Resolution method for relative pose of unmanned aerial vehicle

Abstract

The invention relates to the technical field of image recognition, in particular to an unmanned aerial vehicle relative pose resolving method, which comprises the following steps of: constructing a mapping relation from a world coordinate system to an image pixel coordinate system based on an imaging model and internal and external parameters of a camera; the attitude angles, including a yaw angle, a pitch angle and a roll angle, of the unmanned aerial vehicle relative to the airport runway are estimated by using the pixel coordinates of the left and right sidelines and the vanishing points of the runway in the image in combination with the airport runway structure model; based on the attitude angle estimation result and the camera projection geometrical relationship, establishing an equation set, and solving the vertical height and lateral deviation position parameters of the unmanned aerial vehicle relative to the runway by combining a sideline pixel linear equation in the image and a three-dimensional linear equation of the runway boundary. According to the method, a unified mapping model from a world coordinate system to an image pixel coordinate system is established, an obtained attitude angle is combined with a linear relation between images of left and right side lines of a runway and the world, a linear equation set is constructed, and the vertical height and the lateral deviation distance of the unmanned aerial vehicle are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of image recognition, in particular to a method for calculating the relative posture of an unmanned aerial vehicle (UAV). Background Art

[0002] As drones become increasingly autonomous, their ability to accurately land in complex environments has become a key technology that requires breakthroughs. Traditional positioning methods that rely on the Global Navigation Satellite System (GNSS) face the risk of failure in airports with GNSS denial or signal obstruction.

[0003] Existing visual localization methods are mostly based on feature point matching, global map construction, or multi-frame motion estimation. These methods are computationally complex and highly dependent on the environment, making them unsuitable for applications with significant geometric prior structures, such as runway scenes. On the other hand, while inertial navigation is fast, it suffers from the problem of error accumulation and divergence over time.

[0004] To address these issues, some studies have attempted to incorporate image midline features, such as edges, vanishing points, and horizon lines, to aid positioning. However, existing solutions lack a unified, clear, and practical mathematical modeling approach for effectively matching image line structures with the 3D runway model and constructing pose constraints from linear projections in the image. This makes it difficult to ensure solution accuracy and stability. Summary of the Invention

[0005] To solve the above problems, the present invention proposes a method for solving the UAV's relative runway posture based on image centerline features. By establishing a unified mapping model from the world coordinate system to the image pixel coordinate system, combined with the pixel deviation relationship between the vanishing point and the image center point, and the slope information of the straight line in the image, the yaw, pitch and roll angles of the UAV relative to the runway are estimated respectively. The obtained attitude angles are then combined with the image of the left and right sidelines of the runway and the world straight line relationship to construct a system of linear equations and solve the vertical height and lateral deviation distance of the UAV.

[0006] The present invention is achieved through the following technical solutions:

[0007] A method for calculating the relative posture of a drone, comprising the following steps:

[0008] S1. Based on the imaging model and the internal and external parameters of the camera, a mapping relationship between the world coordinate system and the image pixel coordinate system is constructed;

[0009] S2. Using the pixel coordinates of the left and right edges of the runway and the vanishing point in the image, combined with the airport runway structure model, estimate the attitude angle of the UAV relative to the airport runway, the attitude angle including the yaw angle, the pitch angle, and the roll angle;

[0010] S3. Based on the attitude angle estimation result and the camera projection geometry, a set of equations is established. The equations of the edge pixel lines in the image and the three-dimensional line equations of the runway boundary are combined to solve the vertical height and lateral deviation position parameters of the UAV relative to the runway.

[0011] Furthermore, constructing the mapping relationship in S1 includes the following sub-steps:

[0012] S101, transforming the three-dimensional feature points in the world coordinate system into the camera coordinate system through the rotation matrix and translation vector to form a camera extrinsic parameter model;

[0013] S102, projecting the points in the camera coordinate system to the image physical coordinate system based on the focal length of the camera and the imaging geometry;

[0014] S103, converting the point coordinates in the image physical coordinate system to the image pixel coordinate system based on the pixel scale and the image center position, completing the mapping from the 3D world point to the 2D image point;

[0015] Furthermore, the estimation of the yaw angle in S2 is calculated based on the horizontal deviation between the vanishing point in the image and the center point of the image.

[0016] Furthermore, the estimation of the pitch angle in S2 is calculated based on the vertical deviation between the vanishing point in the image and the center point of the image.

[0017] Furthermore, the estimation of the roll angle in S2 is calculated based on the slope of the runway start line or the fitted horizon in combination with the camera model.

[0018] Furthermore, in the roll angle estimation, the horizon is constructed with the vanishing point as the center to extract the image edge features and perform linear fitting.

[0019] Furthermore, in S3, the attitude angle is substituted into the camera extrinsic parameter model to establish a projection correspondence between the left and right side lines of the runway in the image and the boundary straight line of the runway in the three-dimensional world, and a linear equation group is constructed for solution.

[0020] Furthermore, the linear equations include constraints between straight line equations in at least two images and edge parameters in the corresponding world system, which are used to jointly solve the vertical height and lateral deviation distance of the drone.

[0021] Beneficial effects of the present invention:

[0022] (1) The present invention proposes a method for solving the relative posture of a UAV, which does not require GNSS information and can complete the joint solution of posture and position by relying only on line features and camera parameters in a single-frame image. It is suitable for GNSS-denied environments.

[0023] (2) The present invention proposes a method for solving the relative pose of a UAV, which utilizes clear imaging projection relationships and geometric constraints to avoid highly complex visual SLAM calculations. The solution chain is clear and is suitable for deployment in embedded flight control systems.

[0024] (3) The present invention proposes a method for solving the relative posture of UAVs, which makes full use of the geometric prior of the airport runway structure, combines visual perception with the characteristics of the engineering environment, and improves the accuracy and robustness of posture and position estimation.

[0025] (4) The present invention proposes a method for solving the relative posture of a UAV, which can select adaptive image features (starting line or local horizon) to estimate the roll angle during the UAV approach, pull-up and taxiing stages, thereby improving the continuity of posture estimation. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0027] Figure 1 This is a flow chart of a method for calculating the relative posture of a UAV proposed in the present invention;

[0028] Figure 2 A schematic diagram of the coordinate system of a drone runway landing scene for a drone relative pose calculation method proposed in the present invention;

[0029] Figure 3 This is a schematic diagram of the angle relationship between the world coordinate system and the navigation coordinate system of a method for solving the relative posture of a UAV proposed in the present invention;

[0030] Figure 4 This is a schematic diagram of a pinhole imaging model for a method for solving the relative pose of a UAV proposed in the present invention;

[0031] Figure 5 A schematic diagram of a UAV runway landing airport model for a UAV relative posture solution method proposed in the present invention;

[0032] Figure 6 A schematic diagram of a terminal device for a method for calculating relative posture of a UAV proposed in the present invention;

[0033] Figure 7 A schematic diagram of a readable storage medium for a method for calculating relative posture of a UAV proposed in the present invention;

[0034] In the figure, 200 - terminal device, 210 - memory, 211 - RAM, 212 - cache memory, 213 - ROM, 214 - program / utility, 215 - program module, 220 - processor, 230 - bus, 240 - external device, 250 - I / O interface, 260 - network adapter, 300 - program product. DETAILED DESCRIPTION

[0035] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with examples and drawings. The exemplary embodiments of the present invention and their descriptions are only used to explain the present invention and are not intended to limit the present invention.

[0036] Example 1

[0037] refer to Figure 1 The relative pose solution method of a UAV proposed in this embodiment is suitable for the landing navigation mission of a UAV in a GNSS-denied environment.

[0038] First, a coordinate system consisting of the navigation coordinate system, the world coordinate system, the body coordinate system, the camera coordinate system, the image physical coordinate system, and the image pixel coordinate system was established. The origin of the world coordinate system was set at the intersection of the runway centerline and the starting line. The camera was fixed to the nose of the drone, looking forward and downward to capture the runway scene. The image projection relationship was established based on the pinhole imaging model.

[0039] After the drone camera acquires an image, it extracts features such as the left and right runway edges, the starting line, and the vanishing point based on a detection algorithm. Camera calibration is then performed to obtain the camera's intrinsic parameter matrix K and extrinsic parameter matrix [R|T]. According to the imaging model, the coordinates of the runway boundary points in the world coordinate system are transformed to the camera coordinate system and then projected to the image pixel coordinate system via the intrinsic parameter matrix, completing the mapping process from three-dimensional points to pixels on the image plane. By reading the horizontal and vertical deviations between the vanishing point position and the image center, combined with the runway's true heading angle, the yaw and pitch angles are calculated, respectively. The drone's roll angle is calculated based on the slope of the runway starting line or horizon in the image. In the late landing phase, a localized horizon fitting method centered on the vanishing point is used to improve the robustness of the roll angle estimation.

[0040] After obtaining the attitude angles, the constructed rotation matrix is ​​combined with the camera projection model to construct a system of constraint equations by substituting the pixel line equations of the runway edge in the image and the world coordinate equations of the actual left and right runway edges. Solving this linear system determines the UAV's lateral deviation distance relative to the runway centerline and its vertical height relative to the runway plane, enabling a precise estimation of the UAV's current position.

[0041] This embodiment combines image geometric features with the camera model to complete the conversion from two-dimensional image features to three-dimensional relative pose parameters, constructing a complete data flow and solution link, which is suitable for autonomous landing navigation of drones in scenarios without external navigation sources.

[0042] Example 2

[0043] This embodiment proposes a specific implementation method of a method for solving the relative posture of a drone based on Embodiment 1.

[0044] Specifically include:

[0045] refer to Figure 2 ,According to the imaging characteristics of the UAV looking forward and downward, a landing navigation system model is established, which includes the navigation coordinate system, the world coordinate system, the body coordinate system, the camera coordinate system, the image physical coordinate system and the image pixel coordinate system.

[0046] The navigation coordinate system is defined as D -X D Y D Z D , coordinate origin O b Taken from the initial starting point, X D Along the horizontal plane and pointing due east, Y D Pointing to due north in the horizontal plane, Z D Perpendicular to X D Y D The horizontal plane is pointing towards the sky.

[0047] The world coordinate system is defined as O w -X W Y W Z W , the coordinate system is established on the runway plane, with the origin coinciding with the intersection of the runway centerline and the starting line, X W Along the runway centerline, with forward direction positive, use the right-hand rule to obtain Y W Axis, coincides with the start line of the runway, right is positive, Z W Perpendicular to X W Y W For a flat surface, positive is upward.

[0048] refer to Figure 3 , X of the world coordinate system W With the navigation coordinate system X D The angle α formed between them is the true heading of the world coordinate system.

[0049] The body coordinate system is defined as O b -X b Y b Z b , coordinate origin O bAt the center of mass of the aircraft, X b The axis coincides with the body axis, Z b The axis is perpendicular to the body and points downward. The right-hand rule can be used to determine the axis perpendicular to the X b Axis, Z b Y axis b axis.

[0050] The camera coordinate system is defined as O c -X c Y c Z c , coordinate origin O c Coincident with the optical center of the camera, the main optical axis is Z c Axis, the positive direction is from the optical center to the principal point of the image, the camera coordinate system X c Axis and Y c The axes are parallel to the u and v axes of the image pixel coordinate system.

[0051] The physical coordinate system of the image is defined as I-xy, and the origin of the coordinate is placed in the camera coordinate system Z c The x-axis is the horizontal direction of the image, and the y-axis is the vertical direction of the image.

[0052] The image pixel coordinate system is defined as o-uv, the coordinate origin is defined at the upper left corner of the image, the image horizontal is the u axis, which is parallel to the x axis in the image physical coordinate system, and the image vertical is the v axis, which is parallel to the y axis in the image physical coordinate system.

[0053] This embodiment maps the coordinate points in the real three-dimensional world to the two-dimensional image plane through the pinhole imaging model. Figure 4 , O c The camera projects point P in the three-dimensional world to point P on the two-dimensional image plane through this point. c ′, based on the pinhole imaging principle, the imaging point P c ′ is an inverted real image. By inversion and translation, the imaging plane can be equivalent to the equivalent imaging plane, and the imaging point P is equivalent to P c , thus obtaining the real point P in the three-dimensional world and the corresponding point P on the two-dimensional image c The mapping relationship.

[0054] Based on the world coordinate system, let the coordinates of point P in the world coordinate system be (x w ,y w , z w ), the coordinates in the camera coordinate system are (x c ,y c , z c ), the point is mapped to the projection point P after imaging by the imaging model c The coordinates are (x, y), and the following relationship is obtained:

[0055] Where f represents the distance from the optical center of the camera to the imaging plane, which is called the focal length of the camera.

[0056] The conversion formula from the image physical coordinate system to the image pixel coordinate system is:

[0057] Where u0 and v0 represent the coordinates of the origin in the image physical coordinate system in the image pixel coordinate system, and dx and dy represent the mapping scale from the image physical coordinate system to the image pixel coordinate system. This value is related to the intrinsic parameters of the camera.

[0058] The camera’s intrinsic parameter model is obtained through the above formula, and its matrix form is expressed as:

[0059] in Indicates the equivalent focal length f of the camera x , f y , so the intrinsic parameter matrix of the camera is:

[0060]

[0061] The camera's external parameter model is the transformation of a point in the world coordinate system to the camera coordinate system through the rotation matrix and translation matrix. The relationship is as follows:

[0062] Where R′ represents the orthogonal rotation transformation matrix, and The translation matrix T is expressed as [t x t y t z ] is the coordinate of the origin in the world coordinate system in the camera coordinate system. At the same time, according to the attitude angle, the attitude transfer matrix of the drone from the world coordinate system to the camera coordinate system can be calculated. The world coordinate system can be converted from the camera coordinate system into The order of rotation is obtained, so:

[0063] in,

[0064]

[0065] During the drone landing process, the airport runway will appear as a triangle in the forward-looking visual imaging device. Figure 5 In this embodiment, the runway starting line l is defined s Intersects the left runway line at point A(u A , v A ), its coordinate in the world coordinate system is (x A ,y A , zA ), runway starting line l s Compared with the right runway line at point B (u B , v B ), its coordinate in the world coordinate system is (x B ,y B , z B ), the extension lines of the left and right runway lines intersect at the vanishing point C(u C , v C ), its point in the world coordinate system is (x C ,y C , z C ), while the runway centerline l m It will also pass through point C. The horizontal deviation between the vanishing point C and the center of the image is recorded as Δu (unit: pixel), and the vertical deviation between the vanishing point C and the center of the image is recorded as Δv (unit: pixel). The width of the runway is 2d, then the runway starting line l s The distance from the midpoint to the left and right endpoints is d.

[0066] Get the straight line equations of the left and right runway lines of the airport runway in the world coordinate system:

[0067]

[0068] The equations of the three side lines and the center line of the airport runway in the pixel coordinate system are:

[0069] k i u+v+b i =0(i=l, r, s, m); where u and v represent the horizontal and vertical coordinates in the pixel coordinate system, k i and b i are the slope and intercept of the straight line equation, respectively. (i=l, r, s, m) represent the left line, right line, starting line, and center line of the runway, respectively. A straight line can be represented by a three-dimensional vector:

[0070] l i =[k i ,1,b i ] T (i=l, r, s, m);

[0071] Converting the above equation into matrix form yields:

[0072] in Represents the homogeneous equation of a pixel in the image pixel coordinate system.

[0073] In this embodiment, the left and right runway edges can be accurately detected in real time during the approach, take-off and taxiing phases of the UAV through the above detection method. According to the definition of the airport runway model, the coordinates of the vanishing point are marked as C(uC , v C ), by reading the internal and external parameters of the camera, the size of the visual image is obtained, thereby determining the values ​​of the horizontal deviation Δu and the vertical deviation Δv.

[0074] For the relative yaw angle, the relative yaw angle between the airport runway and the aircraft is reflected in the horizontal deviation Δu between point C and the center point of the image during imaging. The yaw angle is defined as positive to the right. Therefore, when the yaw angle of the drone is to the right, the position of point C in the image is biased to the left, and the relative yaw angle calculation formula is obtained.

[0075]

[0076] For the relative pitch angle, the relative pitch angle between the airport runway and the aircraft is reflected in the vertical deviation Δv between point C and the center point of the image during imaging. The upward direction is positive when the pitch angle is defined. Therefore, when the pitch angle of the drone is upward, the position of point C in the image is lower. The formula for calculating the relative pitch angle is:

[0077]

[0078] For the relative roll angle, in the middle period before the UAV lands, the runway starting line l is passed when the imaging is performed. s Or the slope of the sea-sky horizon to obtain the formula for the relative roll angle:

[0079] φ=arctan(k s );

[0080] In the later stage of UAV landing, the local area near the runway is selected for feature extraction and horizon fitting. The area is centered at the vanishing point C, and the length and width are adaptively adjusted according to the current visual size. The area is defined as area D. The obtained local horizon is represented by l d The calculation formula of the relative roll angle is:

[0081] φ=arctan(k d ).

[0082] Through the above calculations, the attitude angle of the UAV relative to the airport runway can be solved, and the visually estimated yaw angle of the UAV can be obtained by calculating the relative yaw angle and the heading angle of the local airport runway. The visually estimated pitch angle and roll angle of the UAV are the relative pitch angle and relative roll angle.

[0083] Translate a point P in three-dimensional space from the homogeneous coordinates of the world coordinate system Homogeneous coordinates projected onto the image pixel coordinate system Construct the equation constraint, the formula is:

[0084]

[0085] Where K is the internal parameter matrix, It is the rotation matrix from the world coordinate system to the body coordinate system, obtained based on the attitude value solved previously. is the rotation matrix from the body coordinate system to the camera coordinate system, T is the translation matrix, and is the translation vector of the world coordinate system, expressed as [t x t y t z ], P w is the point coordinate in the world coordinate system, and the expanded formula is:

[0086] in

[0087] The rotation matrix representing the camera in the world coordinate system is the camera's extrinsic parameter. In this embodiment, the camera is fixed to the drone head and there is no lever arm error, which is an ideal environment. The result is:

[0088]

[0089] Multiply the above formula by [k i 1 b i ]get:

[0090]

[0091] in

[0092] A i =[k i f x f y k i u0+v0+b i 0]R,i=r,l, in the world coordinate system, the left and right boundaries of the airport runway are:

[0093] A 13 x w -A 11 x y +A 11 t x +A 12 x1+A 13 t z =0

[0094] A 23 x w -A 21 x y +A 21 t x +A 22 x y +A 23 tz =0;

[0095] Substituting the equations of the left and right runway lines of the airport runway in the world coordinate system and comparing the individual terms in the above equation, we get:

[0096] A 13 =A 23 =0

[0097] t x +(A 12 / A 11 )t y +(A 13 / A 11 )t z =d

[0098] t y +(A 22 / A 21 )t y +(A 23 / A 21 )t z =-d;

[0099] Convert the above formula into matrix form:

[0100]

[0101] Solve to get t x and t y , where t x Indicates the distance the drone deviates from the runway centerline, i.e., the side deviation, t y Indicates the aircraft's altitude relative to the runway plane.

[0102] Example 3

[0103] like Figure 6 Based on Example 1, this embodiment proposes a terminal device for a method for solving a relative posture of a drone. The terminal device 200 includes at least one memory 210, at least one processor 220, and a bus 230 connecting different platform systems.

[0104] The memory 210 may include a readable medium in the form of a volatile memory, such as a RAM 211 and / or a cache memory 212 , and may further include a ROM 213 .

[0105] The memory 210 also stores a computer program that can be executed by the processor 220, so that the processor 220 executes any one of the above-mentioned methods for solving the relative pose of a drone in the embodiments of the present application. The specific implementation method is consistent with the implementation method and the technical effect achieved in the embodiments of the above-mentioned application, and some of the contents are not repeated here. The memory 210 may also include a program / utility 214 having a set (at least one) of program modules 215. Such program modules include but are not limited to: an operating system, one or more application programs, other program modules, and program data. Each of these examples or some combination may include the implementation of a network environment.

[0106] Accordingly, the processor 220 may execute the aforementioned computer programs, as well as the program / utility 214 .

[0107] The bus 230 may represent one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor, or a local bus using any of a variety of bus architectures.

[0108] The terminal device 200 can also communicate with one or more external devices 240, such as keyboards, pointing devices, Bluetooth devices, etc., and can also communicate with one or more devices that can interact with the terminal device 200, and / or communicate with any device that enables the terminal device 200 to communicate with one or more other computing devices (such as routers, modems, etc.). Such communication can be carried out through the I / O interface 250. In addition, the terminal device 200 can also communicate with one or more networks (such as local area networks (LANs), wide area networks (WANs) and / or public networks, such as the Internet) through the network adapter 260. The network adapter 260 can communicate with other modules of the terminal device 200 through the bus 230. It should be understood that although not shown in the figure, other hardware and / or software modules can be used in conjunction with the terminal device 200, including but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, RAID systems, tape drives, and data backup storage platforms.

[0109] Example 4

[0110] This embodiment proposes a computer-readable storage medium for a method for solving the relative posture of a drone. Instructions are stored on the computer-readable storage medium. When the instructions are executed by the processor, the specific implementation method of implementing any of the above-mentioned methods for solving the relative posture of a drone is consistent with the implementation method and the technical effect achieved in the above-mentioned application embodiments, and some contents will not be repeated here.

[0111] Figure 7The program product 300 provided in this embodiment for implementing the above-mentioned application is shown. It can adopt a portable compact disc read-only memory (CD-ROM) and include program code, and can be run on a terminal device, such as a personal computer. However, the program product 300 of the present invention is not limited to this. In this embodiment, the readable storage medium can be any tangible medium that contains or stores a program, and the program can be used by or in conjunction with an instruction execution system, device or device. The program product 300 can adopt any combination of one or more readable media. The readable medium can be a readable signal medium or a readable storage medium. The readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of readable storage media (a non-exhaustive list) include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0112] A computer-readable storage medium may include a data signal transmitted in baseband or as part of a carrier wave, carrying readable program code. This transmitted data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. The readable storage medium may also be any readable medium other than a readable storage medium, which can transmit, transmit, or transfer a program for use by or in conjunction with an instruction execution system, apparatus, or device. The program code contained on the readable storage medium may be transmitted using any suitable medium, including but not limited to wireless, wired, optical cable, RF, etc., or any suitable combination thereof. The program code for performing the operations of the present invention may be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, C++, etc., as well as conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as a standalone software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server. Where a remote computing device is involved, the remote computing device may be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (e.g., through the Internet using an Internet service provider).

[0113] The basic principles, main features, and advantages of the present invention are shown and described above. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions are merely illustrative of the principles of the present invention. Various changes and modifications may be made to the present invention without departing from the spirit and scope of the present invention. Such changes and modifications are intended to fall within the scope of the present invention. The scope of protection claimed in the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for calculating the relative posture of a UAV, characterized in that: The following steps are involved: S1. Based on the imaging model and the internal and external parameters of the camera, a mapping relationship between the world coordinate system and the image pixel coordinate system is constructed; S2. Using the pixel coordinates of the left and right edges of the runway and the vanishing point in the image, combined with the airport runway structure model, estimate the attitude angle of the UAV relative to the airport runway, the attitude angle including the yaw angle, the pitch angle, and the roll angle; S3. Based on the attitude angle estimation result and the camera projection geometry, a set of equations is established. The equations of the edge pixel lines in the image and the three-dimensional line equations of the runway boundary are combined to solve the vertical height and lateral deviation position parameters of the UAV relative to the runway.

2. A method for calculating relative posture of a drone according to claim 1, characterized in that: The construction of the mapping relationship in S1 includes the following sub-steps: S101, transforming the three-dimensional feature points in the world coordinate system into the camera coordinate system through the rotation matrix and translation vector to form a camera extrinsic parameter model; S102, projecting the points in the camera coordinate system to the image physical coordinate system based on the focal length of the camera and the imaging geometry; S103 , converting the point coordinates in the image physical coordinate system to the image pixel coordinate system based on the pixel scale and the image center position, thereby completing the mapping from the three-dimensional world point to the two-dimensional image point.

3. The method for calculating relative posture of a drone according to claim 1, wherein: The estimation of the yaw angle in S2 is calculated based on the horizontal deviation between the vanishing point in the image and the center point of the image.

4. The method for calculating relative posture of a drone according to claim 1, wherein: The estimation of the pitch angle in S2 is calculated based on the vertical deviation between the vanishing point in the image and the center point of the image.

5. The method for calculating relative posture of a UAV according to claim 1, wherein: The roll angle estimation in S2 is calculated based on the slope of the runway start line or the fitted horizon in combination with the camera model.

6. The method for calculating relative posture of a UAV according to claim 5, characterized in that: The horizon described in the roll angle estimation is constructed by taking the vanishing point as the center to extract the image edge features and perform linear fitting.

7. The method for calculating relative posture of a UAV according to claim 1, characterized in that: In S3, the attitude angle is substituted into the camera external parameter model to establish a projection correspondence between the left and right side lines of the runway in the image and the boundary straight line of the runway in the three-dimensional world, and a linear equation group is constructed for solution.

8. The method for calculating relative posture of a UAV according to claim 7, characterized in that: The linear equation group includes constraints between straight line equations in at least two images and edge parameters in the corresponding world system, which are used to jointly solve the vertical height and lateral deviation distance of the drone.

Images