as European clustering, to obtain clustered point sets;

s602; the cluster point set is sent to an acquisition terminal;

s603, combining the point cloud data re-acquired by the acquisition terminal with the point cloud data in the step S102;

s604, continuing to execute the steps S103, S104 and S105 to judge the point cloud coverage rate of the detection line; if the point cloud coverage rate is still smaller than the predetermined threshold, the steps S601, S602, S603, S604, S102, S103, S104 and S105 are continuously executed until the point cloud coverage rate of the to-be-detected line reaches the predetermined threshold, the re-acquired area is not required to be calculated, and the acquisition is ended.

According to the method for recalculating the acquisition area, after the point cloud uncovered area is acquired, the updated point cloud coverage rate is calculated, whether the updated point cloud coverage rate meets the preset threshold value is judged, and the steps are repeatedly executed until the acquisition is finished when the point cloud coverage rate reaches the preset threshold value. According to the method, only the point cloud uncovered area with the point cloud coverage rate not reaching the preset threshold value is collected, so that multiple unnecessary repeated collection is avoided, and the collection efficiency is improved.

Referring to fig. 7, a schematic view of the point cloud coverage of the retaining wall line of the dump in a certain mine working area is shown. The black point set is vector line information of the last earth-discharge-field retaining wall line, the white point set is a calculated area needing to be collected according to the point cloud boundary line coverage checking method of the embodiment of the disclosure, the vector line of the earth-discharge-field retaining wall line is subjected to interpolation processing at a distance of 0.1m, and after the earth-discharge-field retaining wall line is collected again, the point cloud coverage of the earth-discharge-field retaining wall line reaches 100%.

Fig. 8 is a schematic structural diagram of a point cloud boundary line coverage verification device according to an embodiment of the disclosure. The point cloud boundary line coverage verification device 800 includes:

the extraction module 801 is configured to extract a to-be-detected line of the boundary line, where the to-be-detected line includes a plurality of data points;

the processing module 802 is configured to obtain point cloud data of the to-be-detected line, perform a neighborhood search on the point cloud data of each point of the to-be-detected line, and determine validity of each point on the to-be-detected line by determining whether the number of point clouds in the neighborhood is greater than a preset number; the effective points on the line to be detected are point cloud covered points, and the ineffective points on the line to be detected are point cloud uncovered points;

a calculating module 803, configured to determine a point cloud coverage rate of the to-be-detected line according to a comparison between the number of point cloud coverage points of the to-be-detected line and the number of data points of the to-be-detected line;

a judging module 804, configured to judge whether the point cloud coverage rate is less than a preset threshold; if yes, determining a re-collected area according to the point cloud uncovered points.

Further, the apparatus 800 further includes a communication module 805, where when the determining module 804 determines whether the point cloud coverage is less than a preset threshold, the communication module 805 sends information of the area that needs to be collected again to a collection terminal (not shown), such as a collection vehicle or an unmanned mine car; and after the acquisition terminal re-acquires, the communication module 805 receives the re-acquired point cloud data sent by the acquisition terminal.

Further, the apparatus 800 further includes a clustering module (not shown in the figure) configured to cluster the set of uncovered points of the point cloud, for example, perform an euro clustering, to obtain a set of clustered points;

the processing module 802 is configured to obtain, according to the set of clustered points, a point on the acquired boundary line.

According to the point cloud boundary line coverage verification device provided by the embodiment of the disclosure, when the point cloud data of the boundary line to be detected is missing, the point cloud coverage of the boundary line to be detected is judged first, when the point cloud coverage is smaller than the preset threshold, the point cloud uncovered area is redetermined, and the point cloud uncovered area is redeployed, so that unnecessary repeated collection is avoided, and the collection efficiency is improved.

Fig. 9 is a schematic structural diagram of an electronic device according to an embodiment of the present disclosure. As shown in fig. 9, 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. The steps of the various method embodiments described above are implemented when the processor 901 executes the computer program 903. Alternatively, the processor 901 performs the functions of the modules/units in the above-described apparatus embodiments when executing the computer program 903.

Illustratively, the computer program 903 may be partitioned into one or more modules/units, which are stored in the memory 902 and executed by the processor 901 to complete the present disclosure. One or more of the modules/units may be a series of computer program instruction segments capable of performing particular functions to describe the execution of the computer program 903 in the electronic device 900.

The electronic device 900 may be a desktop computer, a notebook computer, a palm computer, a cloud server, or the like. The electronic device 900 may include, but is not limited to, a processor 901 and a memory 902. It will be appreciated by those skilled in the art that fig. 9 is merely an example of an electronic device 900 and is not intended to limit the electronic device 900, and may include more or fewer components than shown, or may combine certain components, or different components, e.g., an electronic device may also include an input-output device, a network access device, a bus, etc.

The processor 901 may be a central processing unit (Central Processing Unit, CPU) or other general purpose processor, digital signal processor (Dig i ta l Sig na l Processor, DSP), application specific integrated circuit (Application Specific Integrated Circuit, ASIC), field programmable gate array (Field-Programmable Gate Array, FPGA) or other programmable logic device, discrete gate or transistor logic device, discrete hardware components, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.

The memory 902 may be an internal storage unit of the electronic device 900, for example, a hard disk or a 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 (Flash Card) or the like, which are provided on the electronic device 900. Further, the memory 902 may also include both internal and external storage units of the electronic device 900. 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.

It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-described division of the functional units and modules is illustrated, and in practical application, the above-described functional distribution may be performed by different functional units and modules according to needs, i.e. the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-described functions. The functional units and modules in the embodiment may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit, where the integrated units may be implemented in a form of hardware or a form of a software functional unit. In addition, specific names of the functional units and modules are also only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present disclosure. The specific working process of the units and modules in the above system may refer to the corresponding process in the foregoing method embodiment, which is not described herein again.

In the foregoing embodiments, the descriptions of the embodiments are emphasized, and in part, not described or illustrated in any particular embodiment, reference is made to the related descriptions of other embodiments.

Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present disclosure.

In the embodiments provided in the present disclosure, it should be understood that the disclosed apparatus/electronic device and method may be implemented in other manners. For example, the apparatus/electronic device embodiments described above are merely illustrative, e.g., the division of modules or elements is merely a logical functional division, and there may be additional divisions of actual implementations, multiple elements or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection via interfaces, devices or units, which may be in electrical, mechanical or other forms.

The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed over a plurality of 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 in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.

The integrated modules/units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the present disclosure may implement all or part of the flow of the method of the above-described embodiments, or may be implemented by a computer program to instruct related hardware, and the computer program may be stored in a computer readable storage medium, where the computer program, when executed by a processor, may implement the steps of the method embodiments described above. The computer program may comprise computer program code, which may be in source code form, object code form, executable file or in some intermediate form, etc. The computer readable medium may include: any entity or device capable of carrying computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), an electrical carrier signal, a telecommunications signal, a software distribution medium, and so forth. It should be noted that the content of the computer readable medium can be appropriately increased or decreased according to the requirements of the jurisdiction's jurisdiction and the patent practice, for example, in some jurisdictions, the computer readable medium does not include electrical carrier signals and telecommunication signals according to the jurisdiction and the patent practice.

The above embodiments are merely for illustrating the technical solution of the present disclosure, and are not limiting thereof; although the present disclosure has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the disclosure, and are intended to be included in the scope of the present disclosure.

Claims

1. A point cloud boundary line coverage verification method, the method comprising:

2. The method of claim 1, wherein the extracting the boundary line to-be-detected line comprises: and obtaining the history vector line of the boundary line, and performing interpolation processing on the history vector line to obtain the to-be-detected line.

3. The method of claim 1, wherein the acquiring the line of boundary to be detected comprises: and acquiring an acquisition track line of the boundary line, and performing interpolation processing on the acquisition track line to obtain the to-be-detected line.

4. A method according to any one of claims 1 to 3, wherein the neighborhood search is a radius search.

5. A method according to any one of claims 1 to 3, wherein said determining a re-acquired area from said point cloud uncovered points comprises clustering said point cloud uncovered points to generate a set of clustered points.

6. The method of claim 5, wherein the clusters are euclidean clusters.

7. The method according to claim 1, wherein the method further comprises: and collecting the re-collected area, and re-calculating the point cloud coverage rate until the point cloud coverage rate is not smaller than the preset threshold value.

8. A point cloud boundary line coverage verification device, the method comprising:

9. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, is capable of implementing the point cloud boundary line coverage verification method according to any one of claims 1 to 7.

10. An electronic device, comprising:

one or more processors;

a storage unit configured 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.