Virtual channel drawing method and device based on unmanned aerial vehicle aerial photograph and storage medium

Abstract

The application discloses a kind of based on virtual channel drawing method of unmanned aerial vehicle aerial photograph, computer device and storage medium, including obtaining the aerial photograph data that unmanned aerial vehicle is collected from actual channel, slice data is obtained by slicing to the big map obtained by merging aerial photograph data, according to slice data, draw virtual channel, track the pixel change information of feature point in aerial photograph data, pixel change information is deduced from pixel coordinate system to latitude and longitude coordinate system, obtain feature point position change information, according to feature point position change information, correct virtual channel and the like steps.The present application uses unmanned aerial vehicle to obtain high-precision actual scene layer, and drawing virtual channel under the layer can guarantee channel accuracy as far as possible, calibrate channel every time interval, reduce the error between virtual channel and actual channel as far as possible, improve the accuracy of yaw algorithm.The present application is widely applied in channel drawing technical field.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of channel mapping, and in particular to a virtual channel mapping method based on aerial photographs of unmanned aerial vehicles, a computer device and a storage medium. BACKGROUND

[0002] When a ship sails in a channel, it needs to avoid objects such as bridges to ensure the safety of sailing and the safety of buildings, thereby bringing the demand for active bridge collision avoidance. In the implementation process of active bridge collision avoidance, yaw detection needs to be realized. Specifically, the yaw detection is mostly realized by mapping a virtual channel on a map. However, different map manufacturers use different coordinate systems, and the influence of random algorithm offset and errors and other factors will lead to the lack of yaw accuracy, which limits the data accuracy used by the subsequent yaw algorithm. SUMMARY

[0003] In view of the technical problems of the lack of yaw accuracy and the influence on the implementation of active bridge collision avoidance, the present application aims to provide a virtual channel mapping method based on aerial photographs of unmanned aerial vehicles, a computer device and a storage medium.

[0004] In one aspect, the present application embodiment includes a virtual channel mapping method based on aerial photographs of unmanned aerial vehicles, comprising:

[0005] Obtaining aerial photograph data collected by an unmanned aerial vehicle from an actual channel;

[0006] Slicing a large map obtained by merging the aerial photograph data to obtain slice data;

[0007] Mapping a virtual channel according to the slice data;

[0008] Tracking pixel change information of feature points in the aerial photograph data;

[0009] Reversing the pixel change information from a pixel coordinate system to a latitude and longitude coordinate system to obtain feature point position change information;

[0010] Correcting the virtual channel according to the feature point position change information.

[0011] Further, the obtaining of the aerial photograph data collected by the unmanned aerial vehicle from the actual channel comprises:

[0012] Controlling the unmanned aerial vehicle to sail along the actual channel;

[0013] Controlling the unmanned aerial vehicle to take photographs of the actual channel to obtain the aerial photograph data;

[0014] Controlling the unmanned aerial vehicle to take photographs of feature points in the actual channel to obtain feature point data.

[0015] Further, the acquisition of aerial photograph data collected by the unmanned aerial vehicle from the actual channel further comprises:

[0016] The aerial photograph data is merged into a large map.

[0017] Further, the slicing of the large map obtained by merging the aerial photograph data to obtain slice data comprises:

[0018] Obtain the war package of GeoServer for deployment;

[0019] Access the GeoServer page, and slice the large map in the GeoServer page to obtain the slice data;

[0020] After slicing, distribute the WMTS service through GeoServer.

[0021] Further, the drawing of the virtual channel according to the slice data comprises:

[0022] The WMTS service loads the map tile through OpenLayers;

[0023] The virtual channel is drawn using the Polygon class in OpenLayers.

[0024] Further, the drawing of the virtual channel using the Polygon class in OpenLayers comprises:

[0025] Obtain the latitude and longitude data of the virtual channel;

[0026] Add the latitude and longitude data of the virtual channel to Polygon;

[0027] Add Polygon to Feature;

[0028] Add Feature to Vector;

[0029] Add Vector to the map instance Map to display the virtual channel.

[0030] Further, the tracking of the pixel change information of the feature point in the aerial photograph data comprises:

[0031] According to the position of the feature point in the aerial photograph data, determine the latitude and longitude coordinates of the feature point in the aerial photograph data;

[0032] Take a screenshot of the actual channel at regular intervals to obtain a screenshot containing the feature point;

[0033] determine pixel coordinates of the feature points in the screenshot;

[0034] establish a conversion relationship between the latitude-longitude coordinates and the pixel coordinates according to the latitude-longitude and pixel coordinates of the same feature points;

[0035] determine the pixel change information according to the latitude-longitude coordinates of the feature points in the aerial photograph data and the pixel coordinates of the feature points in the screenshot.

[0036] Further, the pixel change information is inversely deduced from the pixel coordinate system to the latitude-longitude coordinate system to obtain feature point position change information, including:

[0037] acquire a pixel-actual distance scale;

[0038] determine coordinate change in the latitude-longitude coordinate system according to the pixel change information and the pixel-actual distance scale;

[0039] acquire a distance from a camera center point of the unmanned aerial vehicle to a picture center target point;

[0040] determine the feature point position change information according to the coordinate change in the latitude-longitude coordinate system and the distance from the camera center point of the unmanned aerial vehicle to the picture center target point.

[0041] On the other hand, the embodiment of the present application also includes a computer device, including a memory and a processor, the memory is used to store at least one program, the processor is used to load the at least one program to execute the virtual channel plotting method based on unmanned aerial vehicle aerial photograph in the embodiment.

[0042] On the other hand, the embodiment of the present application also includes a storage medium, wherein the processor executable program is stored, the processor executable program is used to execute the virtual channel plotting method based on unmanned aerial vehicle aerial photograph in the embodiment when being executed by the processor.

[0043] The beneficial effects of the present application are: the virtual channel plotting method based on unmanned aerial vehicle aerial photograph in the embodiment, the actual scene layer of high precision is obtained by using unmanned aerial vehicle, and the virtual channel plotted under the layer can guarantee the accuracy of the channel as far as possible, the channel is calibrated once every period of time, the error between the virtual channel and the actual channel is reduced as far as possible, and the accuracy of the drift algorithm is improved. BRIEF DESCRIPTION OF DRAWINGS

[0044] Figure 1 The schematic diagram for each step of the virtual channel plotting method based on unmanned aerial vehicle aerial photograph in the embodiment is shown.

[0045] Figure 2 The mapping relationship between the latitude-longitude coordinate and the pixel coordinate system in the embodiment is shown.

[0046] Figure 3 A schematic diagram for the principle of the step of inversely deducing the pixel change information from the pixel coordinate system to the latitude and longitude coordinate system in the embodiment. DETAILED DESCRIPTION

[0047] In the embodiment, the virtual channel drawing method based on the aerial photograph of the unmanned aerial vehicle includes the following steps: Figure 1

[0048] S1. Obtain the aerial photograph data collected by the unmanned aerial vehicle from the actual channel;

[0049] S2. Slice the large image obtained by merging the aerial photograph data to obtain slice data;

[0050] S3. Draw a virtual channel according to the slice data;

[0051] S4. Track the pixel change information of the feature points in the aerial photograph data;

[0052] S5. Invert the pixel change information from the pixel coordinate system to the latitude and longitude coordinate system to obtain the position change information of the feature points;

[0053] S6. Correct the virtual channel according to the position change information of the feature points.

[0054] In the embodiment, when performing step S1, that is, obtaining the aerial photograph data collected by the unmanned aerial vehicle from the actual channel, the following steps can be performed:

[0055] S101. Control the unmanned aerial vehicle to sail along the actual channel;

[0056] S102. Control the unmanned aerial vehicle to take pictures of the actual channel to obtain aerial photograph data;

[0057] S103. Control the unmanned aerial vehicle to take pictures of the feature points in the actual channel to obtain feature point data;

[0058] S104. Merge each aerial photograph data into a large image.

[0059] When performing step S101, the unmanned aerial vehicle coordinate system can select the WGS-84 coordinate system. When sailing along the channel, the unmanned aerial vehicle collects channel data, uses navigation marks and specific feature buildings and things as feature points, and collects the latitude and longitude coordinates of the feature points.

[0060] In step S102, the feature points in the actual channel can be the positions where the buoys are placed, and the landmark buildings such as wharfs.

[0061] ​In step S103, the feature point data can be the latitude and longitude coordinates representing the location of feature points such as buoys and docks.

[0062] In this embodiment, the actual waterway can be identified by a latitude and longitude coordinate system. For example, the position of each point in the actual waterway can be represented by latitude and longitude coordinates in the latitude and longitude coordinate system. The camera screenshot can also be identified by a latitude and longitude coordinate system. For example, each pixel in the camera screenshot has a corresponding latitude and longitude coordinate.

[0063] In this embodiment, the mapping relationship between latitude and longitude coordinates and pixel coordinate system is as follows: Figure 2 As shown. (Refer to...) Figure 2 , abcd is a latitude and longitude coordinate system surface, ABCD is a pixel coordinate system surface, points in abcd can be mapped to ABCD, and points in ABCD can also be mapped to abcd.

[0064] If multiple aerial images are captured in step S102, in step S103, software such as DJI Terra can be used to combine the multiple aerial images captured by the drone into a single large image, and then export the TIF image for later use.

[0065] In this embodiment, when performing step S2, which involves slicing the large image obtained by merging aerial image data to obtain slice data, the following steps can be specifically performed:

[0066] S201. Obtain the GeoServer WAR package and deploy it;

[0067] S202. Access the GeoServer page, slice the large image on the GeoServer page, and obtain the slice data;

[0068] S203. After successful slicing, WMTS services are distributed via GeoServer.

[0069] When performing steps S201-S203, you can download the GeoServer WAR package from the official website for deployment. After successful deployment, start the service and access the GeoServer page through the IP provided by the service. On the page, you can perform slicing, service services, etc. The GeoServer slicing process is as follows: Click "Create New Workspace" to create a workspace. After the workspace is successfully created, create a new data source and import the TIF image obtained in step S103 for slicing. After successful slicing, publish the WMTS service through GeoServer.

[0070] In this embodiment, when performing step S3, which is to draw the virtual waterway based on the slice data, the following steps can be performed:

[0071] S301. The WMTS service loads map tiles through OpenLayers;

[0072] S302. The virtual channel is drawn using the Polygon class in OpenLayers.

[0073] After the WMTS service is published in step S203, the page loads map tiles through OpenLayers. Map tiles are a series of pictures with latitude and longitude. The essence of loading map tiles is to request pictures and render the pictures to the page. When OpenLayers requests map tiles through the HTTP protocol, a series of parameters need to be transmitted, including layer (layer name), tilematrixset (coordinate system number), Service (service type), Request (request type), Version (version), Format (slice file type), TileMatrix (current slice level), TileCol (sliced x), and TileRow (sliced y). Through the above parameters, the corresponding map tiles can be obtained. After the page loads tile data, the Polygon class in OpenLayers is used to draw the channel.

[0074] In this embodiment, when step S302, that is, the step of drawing a virtual channel using the Polygon class in OpenLayers, is performed, the following steps can be performed:

[0075] S30201. Obtain latitude and longitude data of the virtual channel;

[0076] S30202. Add the latitude and longitude data of the virtual channel to Polygon;

[0077] S30203. Add Polygon to Feature;

[0078] S30204. Add Feature to Vector;

[0079] S30205. Add Vector to the map instance Map to display the virtual channel.

[0080] When steps S30201-S30205 are executed, the channel flow is drawn as follows: first, query whether the virtual channel corresponding to the actual channel has latitude and longitude data through HTTP, if the virtual channel has latitude and longitude data, directly add the data to the Polygon class, if not, obtain the data of the virtual channel by clicking the map with the mouse, then add the data to the Polygon, the next step is to add the Polygon to the Feature, Feature is a vector object of geographic elements with geometric characteristics and other attribute characteristics, similar to the elements in the vector file format. After constructing the Feature, the Feature is added to the Vector, which is a vector layer composed of elements. Finally, the Vector is added to the map instance Map to display the virtual channel.

[0081] In this embodiment, when step S4, that is, tracking the pixel change information of the feature points in the aerial photograph data, is executed, the following steps can be executed:

[0082] S401. Determine the latitude and longitude coordinates of the feature points in the aerial photograph data according to the positions of the feature points in the aerial photograph data.

[0083] S402. Take a camera screenshot of the actual channel at regular intervals to obtain a screenshot containing the feature points.

[0084] S403. Determine the pixel coordinates of the feature points in the screenshot.

[0085] S404. Establish a conversion relationship between the latitude and longitude coordinates and the pixel coordinates according to the latitude and longitude and pixel coordinates of the same feature points.

[0086] S405. Determine the pixel change information according to the latitude and longitude coordinates of the feature points in the aerial photograph data and the pixel coordinates of the feature points in the screenshot.

[0087] In step S401, the latitude and longitude coordinates of the feature points can be obtained using modeling software such as DJI ZhiTu and WebODM.

[0088] In step S402, the camera facing the actual channel can be used to take a video stream, and the feature points in the screenshot can be identified by taking a screenshot of the video stream.

[0089] In step S403, the positions of the feature points in the screenshot are identified, and the pixel coordinates of the feature points in the screenshot can be obtained according to the resolution data in the screenshot.

[0090] In step S404, a conversion relationship between the longitude-latitude coordinates and the pixel coordinates is established according to the longitude and latitude and the pixel coordinates of the same feature point. That is, for the same feature point, the conversion relationship between the longitude-latitude coordinates and the pixel coordinates is established, which can convert the longitude-latitude coordinates of the feature point into the pixel coordinates, and the corresponding inverse conversion relationship can also convert the pixel coordinates of the feature point into the longitude-latitude coordinates. The conversion relationship between the longitude-latitude coordinates and the pixel coordinates can be a linear mapping relationship.

[0091] In step S405, the pixel change information is determined according to the pixel coordinates of the feature point in the aerial photograph data and the pixel coordinates of the feature point in the screenshot. In this embodiment, the difference between the pixel coordinates of the feature point in the aerial photograph data and the pixel coordinates of the feature point in the screenshot can be calculated as the pixel change information of the feature point. Specifically, when calculating the difference, the pixel coordinates of the feature point in the screenshot can be subtracted from the pixel coordinates of the feature point in the aerial photograph data.

[0092] In this embodiment, when step S5 is performed, that is, the pixel change information is inversely deduced from the pixel coordinate system to the longitude-latitude coordinate system to obtain the feature point position change information, the following steps can be specifically performed:

[0093] S501. Obtain a pixel-actual distance scale;

[0094] S502. Determine the coordinate change in the longitude-latitude coordinate system according to the pixel change information and the pixel-actual distance scale;

[0095] S503. Obtain the distance from the center point of the unmanned aerial vehicle camera to the target point of the picture center;

[0096] S504. Determine the feature point position change information according to the coordinate change in the longitude-latitude coordinate system and the distance from the center point of the unmanned aerial vehicle camera to the target point of the picture center.

[0097] The principles of steps S501-S504 are shown in Figure 3

[0098] In step S501, the pixel-actual distance scale can represent the proportion between the pixel change value corresponding to the pixel change information and the actual distance change value in the actual channel. Therefore, in step S502, the pixel change information can be multiplied by the pixel-actual distance scale (when the pixel-actual distance scale represents the ratio of the actual distance change value in the actual channel to the pixel change value corresponding to the pixel change information), or the pixel change information can be divided by the pixel-actual distance scale (when the pixel-actual distance scale represents the ratio of the pixel change value corresponding to the pixel change information to the actual distance change value in the actual channel), and the result obtained by the calculation is the coordinate change in the longitude-latitude coordinate system [which means the change in the horizontal (or vertical) pixel direction], which corresponds to​Figure 3 line segment P_w O_w in FIG. 6.

[0099] In step S503, the distance from the center point of the camera of the unmanned aerial vehicle to the target point of the center of the picture can be calculated by the RTK coordinates, which corresponds to Figure 3 line segment O_w O_c in FIG. 6.

[0100] Referring to Figure 3 , β is the included angle between the water surface and the imaging plane of the camera in the horizontal (or vertical) pixel direction. Since the line segment O_w O_c and the included angle β are known, the lengths of the line segments O’O_c and O’O_w can be calculated.

[0101] In step S504, the angle α can be determined according to the length ratio of the line segment O_w O_c to the line segment P_w O_w, and the included angle β is known, so γ = α + β is also known. According to the known included angle γ and the length of the line segment O’O_c, the length of the line segment PO’ can be calculated. According to the known lengths of the line segments PO’ and O’O_w, the length of the line segment PO_w can be calculated. The length of the line segment PO_w means that the change of the feature point in the aerial photograph data in the horizontal (or vertical) pixel direction corresponds to the moving distance in the world coordinate system, that is, the change of the buoy position in the actual navigation channel (specifically, the change of the actual buoy latitude and longitude).

[0102] When step S6 is performed, each latitude and longitude coordinate value in the virtual navigation channel can be adjusted up or down according to the change of the buoy position in the actual navigation channel, so as to realize the correction of the virtual navigation channel, or the virtual navigation channel can be redrawn using the adjusted latitude and longitude coordinate values.

[0103] The principle of performing steps S1-S6 is that the actual scene layer with high precision is obtained by using the unmanned aerial vehicle, and the virtual navigation channel is drawn under the layer, so as to guarantee the accuracy of the navigation channel as much as possible, calibrate the navigation channel once every period of time, reduce the error between the virtual navigation channel and the actual navigation channel as much as possible, and improve the accuracy of the yaw algorithm. In addition, the actual scene is collected by using the unmanned aerial vehicle aerial photograph, so that the coordinate system of the final aerial synthesis picture is the same as the coordinate system used by the hardware manufacturer, the error caused by the coordinate system conversion is removed, and high-precision data is provided for the subsequent yaw algorithm.

[0104] The computer program for executing the virtual navigation channel drawing method based on the unmanned aerial vehicle aerial photograph in the embodiment can be written into a computer device or a storage medium, and when the computer program is read out and run, the virtual navigation channel drawing method based on the unmanned aerial vehicle aerial photograph in the embodiment is executed, so as to realize the same technical effect as the virtual navigation channel drawing method based on the unmanned aerial vehicle aerial photograph in the embodiment.

[0105] It should be noted that, as used in this disclosure and unless otherwise specified, an "and / or," where used, refers to a combination of that which is specified and / or one or both described with such term. In other words, "A, B, and / or C" means "only A," "only B," "only C," "A and B," "A and C," "B and C," or "A and B and C." It should also be noted that, as used in this disclosure and unless otherwise specified, a "plurality" means two or more, and a "combination" means two or more.

[0106] It should be understood that, although the terms first, second, third, etc. can be used herein to describe various elements, components, regions, layers and / or sections, these elements, components, regions, layers and / or sections should not be limited to these terms. These terms are only used to distinguish one element, component, region, layer or section from another element, component, region, layer or section. For example, a first element could also be termed a second element, and, similarly, a second element could also be termed a first element without departing from the scope of the disclosure. The use of any and all examples, or exemplary language (e.g., "such as" and "like") provided herein, is intended merely to better illuminate embodiments of the disclosure and does not pose a limitation on the scope of the disclosure unless otherwise claimed.

[0107] It should be appreciated that embodiments of the present application can be realized by either computer hardware, a combination of hardware and software, or by computer instructions stored in non-transitory computer-readable memory. The methods can be implemented in a computer program by using standard programming techniques including non-transitory computer-readable storage medium configured with a computer program, wherein the storage medium so configured makes the computer operate in a specific and predefined manner according to the methods described in the specific embodiments and the accompanying drawings. Each program can be implemented in a high-level procedural or object-oriented programming language to communicate with a computer system. However, the program can be implemented in assembly or machine language, if desired. In any case, the language can be a compiled or interpreted language. Moreover, the program can run on a specially programmed integrated circuit for this purpose.

[0108] Further, the operations of the processes described in this embodiment can be performed in any suitable order, unless otherwise indicated herein or otherwise clearly contradicted by context. The processes described in this embodiment (or variations and / or combinations thereof) can be implemented under the control of one or more computer systems configured with executable instructions (e.g., computer programs, one or more computer processes, or one or more applications) that can be executed by one or more processors, by hardware, or by a combination thereof. The computer programs include a plurality of instructions that are executable by one or more processors.

[0109] Further, the methods can be implemented in any suitable type of computing platform operatively coupled to, including but not limited to, a personal computer, a mini-computer, a mainframe, a workstation, a network or distributed computing environment, a stand-alone or integrated computer platform, or in communication with a charged particle tool or other imaging device, and the like. Aspects of the present application can be implemented in machine-readable code stored on a non-transitory storage medium or device, whether removable or integrated to the computing platform, such as a hard disk, an optical read and / or write storage medium, RAM, ROM, and the like, such that it can be read by a programmable computer to configure and operate the computer to perform the processes described herein when the storage medium or device is read by the computer. Further, the machine-readable code, or portions thereof, can be transmitted over wired or wireless networks. The present application described in this embodiment includes these and other different types of non-transitory computer readable storage media when such media include instructions or programs to implement the steps described above in conjunction with a microprocessor or other data processor. The present application also includes the computer itself when programmed according to the methods and techniques described in the present application.

[0110] The computer programs are capable of applying to input data to perform the functions described in this embodiment, thereby transforming the input data to generate output data that is stored to non-volatile memory. The output information can also be applied to one or more output devices, such as a display. In preferred embodiments of the present application, the transformed data represents a physical and tangible object, including a particular visual depiction of the physical and tangible object produced on a display.

[0111] The above description is only preferred embodiments of the present application, the present application is not limited to the above-described embodiments, as long as the same means to achieve the technical effects of the present application, any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application shall be included in the scope of protection of the present application. The technical solutions and / or embodiments within the scope of protection of the present application can have various modifications and changes.

Claims

1. A virtual route drawing method based on aerial photography of a drone, characterized by, The virtual channel drawing method based on the aerial photograph of the unmanned aerial vehicle comprises: acquiring aerial photograph data collected by an unmanned aerial vehicle from an actual channel; slicing a large image obtained by merging the aerial photograph data to obtain slice data; drawing a virtual channel according to the slice data; tracking pixel change information of a feature point in the aerial photograph data; the feature point is a position where a buoy is placed in the actual channel; obtaining feature point position change information by inversely deducing the pixel change information from a pixel coordinate system to a latitude and longitude coordinate system; correcting the virtual channel according to the feature point position change information; the tracking pixel change information of the feature point in the aerial photograph data comprises: determining latitude and longitude coordinates of the feature point in the aerial photograph data according to a position of the feature point in the aerial photograph data; obtaining a screenshot containing the feature point by taking a screenshot of the actual channel at a time; determining pixel coordinates of the feature point in the screenshot; establishing a conversion relationship between latitude and longitude coordinates and pixel coordinates according to latitude and longitude coordinates and pixel coordinates of the same feature point; determining the pixel change information according to the latitude and longitude coordinates of the feature point in the aerial photograph data and the pixel coordinates of the feature point in the screenshot. 2.The virtual route drawing method based on aerial photos of UAVs according to claim 1, wherein, the acquiring aerial photograph data collected by the unmanned aerial vehicle from the actual channel comprises: controlling the unmanned aerial vehicle to sail along the actual channel; controlling the unmanned aerial vehicle to take a photograph of the actual channel to obtain the aerial photograph data; controlling the unmanned aerial vehicle to take a photograph of a feature point in the actual channel to obtain feature point data. 3.The virtual air route plotting method based on aerial photos of UAVs according to claim 2, wherein, the acquiring aerial photograph data collected by the unmanned aerial vehicle from the actual channel further comprises: merging each aerial photograph data into a large image. 4.The virtual air route plotting method based on aerial photos of UAVs according to claim 3, wherein, the slicing the large image obtained by merging the aerial photograph data to obtain slice data comprises: deploying a war package of GeoServer; accessing a GeoServer page, slicing the large image in the GeoServer page to obtain the slice data; distributing a WMTS service through GeoServer after slicing is successful. 5.The virtual air route plotting method based on aerial photos of UAVs according to claim 4, wherein, the drawing a virtual channel according to the slice data comprises: loading a map tile through OpenLayers by a WMTS service; drawing the virtual channel by using a Polygon class in OpenLayers. 6.The virtual air route plotting method based on aerial photos of UAVs according to claim 5, wherein, the drawing the virtual channel by using the Polygon class in OpenLayers comprises: acquiring latitude and longitude data of the virtual channel; adding the latitude and longitude data of the virtual channel to Polygon; adding Polygon to Feature; adding Feature to Vector; adding Vector to a map instance Map to display the virtual channel. 7.The virtual route drawing method based on aerial photos of UAVs according to claim 1, wherein, the inversely deducing the pixel change information from the pixel coordinate system to the latitude and longitude coordinate system to obtain feature point position change information comprises: acquiring a pixel-actual distance scale; determining coordinate change in the latitude and longitude coordinate system according to the pixel change information and the pixel-actual distance scale; acquire a distance from a center point of a camera of the unmanned aerial vehicle to a target point of a picture center; determine the feature point position change information according to the coordinate change in the latitude-longitude coordinate system and the distance from the center point of the camera of the unmanned aerial vehicle to the target point of the picture center.

8. A computer apparatus, comprising: The apparatus comprises a memory and a processor, the memory is configured to store at least one program, and the processor is configured to load the at least one program to execute the method for virtual route plotting based on aerial photographs of unmanned aerial vehicles according to any one of claims 1-7.

9. A computer readable storage medium having stored therein a program that is executable by a processor, the program comprising instructions for causing the processor to perform the method of any one of claims 1 to 8. The program executable by the processor is configured to execute the method for virtual route plotting based on aerial photographs of unmanned aerial vehicles according to any one of claims 1-7 when executed by the processor.

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