Unmanned aerial vehicle denial landing guiding method and device

Abstract

The invention discloses an unmanned aerial vehicle denial landing guidance method and device, and relates to the technical field of visual guidance landing, and the method is based on a scene matching idea, does not need to use a ground cooperation object, and can automatically select a flat terrain with abundant ground texture features as a landing guidance reference for automatic landing. Inertial navigation information and visual sensor information are fused, so that picture strabismus deformation caused by the attitude of the unmanned aerial vehicle in the landing process can be corrected, and the picture can still be corrected even under the inclination angle of the large attitude. A random sampling consistency algorithm is used for carrying out terrain clearance estimation, noise insensitivity is achieved, and robustness is high.

Description

Technical Field

[0001] The present invention relates to the field of vision-guided landing technology, and in particular to a method and device for denying guided landing of an unmanned aerial vehicle (UAV) suitable for use in an unknown environment. Background Art

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

[0003] However, traditional methods for guided landing in denied environments are sensitive to environmental characteristics, poorly adaptable to dynamic scenarios, subject to cumulative errors and drift, and have high hardware costs and system complexity. Vision-guided landing methods based on objects require the cooperation of ground objects for guided landing, resulting in significant limitations. They are not suitable for autonomous landing in unknown environments and cannot guarantee the flight safety of drones in such environments. Summary of the Invention

[0004] In view of the above problems, the present invention provides a method and device for denying guided landing of a UAV, which is used to overcome the above problems or at least partially solve the above problems.

[0005] The present invention provides the following solutions:

[0006] A method for denying and guiding landing of a drone, comprising:

[0007] After the drone is determined to be hovering above the intended landing site, an image in the hovering state is acquired using a visual sensor, and a reference image is selected from the image in the hovering state using mean square error and peak signal-to-noise ratio;

[0008] performing grayscale processing and Gaussian filtering on the reference image, and continuously performing grayscale processing and Gaussian filtering on the image during the landing process collected by the visual sensor;

[0009] Correcting the image during the landing process by using inertial navigation data to obtain a corrected image;

[0010] Scaling the corrected image to obtain a scaled image;

[0011] Matching and positioning the scaled image with the reference image using a sliding window method to determine the position coordinates of the center of the scaled image in the reference image;

[0012] Generate pseudo GPS coordinates using the position coordinates, and simultaneously use a random sampling consistency algorithm to estimate the height above the ground to obtain an estimated height value;

[0013] The pseudo GPS coordinates and the altitude estimate are used to guide the drone to land.

[0014] Preferably, the reference image selection method includes:

[0015] Determine that the pitch angle, roll angle, and relative height change are all less than the corresponding set thresholds;

[0016] Calculate the mean square error (MSE) of adjacent images of 3×FPS frames as follows:

[0017]

[0018] Where: I(i,j) and K(i,j) represent the two adjacent frames of images, m and n represent the width and height of the image respectively;

[0019] A target image is randomly selected from the 3×FPS frame adjacent images, and the peak signal-to-noise ratio (PSNR) is calculated as follows:

[0020]

[0021] If it is determined that the PSNR is greater than or equal to a preset threshold, the target image is determined to be a reference image.

[0022] Preferably, the grayscale processing and Gaussian filtering processing include:

[0023] The weighted average method is used for grayscale processing, as shown in the following formula:

[0024] Gray=0.299R+0.587G+0.144B

[0025] In the formula, Gray is the grayscale image, and R, G, and B are the three-channel components respectively;

[0026] Gaussian filtering is used to remove noise from the grayscale image in order to eliminate discrete salt and pepper noise in the image.

[0027] Preferably, correcting the image during the landing process by using inertial navigation data to obtain a corrected image includes:

[0028] The transformation matrix between the navigation system and the drone body coordinate system is calculated as follows:

[0029]

[0030] Where: Ψ, θ, and γ are the heading angle, pitch angle, and roll angle of the UAV respectively;

[0031] The conversion matrix from the visual sensor coordinate system to the body coordinate system is calculated as follows:

[0032]

[0033] Where: are the relative postures of the visual sensor and the inertial navigation on the three coordinate axes;

[0034] The transformation matrix from the reference image coordinate system to the navigation coordinate system is calculated as follows:

[0035]

[0036] Calculate the correction map of the vision sensor as follows:

[0037]

[0038] Where, is the correction image, K is the internal reference of the visual sensor after calibration, and U is the uncorrected image after grayscale conversion.

[0039] Preferably, scaling the corrected image includes:

[0040] Determine the scaling factor k as follows:

[0041]

[0042] Where, is the initial height of the UAV when it lands, h t is the relative height at the current moment;

[0043] Correction image by scaling k Perform proportional scaling to obtain the scaled image.

[0044] Preferably, using a sliding window method to match and locate the scaled image with the reference image includes:

[0045] Determine the sliding step size as follows:

[0046] step w =w0 / 8

[0047] step h =h0 / 8

[0048] Where, step w 、step h are the sliding steps in width and height respectively, w and h are the width and height of the template image respectively;

[0049] Feature detection is performed using SURF feature detector;

[0050] The FLANN feature matching method is used for feature matching, and the method for selecting feature points is the ratio test;

[0051] Use RANSAC to remove abnormal matching points and generate a more stable homography matrix;

[0052] The matching result is judged by the average matching distance, and the lowest value is taken as the optimal matching result. The calculation is as follows:

[0053]

[0054] Where, is the average matching distance, N is the number of matching feature points, d i is the distance of the i-th matching point;

[0055] Through the optimal matching results, we can get The position coordinates of the image center in the reference image (P x 、P y ).

[0056] Preferably, generating pseudo GPS coordinates using the location coordinates includes:

[0057] Using the position coordinates (P x 、P y )calculate Pseudo GPS coordinates of the image (L0, L a ),as follows:

[0058]

[0059] Among them, t x , t y are the assumed GPS coordinates of the reference image, and level is the tile level.

[0060] Preferably, using a random sampling consistency algorithm to estimate the height above the ground to obtain an estimated height value includes:

[0061] Height estimation is performed using auxiliary point cloud information provided by radar, binocular camera, and depth camera. Height estimation uses a random sampling consistency algorithm to fit the ground point cloud, including the following steps and methods:

[0062] Initialization parameters, including a maximum number of iterations, an inlier threshold, and an inlier number threshold;

[0063] Randomly sample 3 points and calculate the plane equation;

[0064] Calculate the distances of all points to the plane and determine the interior points;

[0065] Update the best plane;

[0066] Repeat the iteration until the termination condition is met and the optimal plane equation and height above the ground are estimated.

[0067] Preferably, guiding the drone to land using the pseudo GPS coordinates and the altitude estimate includes:

[0068] When the altitude above the ground is 0 or less than the altitude threshold, the landing is completed.

[0069] A UAV landing denial guidance device, used to execute the above-mentioned UAV landing denial guidance method, the device comprising:

[0070] A standard image selection unit is used to determine an image of the drone in a hovering state acquired by using a visual sensor after the drone hovers above a target landing site, and to select a reference image from the image in the hovering state using mean square error and peak signal-to-noise ratio;

[0071] a grayscale and filtering processing unit, configured to perform grayscale processing and Gaussian filtering on the reference image, and continuously perform grayscale processing and Gaussian filtering on the image during the landing process acquired by the visual sensor;

[0072] an image correction unit, configured to correct the image during the landing process using inertial navigation data to obtain a corrected image;

[0073] An image scaling unit, configured to scale the corrected image in equal proportion to obtain a scaled image;

[0074] A sliding window feature matching unit, configured to match and locate the scaled image with the reference image using a sliding window method to determine the position coordinates of the center of the scaled image in the reference image;

[0075] a pseudo GPS coordinate and height estimation generating unit, configured to generate pseudo GPS coordinates using the position coordinates and simultaneously estimate the height above the ground using a random sampling consistency algorithm to obtain a height estimation value;

[0076] A landing guidance unit is used to guide the UAV to land using the pseudo GPS coordinates and the altitude estimation value.

[0077] According to the specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0078] The embodiments of the present application provide a method and device for unmanned aerial vehicle (UAV) landing denial guidance. This method, based on scene matching, does not require the aid of ground objects and can autonomously select flat terrain with rich ground texture features as a reference for landing guidance. By fusing inertial navigation information with visual sensor information, the image distortion caused by the UAV's posture during landing can be corrected, even at large inclination angles. A random sampling consistency algorithm is used to estimate ground height, which is noise-insensitive and highly robust.

[0079] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0080] To more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be derived from these drawings without inventive effort.

[0081] Figure 1 This is a flow chart of a method for denying guided landing of a UAV provided by an embodiment of the present invention;

[0082] Figure 2 This is a flowchart of a method for denying and guiding landing of a UAV provided by an embodiment of the present invention;

[0083] Figure 3 Schematic diagram of a UAV landing denial guidance device provided by an embodiment of the present invention;

[0084] Figure 4 Schematic diagram of a UAV landing denial guidance device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0085] The following will be combined with the accompanying drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field are within the scope of protection of the present invention.

[0086] See also Figure 1 、 Figure 2 , is a method for denying and guiding landing of a UAV provided by an embodiment of the present invention, such as Figure 1 、 Figure 2 As shown, the method may include:

[0087] S101: After determining that the drone is hovering above the landing site, using a visual sensor to capture an image in the hovering state, and selecting a reference image from the image in the hovering state using mean square error and peak signal-to-noise ratio; when the drone is hovering above the landing site, using the visual sensor to select a reference image (template) during the landing process, and after the reference image is determined, use it as a fixed image. The reference image determination method includes the following steps and methods;

[0088] (1) Determine whether the pitch angle, roll angle, and relative height change are all less than the corresponding smaller set thresholds;

[0089] (2) If (1) is satisfied, proceed to (3), otherwise stabilize the drone's attitude and repeat steps (1) to

[0090] (2) until step (1) is satisfied;

[0091] (3) Calculate the mean square error (MSE) of adjacent images of 3×FPS frames as follows:

[0092]

[0093] Where: I(i,j) and K(i,j) represent the two adjacent frames of images, m and n represent the width and height of the image respectively;

[0094] Randomly select a frame from the 3×FPS frame adjacent images above and calculate the peak signal-to-noise ratio (PSNR):

[0095]

[0096] For a three-channel RGB image, MAXI=255.

[0097] If PSNR is greater than or equal to a preset threshold, the selected image is defined as the reference image (matching template), otherwise step (3) is repeated. In practical applications, the threshold can be 50.

[0098] S102: Grayscale and Gaussian filter the reference image, and continue to grayscale and Gaussian filter the images collected by the visual sensor during the landing process. The image acquisition frequency during the landing process can be determined according to the actual landing scene requirements, and each collected image is subsequently processed. In specific implementation, the weighted average method is used for grayscale processing, as shown in the following formula:

[0099] Gray=0.299R+0.587G+0.144B

[0100] In the formula, Gray is the grayscale image, and R, G, and B are the three-channel components respectively;

[0101] Gaussian filtering is used to remove noise from the grayscale image in order to eliminate discrete salt and pepper noise in the image.

[0102] S103: Correcting the image during the landing process using inertial navigation data to obtain a corrected image; the specific implementation includes:

[0103] The transformation matrix between the navigation system and the drone body coordinate system is calculated as follows:

[0104]

[0105] Where: Ψ, θ, and γ are the heading angle, pitch angle, and roll angle of the UAV respectively;

[0106] The conversion matrix from the visual sensor coordinate system to the body coordinate system is calculated as follows:

[0107]

[0108] Where: are the relative postures of the visual sensor and the inertial navigation on the three coordinate axes;

[0109] The transformation matrix from the reference image coordinate system to the navigation coordinate system is calculated as follows:

[0110]

[0111] Calculate the correction map of the vision sensor as follows:

[0112]

[0113] Where, is the correction image, K is the internal reference of the visual sensor after calibration, and U is the uncorrected image after grayscale conversion.

[0114] S104: scaling the corrected image to obtain a scaled image; scaling the corrected image includes:

[0115] Determine the scaling factor k as follows:

[0116]

[0117] Where, is the initial height of the UAV when it lands, h t is the relative height at the current moment;

[0118] Correction image by scaling k Perform proportional scaling to obtain the scaled image.

[0119] S105: Matching and positioning the scaled image with the reference image using a sliding window method to determine the position coordinates of the center of the scaled image in the reference image; matching and positioning the scaled image with the reference image using a sliding window method includes:

[0120] Determine the sliding step size as follows:

[0121]

[0122] Where, step w 、step h are the sliding steps in width and height respectively, w and h are the width and height of the template image respectively; in use, if the matching speed is pursued, the sliding step can be increased but the accuracy will be reduced; if the matching accuracy is pursued, the sliding step can be reduced but the matching speed will be reduced. The choice of the above step size is a balance between speed and accuracy.

[0123] Feature detection is performed using SURF feature detector;

[0124] The FLANN feature matching method is used for feature matching, and the method for selecting feature points is the ratio test;

[0125] Use RANSAC to remove abnormal matching points and generate a more stable homography matrix;

[0126] The matching result is judged by the average matching distance, and the lowest value is taken as the optimal matching result. The calculation is as follows:

[0127]

[0128] Where, is the average matching distance, N is the number of matching feature points, d i is the distance of the i-th matching point;

[0129] Through the optimal matching results, we can get The position coordinates of the image center in the reference image (P x 、P y ).

[0130] S106: Generate pseudo GPS coordinates using the position coordinates, and simultaneously use a random sampling consistency algorithm to estimate the height above the ground to obtain a height estimation value; generating pseudo GPS coordinates using the position coordinates includes:

[0131] Using the position coordinates (P x 、P y )calculate Pseudo GPS coordinates of the image (L0, L a ),as follows:

[0132]

[0133] Among them, t x , t y are the assumed GPS coordinates of the reference image, and level is the tile level (1 to 19).

[0134] The height estimation value obtained by using the random sampling consistency algorithm for ground height estimation includes:

[0135] Height estimation is performed using auxiliary point cloud information provided by radar, binocular camera, and depth camera. Height estimation uses a random sampling consistency algorithm to fit the ground point cloud, including the following steps and methods:

[0136] Initialization parameters, including a maximum number of iterations, an inlier threshold, and an inlier number threshold;

[0137] Randomly sample 3 points and calculate the plane equation;

[0138] Calculate the distances of all points to the plane and determine the interior points;

[0139] Update the best plane;

[0140] Repeat the iteration until the termination condition is met and the optimal plane equation and height above the ground are estimated.

[0141] S107: Guide the drone to land using the pseudo GPS coordinates and the altitude estimate.

[0142] The pseudo GPS coordinates and the height information of the altitude above the ground are used to guide the landing. When the altitude above the ground is 0 or less than a certain minimum threshold, the landing is completed.

[0143] The method for guided landing of a drone in a denied environment, provided in embodiments of the present application, addresses the need for autonomous and safe drone landing in a denied environment by guiding the drone to a safe landing based on image correction and image matching algorithms. This method uses mean square error and peak signal-to-noise ratio to select a reference image, uses inertial navigation data to correct the drone's perspective image, employs a sliding window method for feature matching, and finally generates a pseudo-GPS based on the image coordinate information. A random sampling consistency algorithm is then used to estimate the ground clearance to guide the drone to a safe landing. This method can be used to guide drone landing in denied and unknown environments.

[0144] In summary, the drone landing denial guidance method provided in this application is based on scene matching and can autonomously select flat terrain with rich ground texture features as a reference for landing guidance without the need for ground objects. By fusing inertial navigation information with visual sensor information, it can correct for the image distortion caused by the drone's posture during landing, even at large tilt angles. It also uses a random sampling consistency algorithm for height estimation, which is noise-insensitive and highly robust.

[0145] See also Figure 3 , the embodiment of the present application can also provide a UAV rejection and guidance landing device, such as Figure 3 As shown, for executing the above-mentioned UAV denied guided landing method, the device may include:

[0146] The standard image selection unit 301 is configured to obtain an image of the drone in a hovering state by using a visual sensor after the drone hovers above the landing site, and select a reference image from the image in the hovering state by using mean square error and peak signal-to-noise ratio;

[0147] a grayscale and filtering processing unit 302 for performing grayscale processing and Gaussian filtering on the reference image, and continuously performing grayscale processing and Gaussian filtering on the image during the landing process acquired by the visual sensor;

[0148] An image correction unit 303 is configured to correct the image during the landing process using inertial navigation data to obtain a corrected image;

[0149] An image scaling unit 304 is configured to scale the corrected image to obtain a scaled image;

[0150] A sliding window feature matching unit 305 is configured to match and locate the scaled image with the reference image using a sliding window method to determine the position coordinates of the center of the scaled image in the reference image;

[0151] a pseudo GPS coordinate and height estimation generating unit 306 for generating pseudo GPS coordinates using the position coordinates and simultaneously estimating the height above the ground using a random sampling consistency algorithm to obtain a height estimation value;

[0152] The landing guidance unit 307 is configured to guide the UAV to land using the pseudo GPS coordinates and the altitude estimation value.

[0153] The embodiment of the present application may also provide a drone landing denial guidance device, the device comprising a processor and a memory:

[0154] The memory is used to store program code and transmit the program code to the processor;

[0155] The processor is used to execute the steps of the above-mentioned drone denied guided landing method according to the instructions in the program code.

[0156] like Figure 4 As shown, an embodiment of the present application provides a UAV landing denial guidance device, which may include: a processor 10, a memory 11, a communication interface 12, and a communication bus 13. The processor 10, the memory 11, and the communication interface 12 all communicate with each other through the communication bus 13.

[0157] In the embodiment of the present application, 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.

[0158] The processor 10 can call the program stored in the memory 11. Specifically, the processor 10 can execute the operations in the embodiment of the drone denied guided landing method.

[0159] The memory 11 is used to store one or more programs. The program may include program code, and the program code includes computer operating instructions. In the embodiment of the present application, the memory 11 stores at least a program for implementing the following functions:

[0160] After the drone is determined to be hovering above the intended landing site, an image in the hovering state is acquired using a visual sensor, and a reference image is selected from the image in the hovering state using mean square error and peak signal-to-noise ratio;

[0161] performing grayscale processing and Gaussian filtering on the reference image, and continuously performing grayscale processing and Gaussian filtering on the image during the landing process collected by the visual sensor;

[0162] Correcting the image during the landing process by using inertial navigation data to obtain a corrected image;

[0163] Scaling the corrected image to obtain a scaled image;

[0164] Matching and positioning the scaled image with the reference image using a sliding window method to determine the position coordinates of the center of the scaled image in the reference image;

[0165] Generate pseudo GPS coordinates using the position coordinates, and simultaneously use a random sampling consistency algorithm to estimate the height above the ground to obtain an estimated height value;

[0166] The pseudo GPS coordinates and the altitude estimate are used to guide the drone to land.

[0167] In addition, the memory 11 may include a high-speed random access memory and may also include a non-volatile memory, such as at least one disk storage device or other volatile solid-state storage device.

[0168] The communication interface 12 may be an interface of a communication module, and is used to connect to other devices or systems.

[0169] Of course, it needs to be explained that Figure 4 The structure shown does not constitute a limitation on the drone rejection and guidance landing equipment in the embodiment of the present application. In actual application, the drone rejection and guidance landing equipment may include Figure 4 More or fewer components than shown, or combinations of certain components.

[0170] An embodiment of the present application may also provide a computer-readable storage medium, wherein the computer-readable storage medium is used to store program code, and the program code is used to execute the steps of the above-mentioned drone denied guided landing method.

[0171] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply the existence of any such actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or device. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or device comprising the element.

[0172] Through the description of the above embodiments, it can be seen that those skilled in the art can clearly understand that the present application can be implemented by means of software plus a necessary general hardware platform. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which can be stored in a storage medium such as ROM / RAM, a magnetic disk, an optical disk, etc., and includes a number of instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in various embodiments or certain parts of the embodiments of the present application.

[0173] Each embodiment in this specification is described in a progressive manner. The same or similar parts between the embodiments can be referred to each other. Each embodiment focuses on 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. For relevant parts, refer to the partial description of the method embodiment. The system and system embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. A person of ordinary skill in the art can understand and implement it without expending creative work.

[0174] The above description is only a preferred embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention are included in the scope of protection of the present invention.

Claims

1. A method for denying and guiding landing of a drone, characterized in that: include: After the drone is determined to be hovering above the intended landing site, an image in the hovering state is acquired using a visual sensor, and a reference image is selected from the image in the hovering state using mean square error and peak signal-to-noise ratio; performing grayscale processing and Gaussian filtering on the reference image, and continuously performing grayscale processing and Gaussian filtering on the image during the landing process collected by the visual sensor; Correcting the image during the landing process by using inertial navigation data to obtain a corrected image; Scaling the corrected image to obtain a scaled image; Matching and positioning the scaled image with the reference image using a sliding window method to determine the position coordinates of the center of the scaled image in the reference image; Generate pseudo GPS coordinates using the position coordinates, and simultaneously use a random sampling consistency algorithm to estimate the height above the ground to obtain a height estimate value; The pseudo GPS coordinates and the altitude estimate are used to guide the drone to land.

2. The method for denying and guiding landing of a UAV according to claim 1, characterized in that: The reference image selection method includes: Determine that the pitch angle, roll angle, and relative height change are all less than the corresponding set thresholds; Calculate the mean square error (MSE) of adjacent images of 3×FPS frames as follows: Where: I(i,j) and K(i,j) represent the two adjacent frames of images, m and n represent the width and height of the image respectively; A target image is randomly selected from the 3×FPS frame adjacent images, and the peak signal-to-noise ratio (PSNR) is calculated as follows: If it is determined that the PSNR is greater than or equal to a preset threshold, the target image is determined to be a reference image.

3. The method for denying and guiding landing of a UAV according to claim 1, characterized in that: The grayscale processing and Gaussian filtering processing include: The weighted average method is used for grayscale processing, as shown in the following formula: Gray=0.299R+0.587G+0.144B In the formula, Gray is the grayscale image, and R, G, and B are the three-channel components respectively; 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 and guiding landing of a UAV according to claim 1, characterized in that: Correcting the image during the landing process by using inertial navigation data to obtain a corrected image includes: The transformation matrix between the navigation system and the drone body coordinate system is calculated as follows: Where: Ψ, θ, and γ are the heading angle, pitch angle, and roll angle of the UAV respectively; The conversion matrix from the visual sensor coordinate system to the body coordinate system is calculated as follows: Where: are the relative postures of the visual sensor and the inertial navigation on the three coordinate axes; The transformation matrix from the reference image coordinate system to the navigation coordinate system is calculated as follows: Calculate the correction map of the vision sensor as follows: Where, is the correction image, K is the internal reference of the visual sensor after calibration, and U is the uncorrected image after grayscale conversion.

5. The method for denying and guiding landing of a UAV according to claim 1, characterized in that: Scaling the corrected image in equal proportions includes: Determine the scaling factor k as follows: Where, is the initial height of the UAV when it lands, h t is the relative height at the current moment; Correction image by scaling k Perform proportional scaling to obtain the scaled image.

6. The method for denying and guiding landing of a UAV according to claim 1, characterized in that: Matching and positioning the scaled image with the reference image using a sliding window method includes: Determine the sliding step size as follows: step w =w0 / 8 step h =h0 / 8 Where, step w 、step h are the sliding steps in width and height respectively, w and h are the width and height of the template image respectively; Feature detection is performed using SURF feature detector; The FLANN feature matching method is used for feature matching, and the method for selecting feature points is the ratio test; Use RANSAC to remove abnormal matching points and generate a more stable homography matrix; The matching result is judged by the average matching distance, and the lowest value is taken as the optimal matching result. The calculation is as follows: Where, is the average matching distance, N is the number of matching feature points, d i is the distance of the i-th matching point; Through the optimal matching results, we can get The position coordinates of the image center in the reference image (P x 、P y ).

7. The method for denying and guiding landing of a UAV according to claim 6, characterized in that: Generating pseudo GPS coordinates using the location coordinates includes: Using the position coordinates (P x 、P y )calculate Pseudo GPS coordinates of the image (L0, L a ),as follows: Among them, t x , t y are the assumed GPS coordinates of the reference image, and level is the tile level.

8. The method for denying and guiding landing of a UAV according to claim 7, characterized in that: The height estimation value obtained by using the random sampling consistency algorithm for ground height estimation includes: Height estimation is performed using auxiliary point cloud information provided by radar, binocular camera, and depth camera. Height estimation uses a random sampling consistency algorithm to fit the ground point cloud, including the following steps and methods: Initialization parameters, including a maximum number of iterations, an inlier threshold, and an inlier number threshold; Randomly sample 3 points and calculate the plane equation; Calculate the distances of all points to the plane and determine the interior points; Update the best plane; Repeat the iteration until the termination condition is met and the optimal plane equation and height above the ground are estimated.

9. The method for denying and guiding landing of a UAV according to claim 1, characterized in that: Guiding the drone to land using the pseudo GPS coordinates and the altitude estimate includes: When the altitude above the ground is 0 or less than the altitude threshold, the landing is completed.

10. A drone denial guidance landing device, characterized in that: The device is used to execute the method for denying and guiding landing of a drone according to any one of claims 1 to 9, comprising: A standard image selection unit is used to determine an image of the drone in a hovering state acquired by using a visual sensor after the drone hovers above a target landing site, and to select a reference image from the image in the hovering state using mean square error and peak signal-to-noise ratio; a grayscale and filtering processing unit, configured to perform grayscale processing and Gaussian filtering on the reference image, and continuously perform grayscale processing and Gaussian filtering on the image during the landing process acquired by the visual sensor; an image correction unit, configured to correct the image during the landing process using inertial navigation data to obtain a corrected image; An image scaling unit, configured to scale the corrected image in equal proportion to obtain a scaled image; A sliding window feature matching unit, configured to match and locate the scaled image with the reference image using a sliding window method to determine the position coordinates of the center of the scaled image in the reference image; a pseudo GPS coordinate and height estimation generating unit, configured to generate pseudo GPS coordinates using the position coordinates and simultaneously estimate the height above the ground using a random sampling consistency algorithm to obtain a height estimation value; A landing guidance unit is used to guide the UAV to land using the pseudo GPS coordinates and the altitude estimation value.

Images