Positioning method, electronic device, computer-readable storage medium, and positioning system

Abstract

The present application discloses a positioning method, an electronic device, a computer-readable storage medium and a positioning system. The method is applied to a positioning system, wherein the positioning system includes a drone and an image acquisition module. The method includes: obtaining a target image corresponding to a target area acquired by the image acquisition module, dividing the target image to obtain a plurality of regional sub-images; wherein each regional sub-image includes a calibration point and its corresponding first position information, and each regional sub-image matches a target sub-area of ​​the target area; based on the regional sub-image and the calibration points included therein, adjusting the drone to the corresponding target sub-area respectively; obtaining a regional reference image corresponding to the target sub-area acquired by the drone, and determining the second position information of the drone based on the regional sub-image and the regional reference image; and locating the target area based on the first position information and the second position information. The above scheme can realize efficient and fast positioning data acquisition and matching, and save costs.

Description

Technical Field

[0001] The present application relates to the field of camera technology, and in particular to a positioning method, electronic equipment, computer-readable storage medium and positioning system. Background Art

[0002] With the rapid development of intelligent technology, cameras, as a key visual data entry, have put forward higher requirements for full-scene data collection. For large-scale image collection in large scenes, it is necessary to combine clear and accurate location information in order to quickly and accurately analyze images in a large range. At present, the conventional solution for obtaining accurate location information in a large range is still to perform manual calibration and positioning on the spot, but this calibration and positioning solution is not only time-consuming and labor-intensive, but also has high labor costs. Therefore, how to achieve efficient and fast positioning data collection and matching, and save costs has become an urgent problem to be solved. Summary of the invention

[0003] The main technical problem solved by the present application is to provide a positioning method, an electronic device, a computer-readable storage medium and a positioning system, which can realize efficient and fast positioning data collection and matching and save costs.

[0004] To solve the above technical problems, the first aspect of the present application provides a positioning method, which is applied to a positioning system, wherein the positioning system includes a drone and an image acquisition module, and the method includes: obtaining a target image corresponding to a target area acquired by the image acquisition module, dividing the target image to obtain a plurality of regional sub-images; wherein each of the regional sub-images includes a calibration point and its corresponding first position information, and each of the regional sub-images matches a target sub-area of ​​the target area; based on the regional sub-images and the calibration points included therein, adjusting the drones to the corresponding target sub-areas respectively; obtaining a regional reference image corresponding to the target sub-area acquired by the drone, and determining the second position information of the drone based on the regional sub-image and the regional reference image; and locating the target area based on the first position information and the second position information.

[0005] To solve the above technical problem, the second aspect of the present application provides an electronic device, comprising a memory and a processor coupled to each other, wherein the memory stores program instructions, and the processor is used to execute the program instructions to implement the method described in the first aspect.

[0006] In order to solve the above technical problem, the third aspect of the present application provides a computer-readable storage medium on which program data is stored. When the program data is executed by a processor, the method described in the first aspect is implemented.

[0007] To solve the above technical problems, the fourth aspect of the present application provides a positioning system, including an image acquisition module and a drone, and an electronic device as described in the second aspect above, and the electronic device is coupled to the image acquisition module and the drone.

[0008] In the above scheme, after the image acquisition module is aligned with the target area, the target area is acquired by using the image acquisition module to obtain a target image corresponding to the target area, the target image is divided to obtain multiple regional sub-images, each regional sub-image contains a calibration point and its corresponding first position information, and each regional sub-image matches the target sub-area of ​​the target area, based on each regional sub-image and the calibration points included therein, the drone is adjusted to the corresponding target sub-area, and the regional reference image corresponding to the target sub-area acquired by the drone is obtained, the second position information of the drone is determined according to the regional sub-image and the regional reference image, and the target area is located based on the first position information corresponding to the calibration point and the second position information of the drone, and by combining the image acquisition module with the drone, efficient and fast positioning data acquisition and matching can be achieved, and costs can be saved. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. Among them:

[0010] Figure 1 It is a flowchart of an implementation method of the present invention;

[0011] Figure 2 It is a flowchart of another implementation method of the positioning method of the present application;

[0012] Figure 3 It is a structural schematic diagram of an embodiment of the electronic device of the present application;

[0013] Figure 4 It is a schematic diagram of the structure of an embodiment of a computer-readable storage medium of the present application;

[0014] Figure 5 It is a structural schematic diagram of an implementation method of the positioning system of the present application. DETAILED DESCRIPTION

[0015] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments, and different implementation methods can be adaptively combined. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0016] The terms "system" and "network" are often used interchangeably in this article. The term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship. In addition, "many" in this article means two or more than two.

[0017] See also Figure 1 , Figure 1 1 is a flow chart of an implementation method of the present invention. The method is applied to a positioning system, which includes a drone and an image acquisition module, wherein the positioning method includes:

[0018] S101: Obtain a target image corresponding to a target area captured by an image acquisition module, divide the target image, and obtain a plurality of regional sub-images; wherein each regional sub-image includes a calibration point and its corresponding first position information, and each regional sub-image matches a target sub-area of ​​the target area.

[0019] Specifically, after the image acquisition module is aligned with the target area, the target area is captured using the image acquisition module to obtain a target image corresponding to the target area, and the target image is divided to obtain multiple regional sub-images, each of which contains a calibration point and its corresponding first position information, and each regional sub-image matches the target sub-area of ​​the target area.

[0020] It can be understood that the first position information corresponding to the calibration point is pixel point position information.

[0021] In one application, after obtaining a target image corresponding to a target area captured by an image acquisition module, the target image is divided according to a plurality of pre-set calibration points to obtain a plurality of regional sub-images.

[0022] In an application scenario, the pre-set multiple calibration points are the center points of each regional sub-image.

[0023] In another application mode, after obtaining the target image corresponding to the target area captured by the image acquisition module, image features in each target image are extracted, and the target image is divided according to the image features to obtain multiple regional sub-images.

[0024] In some implementation scenarios, after the image acquisition module is aimed at the target area, the video captured by the image acquisition module in real time is obtained, and the key frames in the video are used as the target images.

[0025] In some implementation scenarios, after the image acquisition module is aligned with the target area, reference images taken by the image acquisition module at preset time intervals are obtained, and the target image is determined from the multiple reference images.

[0026] Optionally, the number of calibration points is greater than or equal to 6, and is flexibly set according to actual conditions. This application does not impose any specific restrictions on this.

[0027] It should be noted that in the process of selecting calibration points, interference factors, such as interference features such as the sky, need to be avoided.

[0028] S102: Based on the regional sub-image and the calibration points included therein, the drones are adjusted to the corresponding target sub-regions.

[0029] Specifically, based on each regional sub-image and the calibration points included therein, the drones are adjusted to the corresponding target sub-regions.

[0030] In one application, each regional sub-image and the calibration points included therein are subjected to data translation to generate a control instruction, which is then sent to the drone so that the drone moves to the corresponding target sub-region according to the control instruction.

[0031] In another application, a planned route is generated based on each regional sub-image and the calibration points included therein, and the planned route is sent to the drone so that the drone moves to the corresponding target sub-region according to the planned route.

[0032] It should be noted that the calibration points are usually based on the ground as a reference surface, with no obstacles around and at a certain height from the ground. The drone flies to the calibration points on the sub-image of the corresponding area of ​​the target sub-area.

[0033] In one application scenario, the ground is used as a reference surface, and the center point of each regional sub-image is used as a calibration point. After data translation of each regional sub-image and its included center point, a control instruction is generated, and the control instruction is sent to the drone so that the drone flies to the center point on the regional sub-image corresponding to the target sub-region according to the control instruction.

[0034] In another application scenario, the ground is used as a reference surface, and features such as high-rise buildings in each regional sub-image are used as reference objects. Points at a certain threshold distance from the reference objects are used as calibration points. After a planned route is generated based on each regional sub-image and the calibration points it includes, the planned route is sent to the drone so that the drone can fly to a point at a certain threshold distance from the reference object on the regional sub-image corresponding to the target sub-region according to the planned route.

[0035] S103: Acquire a regional reference image corresponding to the target sub-region captured by the drone, and determine second position information of the drone based on the regional sub-image and the regional reference image.

[0036] Specifically, a regional reference image corresponding to the target sub-region collected by the drone is obtained, and the second position information of the drone is determined according to the regional sub-image and the regional reference image.

[0037] It can be understood that the second location information of the drone is the map location information of the drone.

[0038] In one application, after obtaining a regional reference image corresponding to a target sub-region captured by a drone, image features in the regional sub-image and image features in the regional reference image are extracted, and after accurately analyzing and comparing these image features, the second position information of the drone is determined.

[0039] In one application scenario, the similarity between the image features in the regional sub-image and the image features in the regional reference image is calculated for analysis and comparison.

[0040] In another application method, after obtaining the regional reference image corresponding to the target sub-region captured by the drone, the regional sub-image and the regional reference portrait are respectively compared with the pre-obtained template image to obtain corresponding comparison values. When the comparison value meets the preset conditions, the second position information of the drone is determined.

[0041] S104: Positioning the target area based on the first position information and the second position information.

[0042] Specifically, the target area is located based on the first position information corresponding to the calibration point and the second position information of the drone.

[0043] In one application, an association relationship is obtained based on the first position information corresponding to the calibration point and the second position information of the drone, and the target area is positioned according to the obtained association relationship.

[0044] In another application, the first position information corresponding to the calibration point and the second position information of the drone are input into a pre-trained deep learning model, and a plurality of correlation results are obtained using the deep learning model, and the target area is located according to these correlation results.

[0045] It can be understood that after associating the pixel point position information corresponding to the calibration point and the map position information corresponding to the drone, an association relationship is obtained, and then the positioning result between the regional sub-image of the target image captured by the image acquisition module and the target sub-region of the target area is obtained based on these association relationships. The positioning result includes the accurate positioning between the pixel point position information corresponding to the calibration point of each regional sub-image and the map position information corresponding to the drone.

[0046] Optionally, the map location information corresponding to the drone includes the latitude, longitude and altitude of the drone.

[0047] In the above scheme, after the image acquisition module is aligned with the target area, the target area is acquired by using the image acquisition module to obtain a target image corresponding to the target area, the target image is divided to obtain multiple regional sub-images, each regional sub-image contains a calibration point and its corresponding first position information, and each regional sub-image matches the target sub-area of ​​the target area, based on each regional sub-image and the calibration points included therein, the drone is adjusted to the corresponding target sub-area, and the regional reference image corresponding to the target sub-area acquired by the drone is obtained, the second position information of the drone is determined according to the regional sub-image and the regional reference image, and the target area is located based on the first position information corresponding to the calibration point and the second position information of the drone, and by combining the image acquisition module with the drone, efficient and fast positioning data acquisition and matching can be achieved, and costs can be saved.

[0048] See also Figure 2 , Figure 2 : is a flow chart of another embodiment of the positioning method of the present application. The method is applied to a positioning system, which includes a drone and an image acquisition module, wherein the positioning method includes:

[0049] S201: Acquire a target image corresponding to a target area acquired by an image acquisition module.

[0050] Specifically, after the image acquisition module is aligned with the target area, the target area is captured by the image acquisition module to obtain a target image corresponding to the target area.

[0051] In one implementation scenario, the image acquisition module includes multiple cameras facing different angles, and these cameras capture multiple target images within a preset angle range.

[0052] In another implementation scenario, in order to reduce costs, the image acquisition module includes at least one camera equipped with a pan-tilt head, and the camera is rotated by the pan-tilt head so that the camera captures multiple target images within a preset angle range.

[0053] S202: Acquire the matching calibration points based on a preset number of divisions.

[0054] Specifically, based on the preset number of divisions, calibration points matching the number of divisions are acquired.

[0055] Optionally, the number of calibration points is the same as the number of divisions, and the number of calibration points is greater than or equal to 6. It is flexibly set according to actual conditions, and this application does not impose specific restrictions on this.

[0056] It should be noted that the calibration point is usually a point at a certain height with the ground as a reference surface. Generally, the center point of the regional sub-image is taken as the calibration point, and the drone flies to the calibration point.

[0057] S203: Divide the target image based on the multiple calibration points to obtain multiple regional sub-images.

[0058] Specifically, the target image is divided based on multiple calibration points to obtain multiple regional sub-images matching the target sub-regions. By dividing the target image into multiple regional sub-images, it is convenient to analyze and extract local features, thereby improving the accuracy of subsequent positioning.

[0059] Optionally, in order to increase the image processing rate, the target image may be evenly divided.

[0060] S204: Generate a control instruction based on the regional sub-image and the calibration points included therein.

[0061] Specifically, after data translation is performed on the regional sub-image and the calibration points included therein, a corresponding control instruction is generated.

[0062] S205: Sending a control instruction to the drone, so that the drone moves to the target sub-area based on the control instruction.

[0063] Specifically, the control instructions are sent to the drone, so that the drone moves quickly and accurately to each target sub-area based on the obtained control instructions.

[0064] In one implementation scenario, the image acquisition module also includes a magnification unit, and step S205 may also include: using the magnification unit to adjust the image acquisition module and then photographing the target sub-area to obtain multiple regional magnification images; based on the regional magnification images, determining that the drone moves to the target sub-area.

[0065] Specifically, the image acquisition module is adjusted using the zoom unit of the image acquisition module to shoot the target sub-area to obtain multiple regional zoom images. The movement of the drone to the target sub-area is determined based on the multiple regional zoom images obtained. By shooting the regional zoom images, it is further determined whether the drone has reached the designated target sub-area, thereby improving the accuracy of subsequent positioning.

[0066] Optionally, the magnification unit of the image acquisition module may be a zoom lens.

[0067] S206: Acquire a regional reference image corresponding to the target sub-region captured by the drone, and determine second position information of the drone based on the regional sub-image and the regional reference image.

[0068] Specifically, a regional reference image corresponding to the target sub-region collected by the drone is obtained, and the second position information of the drone is determined according to the regional sub-image and the regional reference image.

[0069] In one implementation scenario, the drone includes a collection device and a positioning device, and step S206 specifically includes: obtaining a regional reference image collected by the drone using the collection device; obtaining a first image feature of the regional sub-image, and obtaining a second image feature of the regional reference image; based on the first image feature and the second image feature, determining the second location information recorded by the drone using the positioning device.

[0070] Specifically, a regional reference image collected by the drone using an acquisition device is obtained, and the first image features of the regional sub-image and the second image features of the regional reference image are extracted respectively. Based on the first image features and the second image features, after determining that the drone has reached the target sub-area, the drone can quickly obtain the second position information through the positioning device and upload it to the positioning system.

[0071] In a specific implementation scenario, the acquisition device is the video system of the drone, the positioning device is a GPS positioning sensor, and the corresponding second location information is GPS information. In other implementation scenarios, the positioning device may also be other location sensors capable of obtaining location information. This application does not make specific limitations on this.

[0072] S207: Positioning the target area based on the first position information and the second position information.

[0073] Specifically, the target area is located based on the first position information corresponding to the calibration point and the second position information of the drone.

[0074] In one implementation scenario, step S207 specifically includes: based on the first position information and the second position information, associating the regional sub-image with the target sub-region to obtain a regional positioning result of the target sub-region; based on the regional positioning results of all target sub-regions, obtaining a positioning result of the target region.

[0075] Specifically, based on the first position information corresponding to the calibration point and the second position information of the drone, the regional sub-image is associated with the target sub-region to obtain the regional positioning result of each target sub-region, and based on the regional positioning results of all target sub-regions, the positioning of the image acquisition module and the target region is completed. By combining the camera with the drone, more efficient and quick positioning data collection and matching can be achieved, while saving costs.

[0076] In the above scheme, after the image acquisition module is aligned with the target area, the image acquisition module is used to acquire the target area to obtain a target image corresponding to the target area, the same number of calibration points are obtained based on a preset number of divisions, the target image is divided based on multiple calibration points, and multiple regional sub-images matching the target sub-areas are obtained. By dividing the target image into multiple regional sub-images, it is convenient to analyze and extract local features, thereby improving the accuracy of subsequent positioning. After the regional sub-images and the calibration points included therein are data translated, corresponding control instructions are generated, and the control instructions are issued to the drone, so that the drone can move quickly and accurately to each target sub-area based on the obtained control instructions, obtain the regional reference image corresponding to the target sub-area acquired by the drone, determine the second position information of the drone based on the regional sub-image and the regional reference image, and locate the target area based on the first position information corresponding to the calibration points and the second position information of the drone. By combining the camera with the drone, more efficient and fast positioning data acquisition and matching can be achieved, while saving costs.

[0077] See also Figure 3 , Figure 3: is a structural diagram of an embodiment of an electronic device of the present application, the electronic device 30 comprises a memory 300 and a processor 302 coupled to each other, wherein the memory 300 stores program instructions (not shown), and the processor 302 is used to execute the program instructions to implement the method in any of the above embodiments. For the description of the relevant content, please refer to the detailed description of the above method embodiment, which will not be repeated here. In addition, the processor 302 can also be called a CPU (Center Processing Unit). The processor 302 may be an integrated circuit chip with signal processing capabilities. The processor 302 may also be a general-purpose processor, a digital signal processor (Digital Signal Processor, DSP), an application-specific integrated circuit (Application Specific Integrated Circuit, ASIC), a field-programmable gate array (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. In addition, the processor 302 may be implemented by an integrated circuit chip.

[0078] See also Figure 4 , Figure 4 It is a structural diagram of an embodiment of a computer-readable storage medium of the present application. The computer-readable storage medium 40 stores program data 400. When the program data 400 is executed by a processor, the method in any of the above embodiments is implemented. For descriptions of related contents, please refer to the detailed description of the above method embodiments, which will not be repeated here.

[0079] See also Figure 5 , Figure 5 It is a structural diagram of an embodiment of a positioning system of the present application. The positioning system 50 includes an image acquisition module 500, a drone 502, and the electronic device 30 described above, and the electronic device 30 is coupled to the image acquisition module 500 and the drone 502.

[0080] It should be noted that the units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present implementation scheme.

[0081] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0082] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) or a processor to execute all or part of the steps of each implementation method of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, etc., and other media that can store program codes.

[0083] The above description is only an implementation method of the present application, and does not limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the present application specification and drawings, or directly or indirectly used in other related technical fields, are also included in the patent protection scope of the present application.

Claims

1. A positioning method, characterized in that: Applied to a positioning system, the positioning system includes a drone and an image acquisition module, and the method includes: Acquire a target image corresponding to the target area acquired by the image acquisition module, divide the target image to obtain a plurality of regional sub-images; wherein each of the regional sub-images includes a calibration point and its corresponding first position information, and each of the regional sub-images matches a target sub-area of ​​the target area, and the first position information is pixel point position information; wherein the image acquisition module includes a plurality of cameras facing different angles or at least one camera equipped with a pan / tilt head; Based on the regional sub-image and the calibration points included therein, the drones are respectively adjusted to the corresponding target sub-regions; the calibration points correspond to points with a height relative to a reference surface, and the drones are adjusted to the points of the calibration points; Acquire a regional reference image corresponding to the target sub-region collected by the drone, and determine, based on the regional sub-image and the regional reference image, after the drone has arrived at the target sub-region, determine second position information of the drone; wherein the second position information is map position information of the drone, and the map position information corresponding to the drone includes the latitude, longitude and elevation of the drone; Based on the first position information and the second position information, the pixel position information of the calibration point and the map position information corresponding to the drone are associated to obtain an associated relationship, and the target area is located based on the obtained associated relationship.

2. The method according to claim 1, characterized in that The step of acquiring a target image corresponding to a target area acquired by the image acquisition module and dividing the target image to obtain a plurality of regional sub-images includes: Acquire the target image corresponding to the target area acquired by the image acquisition module; Acquire the matching calibration points based on a preset number of divisions; The target image is divided based on a plurality of calibration points to obtain a plurality of the region sub-images.

3. The method according to claim 2, characterized in that The image acquisition module acquires a plurality of target images within a preset angle range.

4. The method according to claim 1, characterized in that: The adjusting the drones to the corresponding target sub-areas based on the regional sub-images and the calibration points included therein respectively includes: Generate a control instruction based on the regional sub-image and the calibration points included therein; The control instruction is sent to the drone, so that the drone moves to the target sub-area based on the control instruction.

5. The method according to claim 4, characterized in that The image acquisition module further includes a magnification unit, and after sending the control instruction to the drone so that the drone moves to the target sub-area based on the control instruction, it also includes: The target sub-region is photographed after the image acquisition module is adjusted by the zoom unit to obtain a plurality of regional zoom images; The drone is determined to move to the target sub-area based on the regional zoom image.

6. The method according to claim 1, characterized in that The drone includes a collection device and a positioning device, and the acquiring of a regional reference image corresponding to the target sub-region collected by the drone, and determining second position information of the drone based on the regional sub-image and the regional reference image, includes: Acquire the reference image of the area collected by the drone using the collection device; Acquire a first image feature of the regional sub-image, and acquire a second image feature of the regional reference image; Based on the first image feature and the second image feature, the second position information recorded by the drone using the positioning device is determined.

7. The method according to claim 1, characterized in that The positioning of the target area based on the first position information and the second position information includes: Based on the first position information and the second position information, the regional sub-image is associated with the target sub-region to obtain a regional positioning result of the target sub-region; Based on the region positioning results of all the target sub-regions, a positioning result of the target region is obtained.

8. An electronic device, characterized in that: The method comprises a memory and a processor coupled to each other, wherein the memory stores program instructions, and the processor is used to execute the program instructions to implement the method according to any one of claims 1 to 7.

9. A computer-readable storage medium, characterized in that: Program instructions that can be executed by a processor are stored, and the program instructions are used to implement the method according to any one of claims 1 to 7.

10. A positioning system, characterized in that: It comprises an image acquisition module and a drone, and an electronic device as described in claim 8, and the electronic device is coupled to the image acquisition module and the drone.

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