CN114993348A - Map precision testing method and device, electronic equipment and storage medium - Google Patents
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Abstract
The invention discloses a map precision testing method, a map precision testing device, electronic equipment and a storage medium, wherein the method comprises the following steps: for a plurality of maps to be detected, respectively extracting the mark position information of each map to be detected; clustering the mark position information matched with each map to be detected respectively to obtain the target position information of each mark position; and when any map to be tested is subjected to precision testing, determining the map precision of the map to be tested based on the mark position information corresponding to the same mark position and the target position information. According to the technical scheme, a true value is not required to be measured on site, manpower and material resources are saved, and the map testing efficiency is improved.
Description
Technical Field
The invention relates to the technical field of high-precision maps, in particular to a map precision testing method and device, electronic equipment and a storage medium.
Background
The high-precision map is manufactured by compiling the map acquisition vehicle, and the precision of testing the high-precision map becomes a crucial problem.
The traditional test method is as follows: in the process of data acquisition, a professional measurer obtains absolute longitude and latitude information at a fixed mark point by using high-precision measuring equipment as a true value, the longitude and latitude information derived from the fixed mark point in a high-precision map is used as a test value, and the difference value between the true value and the measured value is used as an absolute error. And calculating to obtain absolute error accuracy by using statistical information.
This method has at least the following problems: a large number of professional measuring personnel are needed to collect a large number of sample points on site, a large amount of costs such as manpower, material resources, financial resources and time are consumed, and the testing efficiency is low.
Disclosure of Invention
The invention provides a map precision testing method, a map precision testing device, electronic equipment and a storage medium, so that field measurement truth value is not required in testing, the problem of data acquisition is solved, and the map testing efficiency is improved.
According to an aspect of the present invention, there is provided a map accuracy testing method, including:
respectively extracting the mark position information of each map to be detected for a plurality of maps to be detected;
clustering the mark position information matched with each map to be detected respectively to obtain the target position information of each mark position;
and when any map to be tested is subjected to precision testing, determining the map precision of the map to be tested based on the mark position information corresponding to the same mark position and the target position information.
According to another aspect of the present invention, there is provided a map accuracy testing apparatus, including:
the information extraction module is used for respectively extracting the mark position information of each map to be detected for a plurality of maps to be detected;
the information clustering module is used for respectively clustering the mark position information matched in each map to be tested to obtain the target position information of each mark position;
and the map precision determining module is used for determining the map precision of the map to be tested based on the mark position information and the target position information corresponding to the same mark position when any map to be tested is subjected to precision testing.
According to another aspect of the present invention, there is provided an electronic apparatus including:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,
the memory stores a computer program executable by the at least one processor, the computer program being executable by the at least one processor to enable the at least one processor to perform the map accuracy testing method of any embodiment of the present invention.
According to another aspect of the present invention, there is provided a computer-readable storage medium storing computer instructions for causing a processor to implement the map accuracy testing method according to any one of the embodiments of the present invention when the computer instructions are executed.
According to the technical scheme of the embodiment of the invention, the mark position information of each map to be measured is respectively extracted for a plurality of maps to be measured, and the matched mark position information in each map to be measured is respectively clustered to obtain the target position information with the same reliability as the measured true value, so that the problem of data acquisition is solved; furthermore, when any map to be tested is subjected to precision testing, the map precision of the map to be tested is determined based on the mark position information and the target position information corresponding to the same mark position, a true value does not need to be measured on site, manpower and material resources are saved, and therefore the map testing efficiency is improved.
It should be understood that the statements in this section are not intended to identify key or critical features of the embodiments of the present invention, nor are they intended to limit the scope of the invention. Other features of the present invention will become apparent from the following description.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
FIG. 1 is a flow chart of a method for testing a map accuracy according to an embodiment of the present invention;
FIG. 2 is a flowchart of a method for testing a map accuracy according to a second embodiment of the present invention;
FIG. 3 is a flowchart of a method for testing the accuracy of a map according to a third embodiment of the present invention;
FIG. 4 is a schematic structural diagram of a map accuracy testing apparatus according to a fourth embodiment of the present invention;
fig. 5 is a schematic structural diagram of an electronic device implementing the map precision testing method according to the embodiment of the present invention.
Detailed Description
In order to make the technical solutions of the present invention better understood, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in sequences other than those illustrated or described herein. Moreover, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Example one
Fig. 1 is a flowchart of a map precision testing method according to an embodiment of the present invention, where the present embodiment is applicable to a case of performing precision testing on a vehicle navigation map, and the method may be performed by a map precision testing device, where the map precision testing device may be implemented in a form of hardware and/or software, and the map precision testing device may be configured in a terminal and/or a server. As shown in fig. 1, the method includes:
and S110, respectively extracting the mark position information of each map to be detected for a plurality of maps to be detected.
The map to be tested can be a vehicle-mounted electronic navigation map to be subjected to precision testing. One or more position points can be selected from each map to be tested for precision testing. The mark position information refers to the position of a mark position in the map to be detected, and the mark position can be a randomly selected or manually pre-selected position point. For example, the marker position information may be longitude and latitude coordinates of a marker position selected from a map to be detected; alternatively, the marker position information may be three-dimensional position coordinates, which is not limited herein.
Specifically, a plurality of maps to be measured are obtained, and the marker position information is derived from each map to be measured. The marker position information may be longitude and latitude coordinates of all the marker bits of the map to be detected, or may be longitude and latitude coordinates of randomly selected marker bits, which is not limited herein.
And S120, clustering the mark position information matched with each map to be detected respectively to obtain the target position information of each mark position.
The matched mark position information refers to the position information of the same mark position in each map to be detected. It can be understood that the map to be measured may include one or more matched mark position information, and when the map to be measured includes a plurality of matched mark position information, the plurality of mark position information in the plurality of maps to be measured may be clustered at the same time, thereby increasing the processing speed of clustering. The target location information is the result of clustering of matched marker location information in multiple maps under test.
It should be emphasized that the target location information is a result of clustering of the matching flag location information in the multiple maps to be tested, and the sum of the differences between the target location information and the matching flag location information in each map to be tested is minimum, which indicates that the reliability of the target location information is close to the true value obtained by the high-precision measuring equipment, so that the target location information can be used as a virtual true value, and when any map to be tested is subjected to precision test, the map precision of the map to be tested is determined according to the flag location information and the target location information corresponding to the same flag, thereby avoiding manual acquisition of a flag sample on site, saving manpower and material resources, and improving the map testing efficiency.
S130, when any map to be tested is subjected to precision testing, determining the map precision of the map to be tested based on the mark position information corresponding to the same mark position and the target position information.
The mark position information and the target position information corresponding to the same mark bit mean that the mark position information and the target position information belong to the same mark bit. It is understood that only the flag position information and the target position information of the same flag bit are comparable. The map precision of the map to be detected can be the position error and the position error of the map to be detected.
In some embodiments, the map accuracy of the map to be measured can be determined by selecting the marker position information and the target position information corresponding to one marker bit; in some embodiments, a plurality of flag bits may be selected, and the map accuracy of the map to be measured is determined according to the flag position information and the target position information corresponding to each flag bit, which may be understood that the more flag bits are selected, the more reliable the map accuracy is obtained.
According to the technical scheme of the embodiment of the invention, the mark position information of each map to be measured is respectively extracted for a plurality of maps to be measured, and the matched mark position information in each map to be measured is respectively clustered to obtain the target position information with the same reliability as the measured true value, so that the problem of data acquisition is solved; furthermore, when any map to be tested is subjected to precision testing, the map precision of the map to be tested is determined based on the mark position information and the target position information corresponding to the same mark position, a true value does not need to be measured on site, manpower and material resources are saved, and therefore the map testing efficiency is improved.
Example two
Fig. 2 is a flowchart of a map precision testing method according to a second embodiment of the present invention, and the system according to the present embodiment may be combined with various alternatives of the map precision testing method according to the foregoing embodiments. The map precision testing method provided by the embodiment is further optimized. Optionally, the clustering the mark position information matched with each map to be tested to obtain the target position information of each mark position includes: and respectively inputting the mark position information matched with each map to be tested into a position clustering model to obtain the target position information of each mark position, wherein the position clustering model is a machine learning model.
As shown in fig. 2, the method includes:
s210, respectively extracting the mark position information of each map to be detected for a plurality of maps to be detected.
And S220, respectively inputting the mark position information matched with each map to be detected into a position clustering model to obtain the target position information of each mark position, wherein the position clustering model is a machine learning model.
The position clustering model may be a machine learning model, and may be configured to perform clustering processing on the marker position information in each map to be measured to obtain target position information of the marker position.
It can be understood that the target position information obtained through clustering is close to the true value obtained through the high-precision measuring equipment, so that the target position information obtained through clustering can be used as a virtual true value, and when the precision of any map to be tested is tested, the map precision of the map to be tested is determined according to the mark position information and the target position information corresponding to the same mark position, so that the situation that a mark position sample is manually collected on site can be avoided, manpower and material resources are saved, and the map testing efficiency is improved.
Illustratively, the longitude and latitude coordinates of each zone bit are input into a position clustering model for fitting clustering, so that a plurality of longitude and latitude coordinates of the same zone bit are aggregated into one target position information. The position clustering model is not limited here, for example, K-means and the like, in some embodiments, the position clustering model may also be a deep learning model trained in advance, and a deep learning model network architecture is not limited here and can be selected and built according to experience.
And S230, when any map to be tested is subjected to precision testing, determining the map precision of the map to be tested based on the mark position information corresponding to the same mark position and the target position information.
According to the technical scheme of the embodiment of the invention, the marker position information matched with each map to be tested is input into the position clustering model respectively to obtain the target position information, and the target position information obtained through clustering is close to the true value obtained through high-precision measuring equipment, so that the target position information obtained through clustering can be used as a virtual true value, the marker position sample can be prevented from being manually collected on site, manpower and material resources are saved, and the map testing efficiency is improved.
EXAMPLE III
Fig. 3 is a flowchart of a map precision testing method provided in the third embodiment of the present invention, and the system of the present embodiment and each alternative in the map precision testing method provided in the foregoing embodiments may be combined. The map precision testing method provided by the embodiment is further optimized. Optionally, the determining the map accuracy of the map to be measured based on the marker position information and the target position information corresponding to the same marker bit includes: determining a position error of each marker bit based on marker position information corresponding to the same marker bit and the target position information; and determining the map precision of the map to be tested based on the position error of each marker bit.
As shown in fig. 3, the method includes:
s310, for the plurality of maps to be detected, the mark position information of each map to be detected is respectively extracted.
And S320, clustering the mark position information matched with each map to be detected respectively to obtain the target position information of each mark position.
S330, when any map to be tested is subjected to precision testing, determining the position error of each marker bit based on the marker position information corresponding to the same marker bit and the target position information.
The position error refers to a difference between the mark position information and the target position information of the same mark bit. The target position information may be used as a metric. It can be understood that, if the difference value between the marker position information and the target position information of the map to be measured is larger, the larger the position error of the marker position is indicated.
On the basis of the foregoing embodiment, the determining a position error of each marker bit based on marker position information and the target position information corresponding to the same marker bit includes: and determining the coordinate difference between the mark position information corresponding to the same mark bit and the target position information as the position error of each mark bit.
Illustratively, the longitude and latitude coordinates of the flag bit of the map to be measured are subtracted from the longitude and latitude coordinates obtained by clustering, so as to obtain the position error of the flag bit. It can be understood that the map to be tested may include a plurality of flag bits, and therefore, the position errors corresponding to the plurality of flag bits may be determined simultaneously, so as to improve the efficiency of the map accuracy test.
S340, determining the map precision of the map to be measured based on the position error of each marker bit.
In this embodiment, the map accuracy of the map to be measured may be determined by the position errors of the plurality of flag bits, where the determination method may be a statistical method, such as an averaging algorithm, a least square method, or the like.
On the basis of the foregoing embodiment, the determining the map accuracy of the map to be measured based on the position error of each flag bit includes: carrying out mean processing on the position errors of the zone bits to obtain the map precision of the map to be measured; or determining the map accuracy of the map to be measured when the sum of squares of the position errors of the marker bits is minimum.
For example, in some embodiments, the position errors of the marker bits are added to obtain a position error sum of the marker bits, the position error sum is divided by the number of the marker bits to obtain a position error mean value, and the position error mean value is used as the map accuracy of the map to be measured, so that the reliability of the map accuracy of the map to be measured is improved. In some embodiments, the position error of each flag bit may be calculated by using a least square method, so that the sum of squares of the position errors of each flag bit is minimized, thereby obtaining the map accuracy of the map to be measured.
On the basis of the above embodiment, the map to be measured includes an automatic driving map or an auxiliary driving map.
The automatic driving map or the auxiliary driving map can be a high-precision map, and the automatic driving map or the auxiliary driving map can be acquired and compiled by a map acquisition vehicle.
According to the technical scheme of the embodiment of the invention, when any map to be tested is subjected to precision test, the position error of each marker bit is determined according to the marker position information corresponding to the same marker bit and the target position information obtained through clustering, and the marker bit sample data does not need to be manually acquired in the determination process, so that the sample data does not need to be manually acquired on site, and the map test efficiency is improved.
Example four
Fig. 4 is a schematic structural diagram of a map precision testing apparatus according to a fourth embodiment of the present invention. As shown in fig. 4, the apparatus includes:
the information extraction module 410 is configured to, for multiple maps to be detected, respectively extract the marker position information of each map to be detected; an information clustering module 420, configured to cluster the matched flag position information in each map to be tested, respectively, to obtain target position information of each flag bit; the map precision determining module 430 is configured to determine, when any map to be tested is subjected to precision testing, the map precision of the map to be tested based on the marker position information and the target position information corresponding to the same marker bit.
According to the technical scheme of the embodiment of the invention, the mark position information of each map to be measured is respectively extracted for a plurality of maps to be measured, and the matched mark position information in each map to be measured is respectively clustered to obtain the target position information with the same reliability as the measurement truth value, so that the field measurement truth value is not needed, and the problem of data acquisition is solved; furthermore, when any map to be tested is subjected to precision testing, the map precision of the map to be tested is determined based on the mark position information and the target position information corresponding to the same mark position, a true value does not need to be measured, manpower and material resources are saved, and therefore the map testing efficiency is improved.
Optionally, the information clustering module 420 is further configured to:
and respectively inputting the mark position information matched with each map to be tested into a position clustering model to obtain the target position information of each mark position, wherein the position clustering model is a machine learning model.
Optionally, the map precision determining module 430 includes:
a position error determination unit configured to determine a position error of each flag bit based on flag position information corresponding to the same flag bit and the target position information;
and the map precision determining unit is used for determining the map precision of the map to be measured based on the position error of each marker bit.
Optionally, the position error determining unit is specifically configured to:
and determining the coordinate difference between the mark position information corresponding to the same mark bit and the target position information as the position error of each mark bit.
Optionally, the map precision determining unit is specifically configured to:
carrying out mean value processing on the position errors of the marker positions to obtain the map precision of the map to be measured; or,
and determining the map precision of the map to be measured under the condition that the square sum of the position errors of the marker bits is minimum.
Optionally, the map to be tested includes an automatic driving map or an auxiliary driving map.
The map precision testing device provided by the embodiment of the invention can execute the map precision testing method provided by any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method.
EXAMPLE five
FIG. 5 illustrates a schematic diagram of an electronic device 10 that may be used to implement an embodiment of the invention. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smart phones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the inventions described and/or claimed herein.
As shown in fig. 5, the electronic device 10 includes at least one processor 11, and a memory communicatively connected to the at least one processor 11, such as a Read Only Memory (ROM)12, a Random Access Memory (RAM)13, and the like, wherein the memory stores a computer program executable by the at least one processor, and the processor 11 can perform various suitable actions and processes according to the computer program stored in the Read Only Memory (ROM)12 or the computer program loaded from a storage unit 18 into the Random Access Memory (RAM) 13. In the RAM 13, various programs and data necessary for the operation of the electronic apparatus 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other via a bus 14. An input/output (I/O) interface 15 is also connected to bus 14.
A number of components in the electronic device 10 are connected to the I/O interface 15, including: an input unit 16 such as a keyboard, a mouse, or the like; an output unit 17 such as various types of displays, speakers, and the like; a storage unit 18 such as a magnetic disk, an optical disk, or the like; and a communication unit 19 such as a network card, modem, wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information/data with other devices via a computer network such as the internet and/or various telecommunication networks.
The processor 11 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various processors running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, or the like. The processor 11 performs the various methods and processes described above, such as a map accuracy testing method.
In some embodiments, the map accuracy testing method may be implemented as a computer program tangibly embodied in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 10 via the ROM 12 and/or the communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the map accuracy testing method described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured by any other suitable means (e.g., by means of firmware) to perform a map accuracy testing method comprising:
for a plurality of maps to be detected, respectively extracting the mark position information of each map to be detected;
clustering the mark position information matched with each map to be detected respectively to obtain the target position information of each mark position;
and when any map to be tested is subjected to precision testing, determining the map precision of the map to be tested based on the mark position information corresponding to the same mark position and the target position information.
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuitry, Field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), system on a chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.
A computer program for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions/acts specified in the flowchart and/or block diagram block or blocks to be performed. A computer program can execute entirely on a machine, partly on a machine, as a stand-alone software package partly on a machine and partly on a remote machine or entirely on a remote machine or server.
In the context of the present invention, a computer-readable storage medium may be a tangible medium that can contain, or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. A computer readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, the computer readable storage medium may be a machine readable signal medium. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user can provide input to the electronic device. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic, speech, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), Wide Area Networks (WANs), blockchain networks, and the internet.
The computing system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical host and VPS service are overcome.
It should be understood that various forms of the flows shown above, reordering, adding or deleting steps, may be used. For example, the steps described in the present invention may be executed in parallel, sequentially, or in different orders, and are not limited herein as long as the desired results of the technical solution of the present invention can be achieved.
The above-described embodiments should not be construed as limiting the scope of the invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made in accordance with design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A map precision testing method is characterized by comprising the following steps:
for a plurality of maps to be detected, respectively extracting the mark position information of each map to be detected;
clustering the mark position information matched with each map to be detected respectively to obtain the target position information of each mark position;
and when any map to be tested is subjected to precision test, determining the map precision of the map to be tested based on the mark position information and the target position information corresponding to the same mark position.
2. The method according to claim 1, wherein the clustering the matched marker position information in each map to be tested to obtain the target position information of each marker bit comprises:
and respectively inputting the mark position information matched with each map to be detected into a position clustering model to obtain the target position information of each mark position, wherein the position clustering model is a machine learning model.
3. The method according to claim 1, wherein the determining the map accuracy of the map to be tested based on the marker position information and the target position information corresponding to the same marker bit comprises:
determining a position error of each marker bit based on marker position information corresponding to the same marker bit and the target position information;
and determining the map precision of the map to be tested based on the position error of each marker bit.
4. The method of claim 3, wherein determining the position error of each flag bit based on the flag position information and the target position information corresponding to the same flag bit comprises:
and determining the coordinate difference between the mark position information corresponding to the same mark bit and the target position information as the position error of each mark bit.
5. The method of claim 3, wherein the determining the map accuracy of the map under test based on the position error of each marker bit comprises:
carrying out mean value processing on the position errors of the marker positions to obtain the map precision of the map to be measured; or,
and determining the map precision of the map to be measured under the condition that the square sum of the position errors of the marker bits is minimum.
6. The method of claim 1, wherein the map under test comprises an autonomous driving map or an assisted driving map.
7. A map accuracy testing apparatus, comprising:
the information extraction module is used for respectively extracting the mark position information of each map to be detected for a plurality of maps to be detected;
the information clustering module is used for respectively clustering the mark position information matched in each map to be tested to obtain the target position information of each mark position;
and the map precision determining module is used for determining the map precision of the map to be tested based on the mark position information corresponding to the same mark position and the target position information when any map to be tested is subjected to precision testing.
8. The apparatus of claim 7, wherein the information clustering module is further configured to:
and respectively inputting the mark position information matched with each map to be tested into a position clustering model to obtain the target position information of each mark position, wherein the position clustering model is a machine learning model.
9. An electronic device, characterized in that the electronic device comprises:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the map accuracy testing method of any one of claims 1-6.
10. A computer-readable storage medium storing computer instructions for causing a processor to perform the map accuracy testing method of any one of claims 1-6 when executed.
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