A method and apparatus for drone denial guidance landing

Abstract

The application discloses a method and device for guiding and landing of a UAV (unmanned aerial vehicle) by refusal, relates to the technical field of visual guiding and landing, and is based on the idea of scene matching, and can select a flat terrain with rich ground texture features as a reference for guiding and landing without the help of ground cooperative objects. Inertial navigation information and visual sensor information are fused to correct picture skewing and deformation caused by the attitude of the UAV during landing, and the picture can be corrected even at a large attitude inclination. Random sampling consistency algorithm is used for off-ground height estimation, and the method is not sensitive to noise and has strong robustness.

Description

Technical Field

[0001] This invention relates to the field of visually guided landing technology, and in particular to a method and apparatus for denying guided landing of unmanned aerial vehicles (UAVs) in unknown environments. Background Technology

[0002] Currently, guided landing methods for UAVs in denied environments include visual guided landing methods based on visual SLAM / optical flow (such as ArUco marker recognition), guided landing methods based on lidar (such as terrain modeling), visual guided landing methods based on cooperative targets (such as ground cooperative targets / QR codes), landing methods based on infrared or LED light guidance (such as LED optical flow tracking), and inertial fusion positioning methods. Most of these algorithms are based on sensors and the "perception-localization-planning-control" landing process.

[0003] However, traditional landing guidance methods in denied environments are sensitive to environmental characteristics, have poor adaptability to dynamic scenarios, suffer from cumulative errors and drift, and have high hardware costs and system complexity. Visual landing guidance methods based on cooperating objects require the cooperation of ground-based cooperating objects for guided landing, thus having significant limitations. They are not suitable for autonomous landing in unknown environments and cannot guarantee the flight safety of UAVs in unknown environments. Summary of the Invention

[0004] In view of the above problems, the present invention provides a method and apparatus for preventing unmanned aerial vehicle (UAV) landing denial, or at least partially solving the above problems.

[0005] This invention provides the following solution: A method for denying guided landing of unmanned aerial vehicles (UAVs) includes: After determining that the UAV is hovering above the landing site, images in the hovering state are acquired using a visual sensor. A reference image is selected from the images in the hovering state using the mean squared error and peak signal-to-noise ratio. The reference image is subjected to grayscale processing and Gaussian filtering, and the images of the descent process acquired by the vision sensor are continuously subjected to grayscale processing and Gaussian filtering. A corrected image is obtained by correcting the image during the descent process using inertial navigation data; The corrected image is scaled proportionally to obtain the scaled image; The center of the scaled image is located in the reference image by matching and positioning the scaled image with the reference image using the sliding window method; The location coordinates are used to generate pseudo GPS coordinates, and the random sampling consensus algorithm is used to estimate the altitude above the ground to obtain an estimated altitude value. The drone is guided to land using the pseudo-GPS coordinates and the altitude estimate.

[0006] Preferably, the reference image selection method includes: Determine that the pitch angle, roll angle, and relative height changes are all less than the corresponding set thresholds; The mean square error (MSE) of adjacent images of 3×FPS frames is calculated as follows:

[0007] In the formula: and This represents two consecutive frames of images. and These represent the width and height of the image, respectively. A target image is randomly selected from the adjacent images of the 3×FPS frames, and the peak signal-to-noise ratio (PSNR) is calculated as follows:

[0008] If the PSNR is determined to be greater than or equal to a preset threshold, then the target image is determined to be the reference image.

[0009] Preferably, the grayscale conversion and Gaussian filtering processes include: The weighted average method is used for grayscale conversion, as shown in the following formula:

[0010] In the formula, The image after grayscale conversion. These are three-channel components; Gaussian filtering is used to remove noise from the grayscale image in order to eliminate discrete salt-and-pepper noise in the image.

[0011] Preferably, correcting the images during the descent process using inertial navigation data to obtain a corrected image includes: The transformation matrix between the navigation system and the UAV body coordinate system is calculated as follows:

[0012] In the formula: These are the heading angle, pitch angle, and roll angle of the drone, respectively. The transformation matrix from the computer vision sensor coordinate system to the machine coordinate system is as follows:

[0013] In the formula: These represent the relative attitudes of the visual sensor and the inertial navigation system on the three coordinate axes. The transformation matrix from the reference image coordinate system to the navigation coordinate system is calculated as follows:

[0014] The calibration diagram for the computer vision sensor is as follows:

[0015] In the formula, For the calibration diagram, These are the intrinsic parameters after calibration of the vision sensor. This is an uncorrected image after grayscale conversion.

[0016] Preferably, scaling the corrected image proportionally includes: Determine the scaling ratio ,as follows:

[0017] In the formula, This is the initial altitude at which the drone lands. The relative altitude at the current moment; Scaling Correction diagram Perform proportional scaling to obtain the scaled result. image.

[0018] Preferably, matching and locating the scaled image with the reference image using the sliding window method includes: The sliding step size is determined as follows:

[0019]

[0020] In the formula, These are the sliding step sizes in the width and height directions, respectively. These are the width and height of the template image, respectively; Feature detection is performed using the SURF feature detector. Feature matching is performed using the FLANN feature matching method, where the method for selecting feature points is the ratio test. RANSAC is used to remove outlier matching points and generate a more stable homography matrix; The matching result is determined by the average matching distance, and the lowest value is taken as the optimal matching result. The calculation is as follows:

[0021] In the formula, For the average matching distance, To match the number of feature points, For the first The distance between matching points; Based on the optimal matching result, we can conclude that... The coordinates of the image center in the reference image ( ).

[0022] Preferably, generating pseudo-GPS coordinates using the location coordinates includes: Using position coordinates ( )calculate Pseudo GPS coordinates of the image ( ),as follows:

[0023]

[0024] in, These are the horizontal and vertical tile indices of the map tile corresponding to the base image at the tile level, respectively. This refers to the tile grade.

[0025] Preferably, the method of using the random sample consensus algorithm to estimate the altitude above ground includes: Ground elevation is estimated using auxiliary point cloud information provided by radar, binocular cameras, and depth cameras. The elevation estimation employs a random sample consensus algorithm to fit the ground point cloud, including the following steps and methods: Initialize parameters, including maximum number of iterations, interior point threshold, and interior point number threshold; Randomly sample 3 points and calculate the plane equation; Calculate the distance from all points to the plane, and determine the interior points; Update the optimal plane; Repeat the iteration until the termination condition is met to obtain the optimal plane equation and the ground elevation estimate.

[0026] Preferably, guiding the drone to land using the pseudo-GPS coordinates and the altitude estimate includes: The landing is completed when the altitude above the ground is 0 or less than the altitude threshold.

[0027] A drone anti-landing guidance device for performing the above-described drone anti-landing guidance guidance method, the device comprising: A standard image selection unit is used to determine the image acquired by the visual sensor after the UAV hovers above the landing site, and to select a reference image from the hovering images using the mean squared error and peak signal-to-noise ratio. The grayscale and filtering processing unit is used to perform grayscale and Gaussian filtering processing on the reference image, and to continuously perform grayscale and Gaussian filtering processing on the images of the descent process acquired by the vision sensor. An image correction unit is used to correct images during the descent process using inertial navigation data to obtain a corrected image; An image scaling unit is used to scale the corrected image proportionally to obtain a scaled image; A sliding window feature matching unit is used to match and locate the scaled image with the reference image using the sliding window method to determine the position coordinates of the center of the scaled image in the reference image; The pseudo-GPS coordinate and altitude estimation generation unit is used to generate pseudo-GPS coordinates using the location coordinates, and at the same time use the random sampling consensus algorithm to estimate the altitude above the ground to obtain an altitude estimate. A landing guidance unit is used to guide the UAV to land using the pseudo-GPS coordinates and the altitude estimate.

[0028] According to specific embodiments provided by the present invention, the present invention discloses the following technical effects: This application provides a method and apparatus for preventing unmanned aerial vehicle (UAV) landing denial. Based on the concept of scene matching, this method can automatically select flat terrain with rich texture features as a landing reference without relying on ground objects. By fusing inertial navigation information and visual sensor information, it can correct for slant distortion caused by the UAV's attitude during landing, even at large tilt angles. The random sample consensus algorithm is used for ground altitude estimation, exhibiting noise insensitivity and strong robustness.

[0029] Of course, any product implementing this invention does not necessarily need to achieve all of the advantages described above at the same time. Attached Figure Description

[0030] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly described below. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without any creative effort.

[0031] Figure 1 This is a flowchart of a method for denying guided landing of an unmanned aerial vehicle (UAV) according to an embodiment of the present invention; Figure 2 This is a flowchart of a method for denying guided landing of an unmanned aerial vehicle (UAV) according to an embodiment of the present invention; Figure 3This is a schematic diagram of a drone landing denial device provided in an embodiment of the present invention; Figure 4 This is a schematic diagram of a drone landing denial device provided in an embodiment of the present invention. Detailed Implementation

[0032] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention are within the scope of protection of the present invention.

[0033] See Figure 1 , Figure 2 This invention provides a method for preventing unmanned aerial vehicle (UAV) landing by denying guidance, such as... Figure 1 , Figure 2 As shown, the method may include: S101: After the UAV hovers above the landing site, it uses a visual sensor to collect images in the hovering state. The mean squared error and peak signal-to-noise ratio are used to select a reference image from the images in the hovering state. When the UAV hovers above the landing site, a reference image (template) is selected based on the visual sensor during the landing process. After the reference image is determined, it is used as a fixed image. The method of determining the reference image includes the following steps and methods. (1) Determine whether the pitch angle, roll angle and relative height changes are all less than the corresponding smaller set threshold; (2) If (1) is satisfied, proceed to (3); otherwise stabilize the UAV attitude and repeat steps (1) to (2) until step (1) is satisfied. (3) Calculate the mean square error (MSE) of adjacent images of 3×FPS frames, as follows:

[0034] In the formula: and This represents two consecutive frames of images. and These represent the width and height of the image, respectively. Randomly select a frame from the adjacent 3×FPS frames above and calculate the peak signal-to-noise ratio (PSNR):

[0035] For a three-channel RGB image, MASI=255.

[0036] If PSNR ≥ preset threshold, the selected image is defined as the reference image (matching template); otherwise, step (3) is repeated. In practical applications, this threshold can be 50.

[0037] S102: The reference image is subjected to grayscale conversion and Gaussian filtering, and the images acquired through the vision sensor during the landing process are continuously subjected to grayscale conversion and Gaussian filtering; wherein the image acquisition frequency during the landing process can be determined according to the actual landing scenario requirements, and each acquired image is subsequently processed. Specifically, a weighted average method is used for grayscale conversion, as shown in the following formula:

[0038] In the formula, The image after grayscale conversion. These are three-channel components; Gaussian filtering is used to remove noise from the grayscale image in order to eliminate discrete salt-and-pepper noise in the image.

[0039] S103: Correcting the image during the descent process using inertial navigation data to obtain a corrected image; specifically, this includes: The transformation matrix between the navigation system and the UAV body coordinate system is calculated as follows:

[0040] In the formula: These are the heading angle, pitch angle, and roll angle of the drone, respectively. The transformation matrix from the computer vision sensor coordinate system to the machine coordinate system is as follows: In the formula: These represent the relative attitudes of the visual sensor and the inertial navigation system on the three coordinate axes. The transformation matrix from the reference image coordinate system to the navigation coordinate system is calculated as follows:

[0041] The calibration diagram for the computer vision sensor is as follows:

[0042] In the formula, For the calibration diagram, These are the intrinsic parameters after calibration of the vision sensor. This is an uncorrected image after grayscale conversion.

[0043] S104: Scale the corrected image proportionally to obtain a scaled image; scaling the corrected image proportionally includes: Determine the scaling ratio ,as follows:

[0044] In the formula, This is the initial altitude at which the drone lands. The relative altitude at the current moment; Scaling Correction diagram Perform proportional scaling to obtain the scaled result. image.

[0045] S105: Using the sliding window method, match and locate the scaled image with the reference image to determine the position coordinates of the center of the scaled image in the reference image; matching and locating the scaled image with the reference image using the sliding window method includes: The sliding step size is determined as follows:

[0046]

[0047] In the formula, These are the sliding step sizes in the width and height directions, respectively. These are the width and height of the template image, respectively. In use, if you want to improve matching speed, you can increase the sliding step size, but the accuracy will decrease. If you want to improve matching accuracy, you can decrease the sliding step size, but the matching speed will decrease. The choice of the above step size is a balance between speed and accuracy.

[0048] Feature detection is performed using the SURF feature detector. Feature matching is performed using the FLANN feature matching method, where the method for selecting feature points is the ratio test. RANSAC is used to remove outlier matching points and generate a more stable homography matrix; The matching result is determined by the average matching distance, and the lowest value is taken as the optimal matching result. The calculation is as follows:

[0049] In the formula, For the average matching distance, To match the number of feature points, For the first The distance between matching points; Based on the optimal matching result, we can conclude that... The coordinates of the image center in the reference image ( ).

[0050] S106: Using the location coordinates, generate pseudo-GPS coordinates, and simultaneously use a random sample consensus algorithm to estimate the altitude above the ground to obtain an estimated altitude value; generating pseudo-GPS coordinates using the location coordinates includes: Using position coordinates ( )calculate Pseudo GPS coordinates of the image ( ),as follows:

[0051]

[0052] in, These are the horizontal and vertical tile indices of the map tile corresponding to the base image at the tile level, respectively. The tile grades are 1 to 19.

[0053] The altitude estimate obtained by using the random sample consensus algorithm for ground clearance estimation includes: Ground elevation is estimated using auxiliary point cloud information provided by radar, binocular cameras, and depth cameras. The elevation estimation employs a random sample consensus algorithm to fit the ground point cloud, including the following steps and methods: Initialize parameters, including maximum number of iterations, interior point threshold, and interior point number threshold; Randomly sample 3 points and calculate the plane equation; Calculate the distance from all points to the plane, and determine the interior points; Update the optimal plane; Repeat the iteration until the termination condition is met to obtain the optimal plane equation and the ground elevation estimate.

[0054] S107: Guide the UAV to land using the pseudo-GPS coordinates and the estimated altitude. Landing is guided using the pseudo-GPS coordinates and altitude information. Landing is completed when the altitude is 0 or less than a certain minimum threshold.

[0055] The UAV landing guidance method provided in this application addresses the need for autonomous and safe landing in UAV-denied environments. It guides the UAV to a safe landing based on image correction and image matching algorithms. This method selects a reference image using the mean squared error and peak signal-to-noise ratio, corrects the UAV's viewpoint image using inertial navigation data, performs feature matching using a sliding window method, generates pseudo-GPS based on image coordinate information, and simultaneously estimates ground clearance using a random sample consensus algorithm to guide the UAV to a safe landing. This method can be used for UAV landing guidance in denied and unknown environments.

[0056] In summary, the UAV denied landing guidance method provided in this application, based on the concept of scene matching, can autonomously select flat terrain with rich ground texture features as a landing guidance reference without relying on ground objects. By fusing inertial navigation information and visual sensor information, it can correct for the slant distortion of the image caused by the UAV's attitude during landing, even at large tilt angles. The random sample consensus algorithm is used for ground altitude estimation, exhibiting noise insensitivity and strong robustness.

[0057] See Figure 3 This application embodiment can also provide a drone anti-landing guidance device, such as... Figure 3 As shown, the device for performing the above-described method for denying guided landing of unmanned aerial vehicles (UAVs) may include: The standard image selection unit 301 is used to determine the image acquired by the visual sensor after the UAV hovers above the landing site, and to select a reference image from the hovering image using the mean squared error and peak signal-to-noise ratio. The grayscale and filtering processing unit 302 is used to perform grayscale processing and Gaussian filtering processing on the reference image, and to continuously perform grayscale processing and Gaussian filtering processing on the image of the descent process acquired by the vision sensor. Image correction unit 303 is used to correct the image during the landing process using inertial navigation data to obtain a corrected image; Image scaling unit 304 is used to scale the corrected image proportionally to obtain a scaled image; The sliding window feature matching unit 305 is used to match and locate the scaled image with the reference image using the sliding window method to determine the position coordinates of the center of the scaled image in the reference image; The pseudo-GPS coordinate and altitude estimation generation unit 306 is used to generate pseudo-GPS coordinates using the location coordinates, and at the same time use the random sampling consensus algorithm to estimate the altitude above the ground to obtain an altitude estimate. The landing guidance unit 307 is used to guide the UAV to land using the pseudo GPS coordinates and the altitude estimate.

[0058] This application embodiment can also provide a drone landing denial device, the device including a processor and a memory: The memory is used to store program code and transmit the program code to the processor; The processor is used to execute the steps of the above-described method for denying guided landing of unmanned aerial vehicles according to the instructions in the program code.

[0059] like Figure 4As shown in the figure, an embodiment of this application provides a drone landing denial device, which may include: a processor 10, a memory 11, a communication interface 12, and a communication bus 13. The processor 10, memory 11, and communication interface 12 all communicate with each other through the communication bus 13.

[0060] In this embodiment, the processor 10 may be a central processing unit (CPU), an application-specific integrated circuit, a digital signal processor, a field-programmable gate array, or other programmable logic devices.

[0061] The processor 10 can call programs stored in the memory 11. Specifically, the processor 10 can execute operations in embodiments of the drone denial-guided landing method.

[0062] The memory 11 is used to store one or more programs. The programs may include program code, which includes computer operation instructions. In this embodiment, the memory 11 stores at least a program for implementing the following functions: After determining that the UAV is hovering above the landing site, images in the hovering state are acquired using a visual sensor. A reference image is selected from the images in the hovering state using the mean squared error and peak signal-to-noise ratio. The reference image is subjected to grayscale processing and Gaussian filtering, and the images of the descent process acquired by the vision sensor are continuously subjected to grayscale processing and Gaussian filtering. A corrected image is obtained by correcting the image during the descent process using inertial navigation data; The corrected image is scaled proportionally to obtain the scaled image; The center of the scaled image is located in the reference image by matching and positioning the scaled image with the reference image using the sliding window method; The location coordinates are used to generate pseudo GPS coordinates, and the random sampling consensus algorithm is used to estimate the altitude above the ground to obtain an estimated altitude value. The drone is guided to land using the pseudo-GPS coordinates and the altitude estimate.

[0063] In addition, memory 11 may include high-speed random access memory, and may also include non-volatile memory, such as at least one disk storage device or other volatile solid-state storage device.

[0064] Communication interface 12 can be an interface for the communication module, used to connect with other devices or systems.

[0065] Of course, it should be noted that, Figure 4The structure shown does not constitute a limitation on the drone anti-landing guidance device in the embodiments of this application. In practical applications, drone anti-landing guidance devices may include those that are more advanced than those described above. Figure 4 More or fewer components as shown, or combinations of certain components.

[0066] This application embodiment may also provide a computer-readable storage medium for storing program code for performing the steps of the above-described method for denying guided landing of unmanned aerial vehicles.

[0067] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0068] As can be seen from the above description of the embodiments, those skilled in the art can clearly understand that this application can be implemented by means of software plus necessary general-purpose hardware platforms. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in various embodiments or some parts of the embodiments of this application.

[0069] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, for system or system embodiments, since they are basically similar to method embodiments, the description is relatively simple, and relevant parts can be referred to the descriptions in the method embodiments. The systems and system embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without creative effort.

[0070] The above description is merely a preferred embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention are included within the scope of protection of the present invention.

Claims

1. A method for denying guided landing of unmanned aerial vehicles (UAVs), characterized in that, include: After determining that the UAV is hovering above the landing site, images in the hovering state are acquired using a visual sensor. A reference image is selected from the images in the hovering state using the mean squared error and peak signal-to-noise ratio. The reference image is subjected to grayscale processing and Gaussian filtering, and the images of the descent process acquired by the vision sensor are continuously subjected to grayscale processing and Gaussian filtering. A corrected image is obtained by correcting the image during the descent process using inertial navigation data; the correction process includes: The transformation matrix between the navigation system and the UAV body coordinate system is calculated as follows: In the formula: These are the heading angle, pitch angle, and roll angle of the drone, respectively. The transformation matrix from the computer vision sensor coordinate system to the machine coordinate system is as follows: In the formula: These represent the relative attitudes of the visual sensor and the inertial navigation system on the three coordinate axes. The transformation matrix from the reference image coordinate system to the navigation coordinate system is calculated as follows: The calibration diagram for the computer vision sensor is as follows: In the formula, For the calibration diagram, These are the intrinsic parameters after calibration of the vision sensor. This is an uncorrected image after grayscale conversion; The corrected image is scaled proportionally to obtain the scaled image; The center of the scaled image is located in the reference image by matching and positioning the scaled image with the reference image using the sliding window method; The location coordinates are used to generate pseudo GPS coordinates, and the random sampling consensus algorithm is used to estimate the altitude above the ground to obtain an estimated altitude value. The drone is guided to land using the pseudo-GPS coordinates and the altitude estimate.

2. The method for denying guided landing of an unmanned aerial vehicle (UAV) according to claim 1, characterized in that, The reference image selection method includes: Determine that the pitch angle, roll angle, and relative height changes are all less than the corresponding set thresholds; calculate Mean square error of adjacent frames As shown in the following formula: In the formula: and This represents two consecutive frames of images. and These represent the width and height of the image, respectively. From the above A target image is randomly selected from adjacent frames, and the peak signal-to-noise ratio is calculated. As shown in the following formula: Sure If a preset threshold is set, the target image is determined as the reference image.

3. The method for denying guided landing of unmanned aerial vehicles according to claim 1, characterized in that, The grayscale conversion and Gaussian filtering processes include: The weighted average method is used for grayscale conversion, as shown in the following formula: In the formula, The image after grayscale conversion. These are three-channel components; Gaussian filtering is used to remove noise from the grayscale image in order to eliminate discrete salt-and-pepper noise in the image.

4. The method for denying guided landing of an unmanned aerial vehicle (UAV) according to claim 1, characterized in that, Scaling the corrected image proportionally includes: Determine the scaling ratio ,as follows: In the formula, This is the initial altitude at which the drone lands. The relative altitude at the current moment; Scaling Correction diagram Perform proportional scaling to obtain the scaled result. image.

5. The method for denying guided landing of an unmanned aerial vehicle (UAV) according to claim 1, characterized in that, Matching and locating the scaled image with the reference image using the sliding window method includes: The sliding step size is determined as follows: In the formula, These are the sliding step sizes in the width and height directions, respectively. These are the width and height of the template image, respectively; Feature detection is performed using the SURF feature detector. Feature matching is performed using the FLANN feature matching method, where the method for selecting feature points is the ratio test. RANSAC is used to remove outlier matching points and generate a more stable homography matrix; The matching result is determined by the average matching distance, and the lowest value is taken as the optimal matching result. The calculation is as follows: In the formula, For the average matching distance, To match the number of feature points, For the first The distance between each matching point; Based on the optimal matching result, we can conclude that... The coordinates of the image center in the reference image ( ).

6. The method for denying guided landing of an unmanned aerial vehicle (UAV) according to claim 5, characterized in that, Generating pseudo-GPS coordinates using the aforementioned location coordinates includes: Using position coordinates ( )calculate Pseudo GPS coordinates of the image ( ),as follows: in, These are the horizontal and vertical tile indices of the map tile corresponding to the base image at the tile level, respectively. This refers to the tile grade.

7. The method for denying guided landing of an unmanned aerial vehicle (UAV) according to claim 6, characterized in that, The altitude estimate obtained by using the random sample consensus algorithm for ground clearance estimation includes: Ground elevation is estimated using auxiliary point cloud information provided by radar, binocular cameras, and depth cameras. The elevation estimation employs a random sample consensus algorithm to fit the ground point cloud, including the following steps and methods: Initialize parameters, including maximum number of iterations, interior point threshold, and interior point number threshold; Randomly sample 3 points and calculate the plane equation; Calculate the distance from all points to the plane, and determine the interior points; Update the optimal plane; Repeat the iteration until the termination condition is met to obtain the optimal plane equation and the ground elevation estimate.

8. The method for denying guided landing of an unmanned aerial vehicle (UAV) according to claim 1, characterized in that, Guiding the drone to land using the pseudo-GPS coordinates and the altitude estimate includes: The landing is completed when the altitude above the ground is 0 or less than the altitude threshold.

9. A device for preventing unmanned aerial vehicle (UAV) landing by denying guidance, characterized in that, For performing the unmanned aerial vehicle (UAV) denied landing method according to any one of claims 1-8, the apparatus comprises: A standard image selection unit is used to determine the image acquired by the visual sensor after the UAV hovers above the landing site, and to select a reference image from the hovering images using the mean squared error and peak signal-to-noise ratio. The grayscale and filtering processing unit is used to perform grayscale and Gaussian filtering processing on the reference image, and to continuously perform grayscale and Gaussian filtering processing on the images of the descent process acquired by the visual sensor. An image correction unit is used to correct images during the descent process using inertial navigation data to obtain a corrected image; An image scaling unit is used to scale the corrected image proportionally to obtain a scaled image; A sliding window feature matching unit is used to match and locate the scaled image with the reference image using the sliding window method to determine the position coordinates of the center of the scaled image in the reference image; The pseudo-GPS coordinate and altitude estimation generation unit is used to generate pseudo-GPS coordinates using the location coordinates, and at the same time use the random sampling consensus algorithm to estimate the altitude above the ground to obtain an altitude estimate. A landing guidance unit is used to guide the UAV to land using the pseudo-GPS coordinates and the altitude estimate.

Images