Visual inertia integrated positioning assisted aircraft take-off and landing method, equipment and medium

Abstract

The invention discloses a visual inertia integrated positioning assisted aircraft take-off and landing method, equipment and a medium, and relates to the field of aircraft take-off and landing control and adjustment. The method comprises the following steps: capturing and identifying a target area to obtain the position of the target area; a plurality of measurement parameters of the target area are obtained by analyzing the incidence relation of conversion from the three-dimensional position to the two-dimensional image; determining the position of the aircraft based on the plurality of measurement parameters of the target area by using an extended Kalman filter; and according to the position of the aircraft and the position of the target area, adjusting the flight speed and attitude of the aircraft to assist the aircraft in taking off and landing. According to the invention, stable and accurate autonomous take-off and landing of the aircraft can be economically and efficiently realized.

Description

Technical Field

[0001] The present application relates to the field of aircraft takeoff and landing control and regulation, and particularly to a method, device and medium for assisting aircraft takeoff and landing using visual inertial combined positioning. Background Art

[0002] With the continuous development and wide application of aircraft technology, its autonomous takeoff and landing ability has become increasingly important. Autonomous takeoff and landing can enable the aircraft to stably and accurately take off and land in the target area without human intervention, greatly improving the autonomy, flexibility and adaptability of the aircraft. At the same time, it can also reduce the risks and costs of human intervention. In addition, it directly affects the success or failure of the aircraft to perform tasks. At present, the main factors restricting the accurate autonomous takeoff and landing of aircraft are the complexity of the landing scene and the susceptibility of traditional navigation methods to electronic interference, resulting in poor accuracy of autonomous takeoff and landing. Summary of the Invention

[0003] The purpose of the present application is to provide a method, device and medium for assisting aircraft takeoff and landing using visual inertial combined positioning, which can economically and efficiently achieve stable and accurate autonomous takeoff and landing of the aircraft, and thus solve the existing problem of poor accuracy of autonomous takeoff and landing.

[0004] To achieve the above purpose, the present application provides the following solutions:

[0005] In a first aspect, the present application provides a method for assisting aircraft takeoff and landing using visual inertial combined positioning, including:

[0006] Capturing and identifying a target area to obtain the position of the target area;

[0007] By analyzing the correlation between the three-dimensional position and the two-dimensional image, obtaining multiple measurement parameters of the target area; the multiple measurement parameters include: the square root of the area of the target area, the position of the center point of the target area in the x-axis direction in the two-dimensional image, and the position of the center point of the target area in the y-axis direction in the two-dimensional image;

[0008] Using an extended Kalman filter to determine the position of the aircraft based on the multiple measurement parameters of the target area;

[0009] According to the position of the aircraft and the position of the target area, adjusting the flight speed and attitude of the aircraft to assist the aircraft in taking off and landing.

[0010] Optionally, capturing and identifying a target area to obtain the position of the target area includes:

[0011] Using two monocular cameras to capture environmental images;

[0012] Based on the environmental images, identifying the target area to obtain the position of the target area.

[0013] Optionally, the width of the target area satisfies the following relationship:

[0014]

[0015] In the formula, is the vector of the width of the target area, X0 is the distance from the camera plane to the target projection plane, f is the camera focal length, p is the pixel size in the monocular camera, θ2 is the angle between the direction from the aircraft to the target area and the optical axis direction, θ1 is the angle between the optical axis direction of the monocular camera and the vertical direction, and S is the width of the target area.

[0016] Optionally, by analyzing the correlation relationship between the three-dimensional position conversion to the two-dimensional image, multiple measurement parameters of the target area are obtained, including:

[0017] Obtain the position vector from the inertial coordinate system to the target area and the predicted position vector of the aircraft;

[0018] Use the difference between the position vector from the inertial coordinate system to the target area and the predicted position vector of the aircraft to determine the vector from the aircraft to the target area;

[0019] Convert the vector from the aircraft to the target area from the inertial coordinate system to the camera reference system;

[0020] Based on the image plane relationship projected by the monocular camera, determine multiple measurement parameters of the target area based on the vector from the aircraft to the target area after coordinate transformation.

[0021] Optionally, the extended Kalman filter includes: a process model and a measurement model; the process model is a dynamic helicopter model; the dynamic helicopter model includes a state estimate value and a covariance matrix;

[0022] The operations performed by the process model include:

[0023] Update the state estimate value using the derivative of the state vector obtained from the inertial measurement, and update the covariance matrix according to the differential Lyapunov equation;

[0024] Predict the aircraft state based on the updated state estimate value and the updated covariance matrix;

[0025] Use the position, pose, and area of the target area, combined with the predicted aircraft state, to generate the expected value of the measurement parameter;

[0026] The operations performed by the measurement model include:

[0027] Determine the relative position between the center of the target area and the aircraft based on the expected value of the measurement parameter to obtain the position of the aircraft.

[0028] Optionally, the linear relationship between the measurement model and the aircraft position in the inertial coordinate system is expressed as:

[0029]

[0030] P = (I - KC)P - ;

[0031] In the formula, f is the camera focal length, is the vector from the aircraft to the target area, is the predicted position vector of the aircraft, A is the given area of the target area, K is the Kalman gain, is the measurement vector, C is the Jacobian matrix of the measurement vector with respect to the state vector, P is the updated covariance matrix, and P - is the prior value of the covariance matrix, X is the x-axis coordinate value of the vector from the aircraft to the target area Y is the y-axis coordinate value of the vector from the aircraft to the target area Z is the z-axis coordinate value of the vector from the aircraft to the target area of.

[0032] Optionally, the linearized model of the measurement vector and the attitude quaternion in the aircraft reference frame is expressed as:

[0033]

[0034] In the formula, is the relative position vector between the center of the target area and the aircraft in the aircraft reference frame, is the attitude quaternion, and L cb is the rotation transformation matrix.

[0035] In a second aspect, the present application provides a computer device, including: a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor executes the computer program to implement the steps of the visual-inertial combined positioning-assisted aircraft takeoff and landing method provided above.

[0036] In a third aspect, the present application provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the visual-inertial combined positioning-assisted aircraft takeoff and landing method provided above are implemented.

[0037] According to the specific embodiments provided by the present application, the present application has the following technical effects:

[0038] The present application provides a method, device and medium for visual-inertial integrated positioning to assist the takeoff and landing of an aircraft. By capturing and identifying a target area and analyzing the correlation relationship between the three-dimensional position conversion to a two-dimensional image, multiple measurement parameters of the target area are obtained. Then, an Extended Kalman Filter (EKF), an economical component, is used. Based on the obtained multiple measurement parameters, the position of the aircraft can be determined. According to the position of the aircraft and the position of the target area, the flight speed and attitude of the aircraft are adjusted to assist the aircraft in taking off and landing, thereby enabling the aircraft to achieve stable and accurate autonomous takeoff and landing economically and efficiently, and solving the problem of poor accuracy of existing autonomous takeoff and landing. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required to be used in the embodiments. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0040] Figure 1 It is a schematic flowchart of a method for visual-inertial integrated positioning to assist the takeoff and landing of an aircraft provided by an embodiment of the present application;

[0041] Figure 2 It is a geometric schematic diagram of an aircraft and a target area provided by an embodiment of the present application;

[0042] Figure 3 It is a schematic diagram of the installation angles of two monocular cameras relative to an aircraft provided by an embodiment of the present application;

[0043] Figure 4 It is an imaging schematic diagram of a target area relative to a monocular camera provided by an embodiment of the present application;

[0044] Figure 5 It is a schematic diagram of the known position to a target area in an inertial coordinate system provided by an embodiment of the present application;

[0045] Figure 6 It is a schematic diagram of the structure of a computer device provided by an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0046] The following will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, rather than all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present application.

[0047] To make the above objects, features, and advantages of the present application more obvious and understandable, the present application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0048] In an exemplary embodiment, the present application provides a method for assisting the takeoff and landing of a vision-inertial integrated positioning assisted aircraft. This method is executed by a computer device, which can be specifically executed by a computer device such as a terminal or a server alone, or jointly executed by a terminal and a server. In the embodiments of the present application, this method is described by taking the application to a server as an example. The components required for the vision-inertial integrated aircraft assisted takeoff and landing method provided by the present application in actual application include: an aircraft body, an inertial navigation unit (IMU), a monocular camera, an image processor, and a flight control computer. Based on this, as Figure 1 shown, the vision-inertial integrated aircraft assisted takeoff and landing method provided by the present application includes:

[0049] Step 100: Capture and identify the target area to obtain the position of the target area. Among them, when the aircraft starts to prepare for takeoff and landing, the monocular camera is used to capture and identify the target area.

[0050] Step 101: Obtain multiple measurement parameters of the target area by analyzing the correlation relationship between the three-dimensional position and the two-dimensional image. The multiple measurement parameters include: the square root α of the target area area, the position u of the center point of the target area in the x-axis direction in the two-dimensional image, and the position v of the center point of the target area in the y-axis direction in the two-dimensional image. Since the position and area of the target area are known a priori, the vision-based estimator uses an extended Kalman filter and the three measurement parameters of the target area to solve the position of the aircraft.

[0051] Step 102: Use an extended Kalman filter to determine the position of the aircraft based on multiple measurement parameters of the target area.

[0052] Step 103: Adjust the flight speed and attitude of the aircraft according to the position of the aircraft and the position of the target area to assist the aircraft in taking off and landing. The aircraft is stably and accurately taken off and landed in the target area.

[0053] In another exemplary embodiment of the present application, the takeoff and landing process of the aircraft starts from a high-altitude environment at a certain distance. To use the inertial / vision integrated navigation and positioning technology to assist the aircraft in achieving accurate takeoff and landing, it is necessary to use a vision sensor to identify the information of the target area from the preparation for takeoff and landing. Therefore, it is assumed that the initial environmental conditions are a horizontal distance of 20 km and a vertical distance of 5 km from the target area.

[0054] Since the focal length of a monocular camera depends only on the image width (w) of the camera image in pixels and the field of view (FOV), the formula for its focal length is as follows:

[0055]

[0056] Assuming that the effective pixels of the monocular camera used are 1024 (H) × 1024 (V), the field of view angle is 40°, and the pixel size is 5 μm, its focal length is approximately 7 mm.

[0057] From Figure 2 it can be seen that the viewing angle θ of the aircraft to the target area is: Solving gives the viewing angle θ of approximately 76°. At the end of the aircraft takeoff and landing process, the viewing angle θ of the aircraft to the target area is approximately 0°. Therefore, it can be considered that the change range of the viewing angle θ of the aircraft to the target area during takeoff and landing is [0°, 76°]. According to the analysis of the change range of the viewing angle θ, it is preferable to use two monocular cameras to capture environmental images. The specific installation angles of the two monocular cameras relative to the aircraft are as Figure 3 shown. Among them, label 1 corresponds to the first monocular camera. Label 2 corresponds to the second monocular camera. The installation instructions for the two monocular cameras are as follows:

[0058] 1) The two monocular cameras are respectively fixedly connected to the aircraft.

[0059] 2) The angle between vector and vector and the angle between vector and vector respectively represent the field of view angle ranges of the two monocular cameras, where θ0 = 20°. Vector is perpendicular to the horizontal plane, and vectors and are parallel to each other.

[0060] 3) Vectors and respectively represent the normal vectors of the mirrors of the two monocular cameras.

[0061] Based on the above description, the implementation process of step 100 may include:

[0062] Step 1: Use two monocular cameras to capture environmental images.

[0063] Step 2: Identify the target area based on the environmental image to obtain the position of the target area.

[0064] In another exemplary embodiment of the present application, under the set initial environmental conditions, if one wants to accurately identify the target area, the area it occupies in the imaging image of the monocular camera should be at least greater than or equal to 10 pixels × 10 pixels. Therefore, it is necessary to analyze the size of the target area.

[0065] The imaging schematic of the target area relative to the monocular camera is as Figure 4 shown. Among them, the initial distance between the aircraft and the target area is C1, the angle between the direction from the aircraft to the target area and the optical axis direction is θ2, the angle between the optical axis direction of the monocular camera and the vertical direction is θ1, the camera focal length is f, and the pixel size in the monocular camera is p. Assume that the vector of the target area width is According to the imaging principle of the camera and the geometric relationship between the vector and the imaging plane, the determination formula for the target imaging width S' is as follows:

[0066]

[0067] In the formula, X0 is the distance from the camera plane to the target projection plane, and ΔY is the projected width of the target width on the target projection plane. The formulas for X0 and ΔY are respectively:

[0068] X0 = C · cos(θ2) (3)

[0069]

[0070] In the formula, C is the Jacobian matrix of the measurement vector relative to the state vector.

[0071] If one wants to accurately identify the target area, the target imaging width S' needs to be greater than or equal to 10 pixels, that is:

[0072]

[0073] Therefore, it can be deduced that the width of the target area needs to satisfy the following relationship:

[0074]

[0075] Based on the above content, the target area needs to be a square area greater than to ensure accurate identification of the target area.

[0076] In another exemplary embodiment of the present application, the method provided by the present application mainly involves three reference systems: the inertial reference system, the camera reference system, and the aircraft reference system. Among them, the inertial reference system is a local inertial reference system centered on the position of the aircraft control building on the ground. The camera reference system takes the principal point of the camera as the origin, and X cThe reference system with the axis along the optical axis of the camera. The aircraft reference system is the fuselage reference system centered on the center of mass of the aircraft. The vector components in different reference systems can be transformed as follows using the direction cosine matrix sequence:

[0077]

[0078] L ci = L cb L bi (9)

[0079] In the formula, L cb is a rotation transformation matrix that uses the pan angle ψ c and the pitch angle θ c to convert the vector in the aircraft reference system to the camera reference system. However, note that the conversion from the aircraft reference system to the camera reference system only considers the directional difference between the two reference systems and ignores the difference in the centers of the two reference systems. L bi is a standard rotation matrix from the aircraft to the inertial reference system, represented by the quaternions q2, q3, q1, q4.

[0080] In another exemplary embodiment of the present application, the present application mainly uses a vision-based extended Kalman filter (EKF) to maintain the estimation of the position and attitude of the aircraft, and the extended Kalman filter includes a process model and a measurement model.

[0081] The process model of the extended Kalman filter (EKF) is a dynamic helicopter model that uses 16 states: attitude quaternion, position and velocity components in the inertial reference system, accelerometer and gyroscope biases. During the process modeling stage of the EKF estimation algorithm, two main events occur:

[0082] The first event is to update the state estimate using the derivative of the state vector directly obtained from inertial measurements At the same time, the covariance matrix P is updated according to the differential Lyapunov equation. Based on this, the process model can be expressed as:

[0083]

[0084] In the formula, is the state estimate, is the non-linear aircraft model, P is the covariance matrix, A is the matrix representing the linearized aircraft dynamics, Q is the diagonal matrix representing the inherent process noise in the system model, and the initial value of the Q matrix is set by using a rough approximation derived from the modeling assumptions and can then be adjusted according to the data of subsequent flight tests. is the state estimate the derivative of with respect to t, is the derivative of the covariance matrix P with respect to t, and T is the transpose of the matrix.

[0085] In the second event, the aircraft uses the position, pose, and size of the known target area, combined with the aircraft state predicted by the dynamic process model, to generate the expected value of the measurement parameter. As Figure 5 shown, by taking the difference between the known position vector to the target area in the inertial coordinate system and the predicted position vector of the aircraft , the vector from the aircraft to the target area can be solved. Then the result can be transformed from the inertial reference frame to the camera reference frame, obtaining:

[0086]

[0087] According to the image plane relationship of monocular camera projection, the model of the measurement vector can be derived, and this measurement vector can be expressed as the following system of equations:

[0088]

[0089] In the formula, is the relative position vector of the target area with respect to the aircraft in the camera reference frame. The given area of the target area is represented by A, and X, Y, and Z are the x, y, and z axis coordinate values of the vector from the aircraft to the target area, is the normal vector of the target area derived from the known pose of the target area.

[0090] The EKF uses the measurement model to update the integration results of the process model given in formulas (10) and (11). The frequencies of the camera and magnetometer readings are both 10 Hz. In the update stage of state estimation, the Kalman gain is first calculated. This gain is used as a weight to fuse the actual camera measurement values with the predicted values given by the measurement model in formulas (13) - (15), thereby correcting the prior process model estimation value according to the following system of equations:

[0091] K = P - C T (CP - C T + V) -1 (16)

[0092]

[0093] P = (I - KC)P - (18)

[0094] Where K is the Kalman gain, V is a diagonal matrix representing the camera measurement noise, and C is the Jacobian matrix of the measurement vector with respect to the state vector, also denoted as while is the predicted vector given by the measurement model in Equations (13) - (15). The negative superscripts in the above Equations (16) - (18) represent the prior values obtained from the process model equations. The results of Equations (16) - (18) are used by the process model in the next time step to further propagate to the state vector and covariance matrix, and this process is repeated. According to the physical properties of the monocular camera, such as the focal length and size of the semiconductor photosensitive element (Charge Coupled Device, CCD) array, the constant value of the diagonal matrix V can be roughly estimated. These parameters can all be adjusted through the data of flight tests, but in order to prove the feasibility of the Extended Kalman Filter (EKF), the inaccuracies generated by using the initial approximation are considered acceptable.

[0095] It can be seen from Equations (16) and (18) that the EKF requires the Jacobian matrix C of the measurement vector with respect to the aircraft state vector to fuse the camera information and inertial data. The measurement value of the monocular camera is only affected by the position and attitude of the aircraft, so the partial derivatives with respect to other state variables (such as aircraft speed and accelerometer and gyroscope biases) are all zero. According to Equations (13) - (16), the linear relationship between the measurement model and the aircraft position in the inertial reference frame can be obtained as:

[0096]

[0097] P = (I - KC)P - (21)

[0098] Where P - is the prior value of the covariance matrix.

[0099] The linearized model of the measurement vector and the attitude quaternion in the aircraft reference frame is:

[0100]

[0101] Where is the relative position vector between the center of the target area and the aircraft in the aircraft reference frame, is the attitude quaternion.

[0102] According to the relative position vector between the center of the target area and the aircraft in the above aircraft reference frame, the flight speed and attitude of the aircraft can be precisely controlled, enabling the aircraft to take off and land stably and accurately in the target area.

[0103] In another exemplary embodiment of the present application based on the above description, the implementation process of step 101 includes:

[0104] Step 1: Obtain the position vector from the inertial coordinate system to the target area and the predicted position vector of the aircraft.

[0105] Step 2: Use the difference between the position vector from the inertial coordinate system to the target area and the predicted position vector of the aircraft to determine the vector from the aircraft to the target area.

[0106] Step 3: Convert the vector from the aircraft to the target area from the inertial coordinate system to the camera reference system.

[0107] Step 4: Based on the image plane relationship of monocular camera projection, determine multiple measurement parameters of the target area based on the vector from the aircraft to the target area after coordinate transformation.

[0108] In another exemplary embodiment of the present application, when analyzing the positioning accuracy of the method provided by the present application, according to the imaging principle of the monocular camera, the visual positioning accuracy mainly depends on the initial distance C1 and the included angle θ0. When the size of the target area in the imaging plane is greater than 10 pixels × 10 pixels, it can be considered that the target area can be accurately recognized, but the target area within 1 pixel in the imaging plane cannot be accurately distinguished. Therefore, the visual positioning accuracy is the width S0 of the target area represented by a single pixel, and its formula is as follows:

[0109]

[0110] According to the above positioning accuracy analysis, it can be seen that the visual positioning accuracy is related to the initial distance C1 of the target area, and the closer the distance, the better the accuracy. The typical cases are as follows:

[0111] When the included angle θ0 = 60° and the initial distance C1 from the target area is 20 km, a positioning accuracy of 30 meters can be achieved. When the included angle θ0 = 60° and the initial distance C1 from the target area is 200 m, a positioning accuracy of the meter level can be achieved.

[0112] In summary, the vision-inertial combined aircraft assisted takeoff and landing method provided by the present application uses a special landing scenario and has strong anti-interference ability, and can economically and efficiently achieve stable and accurate autonomous takeoff and landing of the aircraft, solves the problem of poor autonomous takeoff and landing accuracy of the aircraft system, and has a very high visual positioning accuracy.

[0113] In an exemplary embodiment, a computer device is provided. The computer device can be a server or a terminal, and its internal structure diagram can be as Figure 6As shown in the figure. The computer device includes a processor, a memory, an input / output interface (Input / Output, abbreviated as I / O), and a communication interface. Among them, the processor, the memory, and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store visual-inertial integrated positioning-assisted aircraft takeoff and landing data. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, it implements a method for visual-inertial integrated positioning-assisted aircraft takeoff and landing.

[0114] Those skilled in the art can understand that Figure 6 the structure shown in the figure is only a block diagram of some structures related to the solution of this application, and does not constitute a limitation on the computer device to which the solution of this application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine some components, or have different component arrangements. In an exemplary embodiment, a computer device is provided, including a memory and a processor. A computer program is stored in the memory, and when the processor executes the computer program, it implements the steps in the above method embodiments.

[0115] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, and when the computer program is executed by the processor, it implements the steps in the above method embodiments.

[0116] In an exemplary embodiment, a computer program product is provided, including a computer program, and when the computer program is executed by the processor, it implements the steps in the above method embodiments.

[0117] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use, and processing of relevant data need to comply with relevant regulations.

[0118] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, database, or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memories. Non-volatile memories can include read-only memory (ROM), magnetic tapes, floppy disks, flash memories, optical memories, high-density embedded non-volatile memories, resistive random access memories (ReRAMs), magnetoresistive random access memories (MRAMs), ferroelectric random access memories (FRAMs), phase change memories (PCMs), graphene memories, etc. Volatile memories can include random access memory (RAM) or external cache memories, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0119] The databases involved in the embodiments provided in the present application can include at least one of relational databases and non-relational databases. Non-relational databases can include distributed databases based on blockchain, etc., without limitation. The processors involved in the embodiments provided in the present application can be general-purpose processors, central processors, graphics processors, digital signal processors, programmable logics, data processing logics based on quantum computing, etc., without limitation.

[0120] The technical features of the above embodiments can be combined arbitrarily. For the sake of concise description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope described in this specification.

[0121] Specific examples are used in this article to elaborate on the principles and implementation manners of the present application. The descriptions of the above embodiments are only used to help understand the method and its core idea of the present application; at the same time, for those of ordinary skill in the art, according to the idea of the present application, there will be changes in the specific implementation manners and application scopes. In summary, the content of this specification should not be construed as a limitation to the present application.

Claims

1. A method for assisting the takeoff and landing of a vision-inertial integrated positioning assisted aircraft, characterized in that, The method for assisting the takeoff and landing of an aircraft using visual-inertial integrated positioning includes: Capturing and identifying a target area to obtain the position of the target area; By analyzing the correlation relationship between the three-dimensional position and the two-dimensional image, obtaining multiple measurement parameters of the target area; the multiple measurement parameters include: the square root of the area of the target area, the position of the center point of the target area in the x-axis direction in the two-dimensional image, and the position of the center point of the target area in the y-axis direction in the two-dimensional image; Using an extended Kalman filter to determine the position of the aircraft based on the multiple measurement parameters of the target area; According to the position of the aircraft and the position of the target area, adjusting the flight speed and attitude of the aircraft to assist the aircraft in taking off and landing.

2. The visual-inertial integrated positioning assisted aircraft takeoff and landing method according to claim 1, wherein Capturing and identifying a target area to obtain the position of the target area, including: Using two monocular cameras to capture environmental images; Based on the environmental images, identifying the target area to obtain the position of the target area.

3. The visual-inertial integrated positioning assisted aircraft takeoff and landing method according to claim 2, characterized in that The width of the target area satisfies the following relationship: In the formula, is the vector of the width of the target area, X0 is the distance from the camera plane to the target projection plane, f is the camera focal length, p is the pixel size in the monocular camera, θ2 is the angle between the direction from the aircraft to the target area and the optical axis direction, θ1 is the angle between the optical axis direction of the monocular camera and the vertical direction, and S is the width of the target area.

4. The visual-inertial integrated positioning assisted aircraft takeoff and landing method according to claim 2, wherein By analyzing the correlation relationship between the three-dimensional position and the two-dimensional image, obtaining multiple measurement parameters of the target area, including: Obtaining the position vector from the inertial coordinate system to the target area and the predicted position vector of the aircraft; Using the difference between the position vector from the inertial coordinate system to the target area and the predicted position vector of the aircraft to determine the vector from the aircraft to the target area; Converting the vector from the aircraft to the target area from the inertial coordinate system to the camera reference system; According to the image plane relationship of the monocular camera projection, determining the multiple measurement parameters of the target area based on the vector from the aircraft to the target area after coordinate transformation.

5. The method for assisting the takeoff and landing of a vision-inertial integrated positioning assisted aircraft according to claim 1, characterized in that, The extended Kalman filter includes: a process model and a measurement model; the process model is a dynamic helicopter model; the dynamic helicopter model includes a state estimate value and a covariance matrix; The operations performed by the process model include: Updating the state estimate value using the derivative of the state vector obtained from inertial measurement, and updating the covariance matrix according to the differential Lyapunov equation; Predicting the aircraft state based on the updated state estimate value and the updated covariance matrix; Using the position, pose, and area of the target area, combined with the predicted aircraft state, to generate the expected value of the measurement parameters; The operations performed by the measurement model include: Determining the relative position between the center of the target area and the aircraft based on the expected value of the measurement parameters to obtain the position of the aircraft.

6. The method for assisting the takeoff and landing of a vision-inertial integrated positioning assisted aircraft according to claim 5, wherein, The linear relationship between the measurement model and the aircraft position in the inertial coordinate system is expressed as: P = (I - KC)P - ; where f is the camera focal length, is the vector from the aircraft to the target area, is the predicted position vector of the aircraft, A is the given area of the target area, K is the Kalman gain, is the measurement vector, C is the Jacobian matrix of the measurement vector with respect to the state vector, P is the updated covariance matrix, P - is the prior value of the covariance matrix, X is the vector from the aircraft to the target area 's x-axis coordinate value, Y is the vector from the aircraft to the target area 's y-axis coordinate value, Z is the vector from the aircraft to the target area 's z-axis coordinate value.

7. The method for assisting the takeoff and landing of a vision-inertial integrated positioning assisted aircraft according to claim 6, characterized in that, The linearized model of the measurement vector and the attitude quaternion in the aircraft reference system is expressed as: In the formula, is the relative position vector between the center of the target area and the aircraft in the aircraft reference system, is the attitude quaternion, and L cb is the rotation transformation matrix.

8. A computer device, comprising: A memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor executes the computer program to implement the method for assisting the takeoff and landing of an aircraft using visual-inertial integrated positioning according to any one of claims 1-7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the method for assisting the takeoff and landing of an aircraft using visual-inertial integrated positioning according to any one of claims 1-7.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the method for assisting the takeoff and landing of an aircraft using visual-inertial integrated positioning according to any one of claims 1-7.

Images