High-precision map detection method, device, equipment and storage medium

Abstract

The present disclosure provides a detection method, device, equipment and storage medium for high-precision maps, which relates to the field of data processing technology, especially to the technical fields of computer vision, autonomous driving, intelligent transportation, etc. The specific implementation scheme is: determine the first map element in the acquired image data; use the acquired point cloud data to determine the coordinate information of the first map element; compare the coordinate information of the first map element with the coordinate information of the second map element in the map to be detected, and use the comparison result as the map detection result. It can overcome the need for manual detection of map changes in related technologies. The scheme of the present disclosure can realize automatic detection of map changes by fusing point cloud data and image data. Furthermore, the map change elements and the coordinates of the changed elements can be output, thereby improving the efficiency of map detection and updating.

Description

Technical Field

[0001] The present disclosure relates to the field of data processing technology, in particular to the technical fields of computer vision, autonomous driving, intelligent transportation, and more particularly to a method, device, equipment, and storage medium for drawing a map. Background Art

[0002] High-precision maps, also known as high-accuracy maps, are used by self-driving cars. High-precision maps have accurate vehicle location information and rich road element data information, which can help cars predict complex road information, such as slope, curvature, heading, etc., to better avoid potential risks. For the maintenance of high-precision maps, it is very important to be able to detect changes in road information in the real world and update the map in a timely manner. Related technologies mainly focus on map reality change detection, which relies on the discovery of map change intelligence points, lacks the technical process of rebuilding new elements, requires operators to mark, and has low efficiency. Summary of the invention

[0003] The present invention provides a method, device, equipment and storage medium for detecting a high-precision map.

[0004] According to one aspect of the present disclosure, a method for detecting a high-precision map is provided, and the method may include the following steps:

[0005] Determining a first map element in the acquired image data;

[0006] Determine the coordinate information of the first map element using the acquired point cloud data;

[0007] The coordinate information of the first map element is compared with the coordinate information of the second map element in the map to be detected, and the comparison result is used as the map detection result.

[0008] According to another aspect of the present disclosure, a high-precision map detection device is provided, which may include:

[0009] A first map element determination module, used to determine a first map element in the acquired image data;

[0010] A coordinate information generating module, used to determine the coordinate information of the first map element using the acquired point cloud data;

[0011] The difference comparison module is used to compare the coordinate information of the first map element with the coordinate information of the second map element in the map to be detected, and use the comparison result as the map detection result.

[0012] According to another aspect of the present disclosure, there is provided an electronic device, comprising:

[0013] at least one processor; and

[0014] a memory communicatively connected to the at least one processor; wherein,

[0015] The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the method in any embodiment of the present disclosure.

[0016] According to another aspect of the present disclosure, a non-transitory computer-readable storage medium storing computer instructions is provided. The computer instructions are used to enable a computer to execute the method in any embodiment of the present disclosure.

[0017] According to another aspect of the present disclosure, a computer program product is provided, including a computer program / instruction, which implements the method in any embodiment of the present disclosure when the computer program / instruction is executed by a processor.

[0018] The technology disclosed in the present invention can overcome the need for manual detection of map changes in related technologies, and can achieve automatic detection of map changes by fusing point cloud data and image data. Furthermore, map change elements and the coordinates of the changed elements can be output, thereby improving the efficiency of map detection and updating.

[0019] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present disclosure, nor is it intended to limit the scope of the present disclosure. Other features of the present disclosure will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The accompanying drawings are used to better understand the present solution and do not constitute a limitation of the present disclosure.

[0021] Figure 1 is a flow chart of a detection method for a high-precision map according to the present disclosure;

[0022] Figure 2 is a flow chart for determining a first map element according to the present disclosure;

[0023] Figure 3 is a schematic diagram of identifying a first map element in multiple frames of image data according to the present disclosure;

[0024] Figure 4 is a flow chart for processing image data according to the present disclosure;

[0025] Figure 5 is a flow chart for determining coordinate information of a first map element according to the present disclosure;

[0026] Figure 6 is a flow chart for executing an adjustment strategy according to the present disclosure;

[0027] Figure 7 is a schematic diagram of a detection device for a high-precision map according to the present disclosure;

[0028] Figure 8 It is a block diagram of an electronic device used to implement the high-precision map detection method of the embodiment of the present disclosure. DETAILED DESCRIPTION

[0029] The following is a description of exemplary embodiments of the present disclosure in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. Similarly, for the sake of clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.

[0030] like Figure 1 As shown, the present disclosure relates to a method for detecting a high-precision map, which may include the following steps:

[0031] S101: Determine a first map element in the acquired image data;

[0032] S102: Determine coordinate information of a first map element using the acquired point cloud data;

[0033] S103: performing a difference comparison between the coordinate information of the first map element and the coordinate information of the second map element in the map to be detected, and using the comparison result as the map detection result.

[0034] The executor of the above-mentioned solution of the present disclosure may be a server of a map application, or may be a vehicle with an autonomous driving function, and a cloud that communicates with the vehicle, etc.

[0035] The acquired image data may be image data acquired in real time by an image acquisition device such as a vehicle-mounted camera; or, the acquired image data may be image data acquired by different vehicles or different staff members.

[0036] By performing semantic segmentation processing on the acquired image data, the first map element in the image data can be determined and marked. Exemplarily, the first map element may include a traffic sign. For example, the traffic sign may include a traffic sign line, a signal light, or a traffic sign board. Further, the traffic sign line may include a solid line, a dotted line, a turn arrow, etc. Traffic signs may include road signs, prohibition signs, warning signs, etc.

[0037] Point cloud data can be acquired by collecting through a three-dimensional laser sensor. The point cloud data and the aforementioned image data may include single-frame data or (continuous) multi-frame data. A high-precision semantic map can be obtained by fusing the point cloud data and the image data. The fusion process may include complementing the 3D coordinates in the point cloud data with the 2D coordinates of the object to be annotated in the image data to generate a semantic map; or, the 3D coordinates in the point cloud data may be projected into the image data to generate semantic high-precision map data.

[0038] In the high-precision semantic map, the coordinate information of the first map element can be obtained. The coordinate information can be the coordinate information corresponding to the midpoint of the first map element, or the coordinate information of multiple vertices of the detection box of the first map element. The coordinate information can be presented in the form of 3D coordinates.

[0039] The map to be detected may include a map to be updated or upgraded, etc. The map to be detected may be a high-precision map in the same area as the image data. By using techniques such as semantic segmentation processing, the second map element and the coordinates of the second map element may be determined in the map to be detected.

[0040] The difference comparison may include determining whether the first map element is a newly added map element; or, the difference comparison may include determining whether the second map element in the map to be detected is a removed map element; or, the difference comparison may also include determining whether the coordinates of the first map element are offset compared to the coordinates of the second map element.

[0041] The result of the above difference comparison can be used as a map detection result, and the above detection result can be used to determine whether the map to be detected needs to be updated.

[0042] Through the above solution, the problem of related technologies requiring manual detection of map changes can be overcome. The solution disclosed in the present invention can achieve automatic detection of map changes by fusing point cloud data and image data. Furthermore, map change elements and the coordinates of the changed elements can be output, thereby improving the efficiency of map detection and updating.

[0043] like Figure 2 As shown, in one implementation, when the image data includes multiple frames, step S101 may specifically include the following steps:

[0044] S201: Identify the first map element in each frame of image data;

[0045] S202: Acquire a detection frame of a first map element in each frame of image data;

[0046] S203: When the overlap rate of the detection frames of the first map elements in two adjacent frames of image data is greater than a corresponding threshold, the first map elements in the two adjacent frames of image data are determined to be the same first map elements.

[0047] When the image acquisition device is in real-time motion, the detection frames of the first map element in the multiple frames of image data may have position differences. For example, the position of the first map element in the first frame of image is x1, and the position in the second frame of image is x2, etc. The above x1 and x2 can represent the coordinates of the midpoint of the first map element in the corresponding image data.

[0048] Take the traffic light as an example. Figure 3 As shown, when the image data includes multiple frames, the traffic lights in each frame of image data can be determined in sequence. The first map element can be determined by using the aforementioned semantic segmentation technology; in addition, it can also be determined by using a pre-trained recognition model.

[0049] The first map element can be marked in the image data by using a detection frame. According to the positions of each vertex of the detection frame, the position of the first map element in each frame of image data can be determined.

[0050] The positions of the first detection frames in two adjacent frames of image data are matched, and when the overlap rate of the detection frames is greater than a corresponding threshold, it can be determined that the first map elements determined in the two adjacent frames of image data are the same first map elements. For the same first map elements, unique identification information can be assigned.

[0051] Through the above process, the tracking of the first map element in the multi-frame image data can be achieved, thereby overcoming the missed detection caused by obstacles that may occur during single-frame image detection. In addition, since the first map element is a traffic sign, compared with real-time moving objects such as vehicles and pedestrians, the first map element is a static object. Therefore, the above multi-frame detection method can improve the accuracy of detection.

[0052] like Figure 4 As shown, in one embodiment, the following steps are also included:

[0053] S401: Evaluate the accuracy of the determined first map element to obtain an evaluation result;

[0054] S402: When the evaluation result is lower than the corresponding threshold, the image data including the first map element whose evaluation result is lower than the corresponding threshold is deleted.

[0055] The accuracy of the first map element can be determined by the aforementioned overlap rate. For example, when the overlap rate of the detection frame of the first map element in the current frame image data and the detection frame in the previous frame image data is less than the corresponding threshold, and the overlap rate of the detection frame of the first map element in the current frame image data and the detection frame in the next frame image data is less than the corresponding threshold, it can be obtained that the accuracy evaluation result of the first map element in the current frame image data is lower than the corresponding threshold.

[0056] Alternatively, the accuracy of the first map element may be determined by using the result output by a pre-trained recognition model. For example, when the recognition model output includes confidence information, the accuracy evaluation result of the first map element in the current frame image data is lower than the corresponding threshold.

[0057] Image data with evaluation results lower than the corresponding threshold value can be considered as image data with false detection, and can be deleted.

[0058] Through the above solution, the accuracy of the first map element detection can be improved.

[0059] like Figure 5 As shown, in one implementation, step S102 may specifically include:

[0060] S501: Determine the relative position relationship between the point cloud data and the image data;

[0061] S502: constructing a projection relationship between 3D pixel points in the point cloud data and 2D pixel points in the image data according to the relative position relationship;

[0062] S503: Projecting the point cloud data onto the image data according to the projection relationship to obtain a projection result;

[0063] S504: Determine the coordinate information of the first map element using the projection result.

[0064] The relative position relationship between the point cloud data and the image data can be calculated through the hardware posture relationship, internal parameters, external parameters and other parameters of the image acquisition device and the point cloud data acquisition device.

[0065] In the process of projecting the point cloud data to the image data, the relative position relationship and time dimension information can be used. For example, in general, the collection of point cloud data and image data can be achieved by an information collection vehicle. The point cloud data and image data collected at the same time can be recorded. Furthermore, the projection position of any 3D pixel point in the point cloud data at the same time projected to the 2D pixel point in the image data can be calculated, that is, the projection relationship between the 3D pixel point in the point cloud data and the 2D pixel point in the image data can be constructed.

[0066] In addition, when the point cloud data includes multiple frames, the multiple frames of point cloud data can be spliced ​​in advance. Splicing multiple frames of point cloud data can obtain point cloud data of a larger range or a larger area, such as point cloud data of a street or multiple streets. Splicing multiple frames of point cloud data can include coordinate recognition processing of objects in each frame of point cloud data, so as to match, superimpose and other processing of each object whose coordinate difference in each frame of point cloud data is within the allowable range, and finally achieve splicing of multiple frames of point cloud data to obtain a splicing processing result. Then, the projection position of any 3D pixel point in the spliced ​​point cloud data to the 2D pixel point in the image data can be calculated.

[0067] The coordinate information of the first map element may be calculated using the coincident pixel points after projection or the pixel points whose distance after projection is within the corresponding threshold range.

[0068] Through the above process, the point cloud data can be fused with the image data of the identified objects to be labeled to accurately determine a high-precision semantic map.

[0069] In one embodiment, the method may further include the following steps: when the comparison result exceeds a corresponding threshold, generating an adjustment strategy for the second map element in the map to be detected.

[0070] When the comparison result does not exceed the corresponding threshold, it can be indicated that there is no significant difference between the coordinate information of the first map element and the coordinate information of the second map element in the map to be detected. Based on this, it can be determined that there is no change in the map element.

[0071] When the comparison result exceeds the corresponding threshold, it can indicate that the map to be detected needs to be updated. That is, different adjustment strategies can be generated according to the specific differences. Alternatively, the corresponding adjustment strategy can be generated by default.

[0072] For example, the adjustment strategy may include correcting the map to be inspected based on the coordinate information of the first map element. Alternatively, the adjustment strategy may include displaying the changed map element to prompt relevant staff to perform follow-up work. Alternatively, the adjustment strategy may also include sending the changed map element to a server for executing map element changes of a high-precision map.

[0073] like Figure 6 As shown, in one embodiment, executing the adjustment strategy includes:

[0074] S601: Generate name information of the first map element whose comparison result exceeds the corresponding threshold;

[0075] S602: Display the name information and the coordinate information of the first map element whose comparison result exceeds the corresponding threshold.

[0076] The name information of the first map element may be the identifier assigned to different first map elements in the aforementioned process, or the name information of the first map element may also be the name of a traffic sign determined by image recognition technology.

[0077] The adjustment strategy may include: displaying the name information of the first map element whose comparison result exceeds the corresponding threshold, and the coordinate information of the corresponding first map element. The relevant staff only needs to confirm the information and adjust and repair the map to be tested, and then the repaired map can be put back online. In this way, the efficiency of map operation can be improved.

[0078] like Figure 7 As shown, the present disclosure relates to a high-precision map detection device, which may include:

[0079] A first map element determination module 701, used to determine a first map element in the acquired image data;

[0080] A coordinate information generating module 702 is used to determine the coordinate information of the first map element using the acquired point cloud data;

[0081] The difference comparison module 703 is used to compare the coordinate information of the first map element with the coordinate information of the second map element in the map to be detected, and use the comparison result as the map detection result.

[0082] In one implementation, when the image data includes multiple frames, the first map element determination module 701 may further include:

[0083] A first map element recognition submodule, used to recognize a first map element in each frame of image data;

[0084] A detection frame acquisition submodule, used to acquire a detection frame of the first map element in each frame of image data;

[0085] The first map element determination execution submodule is used to determine the first map elements in two adjacent frames of image data as the same first map elements when the overlap rate of the detection frames of the first map elements in two adjacent frames of image data is greater than a corresponding threshold.

[0086] In one embodiment, the high-precision map detection device further includes:

[0087] An accuracy evaluation module, used to evaluate the accuracy of the determined first map element to obtain an evaluation result;

[0088] The deleting module is used to delete the image data of the first map element whose evaluation result is lower than the corresponding threshold value when the evaluation result is lower than the corresponding threshold value.

[0089] In one implementation, the coordinate information generating module 702 may further include:

[0090] A relative position relationship determination submodule is used to determine the relative position relationship between point cloud data and image data;

[0091] The projection relationship determination submodule is used to construct the projection relationship between the 3D pixel points in the point cloud data and the 2D pixel points in the image data according to the relative position relationship;

[0092] A projection result generation module is used to project the point cloud data onto the image data according to the projection relationship to obtain the projection result;

[0093] The coordinate information determination execution submodule is used to determine the coordinate information of the first map element using the projection result.

[0094] In one embodiment, the high-precision map detection device further includes:

[0095] The adjustment strategy generation module is used to generate an adjustment strategy for the second map element in the map to be detected when the comparison result exceeds the corresponding threshold.

[0096] In one embodiment, the adjustment strategy generation module includes:

[0097] A name information generation submodule, used to generate name information of the first map element whose comparison result exceeds the corresponding threshold;

[0098] The display submodule is used to display the name information and the coordinate information of the first map element whose comparison result exceeds the corresponding threshold.

[0099] In the technical solution disclosed herein, the acquisition, storage and application of user personal information involved are in compliance with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0100] According to an embodiment of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium and a computer program product.

[0101] Figure 8A schematic block diagram of an example electronic device 800 that can be used to implement an embodiment of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or required herein.

[0102] like Figure 8 As shown, the device 800 includes a computing unit 801, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 802 or a computer program loaded from a storage unit 808 into a random access memory (RAM) 803. In the RAM 803, various programs and data required for the operation of the device 800 can also be stored. The computing unit 801, the ROM 802, and the RAM 803 are connected to each other via a bus 804. An input / output (I / O) interface 805 is also connected to the bus 804.

[0103] A number of components in the device 800 are connected to the I / O interface 805, including: an input unit 806, such as a keyboard, a mouse, etc.; an output unit 807, such as various types of displays, speakers, etc.; a storage unit 808, such as a disk, an optical disk, etc.; and a communication unit 809, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 809 allows the device 800 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0104] The computing unit 801 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 801 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 801 performs the various methods and processes described above, such as the detection method of a high-precision map. For example, in some embodiments, the detection method of a high-precision map may be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as a storage unit 808. In some embodiments, part or all of the computer program may be loaded and / or installed on the device 800 via the ROM 802 and / or the communication unit 809. When the computer program is loaded into the RAM 803 and executed by the computing unit 801, one or more steps of the detection method of the high-precision map described above may be performed. Alternatively, in other embodiments, the computing unit 801 may be configured to perform the detection method of the high-precision map in any other appropriate manner (e.g., by means of firmware).

[0105] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0106] The program code for implementing the method of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0107] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, 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 disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0108] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the 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 can be received in any form (including acoustic input, voice input, or tactile input).

[0109] The systems and techniques described herein may be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), and the Internet.

[0110] A computer system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The relationship of client and server is generated by computer programs running on respective computers and having a client-server relationship with each other. The server may be a cloud server, a server of a distributed system, or a server combined with a blockchain.

[0111] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps recorded in this disclosure can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved, and this document does not limit this.

[0112] The above specific implementations do not constitute a limitation on the protection scope of the present disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present disclosure shall be included in the protection scope of the present disclosure.

Claims

1. A high-precision map detection method, comprising: Determining a first map element in the acquired image data; Determine the coordinate information of the first map element using the acquired point cloud data; Comparing the coordinate information of the first map element with the coordinate information of the second map element in the map to be detected, and using the comparison result as the map detection result, wherein the map to be detected is a map to be updated or upgraded; When the comparison result exceeds the corresponding threshold, generating an adjustment strategy for the second map element in the map to be detected; In the case where the image data includes multiple frames, determining the first map element in the acquired image data includes: identifying a first map element in each frame of the image data; Obtaining a detection frame of a first map element in each frame of the image data; When the overlap rate of the detection frames of the first map elements in the image data of two adjacent frames is greater than the corresponding threshold, determining the first map elements in the image data of the two adjacent frames as the same first map elements; Determining the accuracy of the first map element according to the overlap rate; When the accuracy is lower than the corresponding threshold, the image data including the first map element whose accuracy is lower than the corresponding threshold is deleted.

2. The method according to claim 1, wherein the step of generating the coordinate information of the first map element by using the acquired point cloud data comprises: Determining a relative positional relationship between the point cloud data and the image data; Constructing a projection relationship between 3D pixel points in the point cloud data and 2D pixel points in the image data according to the relative position relationship; According to the projection relationship, the point cloud data is projected onto the image data to obtain a projection result; The coordinate information of the first map element is determined using the projection result.

3. The method according to claim 1, executing the adjustment strategy comprises: Generate name information of the first map element whose comparison result exceeds the corresponding threshold; The name information and the coordinate information of the first map element whose comparison result exceeds the corresponding threshold are displayed.

4. A high-precision map detection device, comprising: A first map element determination module, used to determine a first map element in the acquired image data; A coordinate information generating module, used to determine the coordinate information of the first map element using the acquired point cloud data; A difference comparison module, used for comparing the coordinate information of the first map element with the coordinate information of the second map element in the map to be detected, and using the comparison result as the map detection result, wherein the map to be detected is a map to be updated or upgraded; An adjustment strategy generating module, used for generating an adjustment strategy for the second map element in the map to be detected when the comparison result exceeds the corresponding threshold; In the case where the image data includes multiple frames, the first map element determination module includes: A first map element recognition submodule, used for recognizing a first map element in each frame of the image data; A detection frame acquisition submodule, used to acquire a detection frame of the first map element in each frame of the image data; A first map element determination execution submodule, configured to determine the first map elements in the two adjacent frames of image data as the same first map element when the overlap rate of the detection frames of the first map elements in the two adjacent frames of image data is greater than a corresponding threshold; an accuracy evaluation module, used to determine the accuracy of the first map element according to the overlap rate; The deleting module is used to delete the image data of the first map element whose evaluation result is lower than the corresponding threshold value when the accuracy is lower than the corresponding threshold value.

5. The device according to claim 4, wherein: The coordinate information generating module comprises: A relative position relationship determination submodule, used to determine the relative position relationship between the point cloud data and the image data; A projection relationship determination submodule, used to construct a projection relationship between the 3D pixel points in the point cloud data and the 2D pixel points in the image data according to the relative position relationship; A projection result generating module, used for projecting the point cloud data onto the image data according to the projection relationship to obtain a projection result; The coordinate information determination execution submodule is used to determine the coordinate information of the first map element using the projection result.

6. The device according to claim 4, wherein: The adjustment strategy generation module includes: A name information generation submodule, used to generate name information of the first map element whose comparison result exceeds the corresponding threshold; The display submodule is used to display the name information and the coordinate information of the first map element whose comparison result exceeds the corresponding threshold.

7. An electronic device comprising: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 3.

8. A non-transitory computer-readable storage medium storing computer instructions, wherein: The computer instructions are used to cause the computer to execute the method according to any one of claims 1 to 3.

9. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instructions are executed by a processor, the steps of the method according to any one of claims 1 to 3 are implemented.

Images