UAV airborne camera installation error calibration method and computer equipment

Abstract

The invention provides a method for calibrating the installation error of an airborne camera of an unmanned aerial vehicle and a computer device. Firstly, a reference image taken by the unmanned aerial vehicle at different postures and positions and its corresponding posture and position reference data are recorded by the airborne camera of the unmanned aerial vehicle, an inertial navigation system and a satellite receiver. Secondly, visual positioning information of the reference image is given by a visual positioning method, and an error equation between the visual positioning information including the installation error and the reference data is established. Finally, a Newton downhill method based on RANSAC is proposed to accurately calibrate the installation error between the airborne camera of the unmanned aerial vehicle and the inertial navigation system, so as to improve the visual positioning accuracy.

Description

Technical Field

[0001] The present invention belongs to the field of UAV positioning technology, and relates to a method for calibrating the installation error of a UAV airborne camera and a computer device. The method combines technologies in multiple fields such as image processing, attitude control, and sensor data fusion. The method can be applied to the fields of national defense security, aerial photography, geographic information collection, and environmental monitoring, and aims to improve the accuracy, stability, and application value of UAV aerial images. Background Art

[0002] With the rapid development of UAV technology, UAV visual positioning has become one of the important applications in the fields of national defense, surveying and mapping, and monitoring. However, when installing an airborne camera on a UAV, there are deviations between the measurement or observation results and the actual situation caused by slight deviations and uncertainties in the installation position, including but not limited to slight deviations in the installation angle, non-verticality of the installation plane, and position drift caused by carrier vibration. Among them, slight deviations in the installation angle will cause problems such as distortion and posture misalignment of the aerial image, which will seriously affect the accuracy of the measurement or observation results. At present, there is no effective solution to the problem of poor visual positioning accuracy caused by installation errors of UAV airborne cameras. Therefore, accurately evaluating and correcting the installation errors of UAV airborne cameras is crucial to improving the quality of aerial images and data accuracy. Summary of the invention

[0003] The present invention aims to solve at least one of the technical problems existing in the prior art or related art.

[0004] To this end, the present invention provides a method for calibrating the installation error of an unmanned aerial vehicle (UAV) airborne camera and a computer device.

[0005] The technical solution of the present invention is as follows:

[0006] According to one aspect, a method for calibrating an installation error of an unmanned aerial vehicle airborne camera is provided, the calibration method comprising:

[0007] Step 1: Perform visual positioning and establish the error equation between the visual positioning information including the installation error and the reference data to obtain the installation error angle. δθ, δγ expressions;

[0008] Step 2: According to the error equation, the Newton downhill method based on RANSAC is used to calibrate the installation error, including:

[0009] 2.1. Based on the error equation, the iterative format of Newton's downhill method is obtained:

[0010]

[0011] Among them, the definition y=δθ,z=δγ,x k represents the installation error at time k, x k+1 represents the installation error at time k; is the heading angle; θ is the pitch angle; γ is the roll angle; Δp is the visual positioning error directly below the drone;

[0012] 2.2. Set the initial value x0 of the installation error and the maximum number of iterations;

[0013] 2.3. Randomly select a set of sample data from all sample data, and solve the installation error angle x according to the Newton downhill method based on the iterative format k ;

[0014] 2.4. According to the installation error angle x k Verify all samples to obtain the corresponding visual positioning error directly below the UAV, and sum the visual positioning errors directly below multiple UAVs to obtain Δd;

[0015] 2.5. Perform random calculations on the remaining sample data according to steps 2.3 to 2.4 to obtain the corresponding Δd;

[0016] 2.6. After the iteration end condition is met, the smallest Δd among all Δd is selected as the final result.

[0017] Furthermore, the condition for ending the iteration is satisfied includes:

[0018] If the absolute value of the difference between the installation error angles calculated from two consecutive sample data is less than or equal to 1e -6 , then the iteration ends; or if the maximum number of iterations is met, then the iteration ends.

[0019] Further, in step 2.3, the installation error angle x is solved according to the Newton downhill method based on the iterative format. k include:

[0020] (1) Set the initial value of the iteration and obtain θ and γ determine the value of f(x);

[0021] (2) Determine the value of f'(x) based on the initial value of the iteration and θ and γ;

[0022] (3) Iteration format based on Newton's downhill method Calculate the kth iteration result;

[0023] (4) Repeat the loop until convergence.

[0024] Furthermore, in step 2.2, the initial value x0 of the installation error and the maximum number of iterations are designed as follows:

[0025] x0=

[111] , the number of iterations is 100.

[0026] Furthermore, visual positioning is performed in the following manner:

[0027] The reference images taken by the drone in different postures and positions and their corresponding posture and position reference data are recorded through the drone's onboard camera, inertial navigation and satellite receiver;

[0028] According to the attitude and position reference data, the vector between the ground point and the positioning point directly below the drone is obtained:

[0029] The visual navigation coordinates of the positioning point directly below the UAV are calculated based on the vector between the ground point and the positioning point directly below the UAV.

[0030] Furthermore, the vector between the ground point and the positioning point directly below the drone is solved by the following formula:

[0031] in, is the inverse matrix of the attitude matrix at point A is the transformation matrix from the camera coordinate system to the carrier coordinate system, h A is the height at point A; θ A is the corresponding pitch angle at point A; A is the corresponding roll angle at point A.

[0032] Furthermore, the visual navigation coordinates of the positioning point directly below the drone are calculated by the following formula:

[0033] Among them, R M is the principal radius of curvature of the Earth’s meridian, R N is the principal curvature radius of the circle; the attitude angle of the drone causes the main axis of the camera to intersect with the ground at one point, namely, matching point A2. is the longitude of point A2, is the latitude of point A2; is the navigation coordinates corresponding to the main point of the aerial image; Refers to the positioning information of the ground point in the camera coordinate system; Refers to the positioning information of the ground point in the calculation camera coordinate system; Refers to the vector containing the installation error between the ground point and the positioning point directly below the drone.

[0034] Furthermore, the error equation between the visual positioning information including the installation error and the reference data is established in the following way to obtain the installation error angle δθ, δγ expressions;

[0035] Assume that the latitude and longitude of the ground point solved by visual navigation is the ideal true value, then the visual positioning error directly below the drone is obtained by the following formula:

[0036]

[0037] In the formula,

[0038]

[0039] in, δθ and δγ are the heading angle error, pitch angle error, and roll angle error when the camera is installed, respectively;

[0040] The installation error angle is calculated as follows:

[0041]

[0042] According to another aspect, a computer device is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above method when executing the computer program.

[0043] The above technical solution calculates the iterative format of the Newton iteration method according to the attitude, altitude and position information provided by the benchmark data, and accurately calibrates the installation error between the airborne camera and the inertial navigation through an improved RANSAC Newton iteration method. It is robust and can accurately evaluate and correct the installation error of the UAV airborne camera, greatly improving the visual positioning accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] The included drawings are used to provide a further understanding of the embodiments of the present invention, which constitute a part of the specification, are used to illustrate the embodiments of the present invention, and together with the text description, explain the principles of the present invention. Obviously, the drawings in the following description are only some embodiments of the present invention, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0045] Figure 1 is a schematic diagram of a coordinate system of an embodiment of the present invention;

[0046] Figure 2 Schematic diagram of visual positioning according to an embodiment of the present invention. DETAILED DESCRIPTION

[0047] It should be noted that, in the absence of conflict, the embodiments in this application and the features in the embodiments can be combined with each other. The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. The following description of at least one exemplary embodiment is actually only illustrative and is by no means intended to limit the present invention and its application or use. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0048] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present application. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprise" and / or "include" are used in this specification, it indicates the presence of features, steps, operations, devices, components and / or combinations thereof.

[0049] Unless otherwise specifically stated, the relative arrangement of the parts and steps described in these embodiments, numerical expressions and numerical values ​​do not limit the scope of the present invention. At the same time, it should be understood that, for ease of description, the sizes of the various parts shown in the accompanying drawings are not drawn according to the actual proportional relationship. The technology, method and equipment known to ordinary technicians in the relevant field may not be discussed in detail, but in appropriate cases, the technology, method and equipment should be regarded as a part of the authorization specification. In all examples shown and discussed here, any specific value should be interpreted as being merely exemplary, rather than as a limitation. Therefore, other examples of exemplary embodiments may have different values. It should be noted that similar reference numerals and letters represent similar items in the following drawings, so once a certain item is defined in an accompanying drawing, it does not need to be further discussed in subsequent drawings.

[0050] In one embodiment of the present invention, a method for calibrating an installation error of an airborne camera of an unmanned aerial vehicle is provided, and the calibration method comprises:

[0051] Step 1: Perform visual positioning and establish the error equation between the visual positioning information including the installation error and the reference data to obtain the installation error angle. δθ, δγ expressions;

[0052] Step 2: According to the error equation, the Newton downhill method based on RANSAC is used to calibrate the installation error, including:

[0053] 2.1. Based on the error equation, the iterative format of Newton's downhill method is obtained:

[0054]

[0055] Among them, the definition y=δθ,z=δγ,x k represents the installation error at time k, x k+1 represents the installation error at time k; is the heading angle; θ is the pitch angle; γ is the roll angle; Δp is the visual positioning error directly below the drone;

[0056] 2.2. Set the initial value x0 of the installation error and the maximum number of iterations;

[0057] 2.3. Randomly select a set of sample data from all sample data, and solve the installation error angle x according to the Newton downhill method based on the iterative format k ;

[0058] 2.4. According to the installation error angle x k Verify all samples to obtain the corresponding visual positioning error directly below the UAV, and sum the visual positioning errors directly below multiple UAVs to obtain Δd;

[0059] 2.5. Perform random calculations on the remaining sample data according to steps 2.3 to 2.4 to obtain the corresponding Δd;

[0060] 2.6. After the iteration end condition is met, the smallest Δd among all Δd is selected as the final result.

[0061] Specifically, during the navigation process of the drone, the pitch angle and roll angle are small most of the time, and the equation is approximated to define y = δθ, z = δγ to get the formula:

[0062]

[0063] Simplified, the iterative format of Newton's downhill method is shown in the following formula:

[0064]

[0065] Applying the above configuration, the iterative format of the Newton iteration method is calculated according to the attitude, altitude and position information provided by the benchmark data. The installation error between the airborne camera and the inertial navigation is accurately calibrated through an improved RANSAC Newton iteration method. It has robustness and can accurately evaluate and correct the installation error of the UAV airborne camera, greatly improving the visual positioning accuracy.

[0066] Preferably, the condition for ending the iteration is satisfied includes:

[0067] If the absolute value of the difference between the installation error angles calculated from two consecutive sample data is less than or equal to 1e -6 , then the iteration ends; or if the maximum number of iterations is met, then the iteration ends. The iteration here means: using a set of samples to calculate the corresponding Δd is counted as one iteration.

[0068] In the above embodiment, in step 2.3, the installation error angle x is solved according to the Newton downhill method based on the iterative format. k include:

[0069] (1) Set the initial value of the iteration and obtain θ and γ determine the value of f(x);

[0070] (2) Determine the value of f'(x) based on the initial value of the iteration and θ and γ;

[0071] (3) Iteration format based on Newton's downhill method Calculate the kth iteration result;

[0072] (4) Repeat the loop until convergence.

[0073] It can be seen that the Newton downhill method of this embodiment can directly solve the installation error based on a group of samples, but the installation error solved in this way is not robust. Therefore, the embodiment of the present invention proposes a Newton downhill method based on RANSAC. The design principle of the embodiment of the present invention is: refer to the "hypothesis-verification" idea of ​​the RANSAC algorithm, set the maximum number of iterations, randomly select a group of sample data from all sample data to solve the installation error angle through the Newton downhill method, and then verify all samples according to the installation error angle. The data that meets the installation error model is recorded as an inlier, otherwise it is an outlier. The optimal installation error angle is solved by judging the number of inliers, that is, the optimal solution of the installation error of this group of data is determined by the "hypothesis-verification" idea and the Newton downhill method. However, since the task requirement is a positioning problem, the improved RANSAC inlier criterion is the Euclidean distance. In the verification process, the Newton downhill method based on RANSAC will save the sum of the Euclidean distance errors of each verification result, and finally the installation error parameter with the smallest error in the Euclidean space is taken as the optimal solution.

[0074] Preferably, in step 2.2, the initial value x0 of the installation error and the maximum number of iterations are designed as follows:

[0075] x0 =

[111] , the number of iterations is 100 (i.e., 100 sets of sample data need to be calculated). That is, in step (1), the initial value of the iteration is x0 =

[111] .

[0076] In the above embodiment, visual positioning can be performed in the following manner:

[0077] The reference images taken by the drone in different postures and positions and their corresponding posture and position reference data are recorded through the drone's onboard camera, inertial navigation and satellite receiver;

[0078] According to the attitude and position reference data, the vector between the ground point and the positioning point directly below the drone is obtained:

[0079] The visual navigation coordinates of the positioning point directly below the UAV are calculated based on the vector between the ground point and the positioning point directly below the UAV.

[0080] In the embodiment of the present invention, the vector between the ground point and the positioning point directly below the drone is calculated by the following formula:

[0081]

[0082]

[0083] in, is the inverse matrix of the attitude matrix at point A is the transformation matrix from the camera coordinate system to the carrier coordinate system, h A is the height at point A; θ A is the corresponding pitch angle at point A; A is the corresponding roll angle at point A.

[0084] In the embodiment of the present invention, the visual navigation coordinates of the positioning point directly below the drone are calculated by the following formula:

[0085] Among them, R M is the principal radius of curvature of the Earth’s meridian, R N is the principal curvature radius of the circle; the attitude angle of the drone causes the main axis of the camera to intersect with the ground at one point, namely, matching point A2. is the longitude of point A2, is the latitude of point A2; is the navigation coordinates corresponding to the main point of the aerial image; Refers to the positioning information (latitude, altitude and longitude) of the ground point in the camera coordinate system; Refers to the positioning information of the ground point in the calculated camera coordinate system (latitude, altitude, longitude) The vector containing the installation error between the ground point and the positioning point directly below the drone.

[0086] That is, by matching the aerial image with the commercial map, the navigation coordinates of the main point of the aerial image corresponding to the commercial map are calculated. The coordinates mainly include the true value of the navigation coordinates, the positioning error caused by the installation error, and the noise. Among them, the installation error is related to the height and attitude. When the UAV flies at a high altitude, the installation error will further increase, thereby reducing the viewing angle positioning accuracy, such as Figure 1 As shown, the coordinate system of the embodiment of the present invention is defined as follows:

[0087] Inertial coordinate system (i system): The origin of the coordinate system is the center of the earth, X i The axis points to the vernal equinox (one of the intersection points where the intersection of the ecliptic plane and the equatorial plane intersects the celestial sphere), Y i The axis is the Earth's rotation axis and points to the North Pole, Z i The axis and the other two axes satisfy the right-hand rule, and the output of the inertial sensor (gyroscope, inertial table) is based on this coordinate system as a reference.

[0088] Geographic coordinate system (g system): coordinate system origin O g Take as the center of mass of the carrier, X g The Y axis points to the local geographic north. g The axis is perpendicular to the local ellipsoid and points to the sky, Z g The axis points to the local geographic east direction. In this paper, the "North-Sky-East" geographic coordinate system is selected as the navigation coordinate system (n system) to solve the navigation parameters.

[0089] Carrier coordinate system (b system): coordinate system origin O b Take the center of mass of the aircraft carrier, X b Along the longitudinal axis of the carrier, forward is positive; Y b Along the vertical axis of the carrier, upward is positive; Z b Along the horizontal axis of the carrier, the right direction is positive. This paper assumes that the IMU coordinate system coincides with the origin of the carrier coordinate system, and there is no installation error angle between the IMU coordinate system and the carrier coordinate system.

[0090] Camera coordinate system (C system): The center of the camera is the coordinate origin o c ;x c The y axis is parallel to the horizontal axis of the pixel coordinate system, and is positive to the right; c The axis is parallel to the vertical axis of the pixel coordinate system, and is positive downward; the perpendicular line from the center of the camera to the image plane is called the z axis of the camera. c axis, and with x c Axis and y c The axes form a right-handed coordinate system.

[0091] Pixel coordinate system (p system): The origin is the upper left corner of the image. p , along the horizontal direction of the image to the right is the x of the image coordinate system pAxis, downward along the vertical direction of the image is the y axis of the image coordinate system p Axis. The coordinates of a point in the pixel coordinate system are (u, v), and the unit is pixel (pixel).

[0092] The visual positioning principle of the embodiment of the present invention is as follows:

[0093] Through image matching, the navigation coordinates of the main point of the aerial image on the commercial map can be calculated. In the case of an ideal downward view, the coordinates are consistent with the navigation coordinates of the drone at the current moment. However, due to the change of the drone's attitude and altitude, the navigation coordinates of the main point of the aerial image obtained are not the navigation coordinates of the drone. The navigation coordinates of the drone need to be obtained through spatial position inversion. The schematic diagram of the vacant position inversion is shown in the figure. Figure 2 As shown:

[0094] Navigation coordinates corresponding to the main point of the aerial image That is, the latitude and longitude of the ground point. The vector between the ground point and the positioning point directly below the drone can be calculated by the following formula:

[0095]

[0096] in, is the inverse matrix of the attitude matrix at point A It is the transformation matrix from the camera coordinate system to the carrier coordinate system.

[0097] The visual navigation coordinates of the positioning point directly below the drone can be calculated using the following formula:

[0098]

[0099] Among them, R M is the principal radius of curvature of the Earth’s meridian, R N It is the principal curvature radius of the Maoyou circle.

[0100] In the above embodiment, the error equation between the visual positioning information including the installation error and the reference data is established in the following manner to obtain the installation error angle δθ, δγ expressions;

[0101] When there is an angle difference between the camera coordinate system and the carrier coordinate system, there is an installation error. Assuming that the latitude and longitude of the ground point solved by visual navigation is the ideal true value, the visual positioning error directly below the drone is:

[0102]

[0103] In the formula Right now

[0104]

[0105] in, δθ and δγ are the heading angle error, pitch angle error and roll angle error when the camera is installed, respectively.

[0106] If the direction cosine matrix is ​​known The installation error angle is calculated as follows:

[0107]

[0108] That is, in the process of converting the visual positioning results into the navigation results directly below the drone, the slight angular deviation of the installation position of the onboard camera will cause the visual positioning accuracy to be significantly reduced. By establishing an installation error model between the visual positioning and the benchmark, the installation error angle can be accurately solved.

[0109] According to another embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above method when executing the computer program.

[0110] In summary, the embodiment of the present invention first records the reference images taken by the drone in different postures and positions and their corresponding posture and position benchmark data through the drone's onboard camera, inertial navigation and satellite receiver; secondly, the visual positioning information of the reference image is given through the existing visual positioning method, and the error equation between the visual positioning information including the installation error and the benchmark data is established; finally, a Newton descent method based on RANSAC is proposed to accurately calibrate the installation error between the drone's onboard camera and inertial navigation, which greatly improves the visual positioning accuracy.

[0111] Features described and / or illustrated above for one embodiment may be used in the same or similar manner in one or more other embodiments, and / or combined with or used in place of features in other embodiments.

[0112] It should be emphasized that the term “include / comprises” when used herein refers to the presence of features, integers, steps or components, but does not exclude the presence or addition of one or more other features, integers, steps, components or combinations thereof.

[0113] The above method of the present invention can be implemented by hardware, or by hardware combined with software. The present invention relates to such a computer-readable program, which, when executed by a logic component, enables the logic component to implement the above-mentioned device or component, or enables the logic component to implement the above-mentioned various methods or steps. The present invention also relates to a storage medium for storing the above program, such as a hard disk, a magnetic disk, an optical disk, a DVD, a flash memory, etc.

[0114] The many features and advantages of these embodiments are apparent from this detailed description, and thus the appended claims are intended to cover all such features and advantages of these embodiments that fall within their true spirit and scope. Furthermore, since numerous modifications and changes will readily occur to those skilled in the art, it is not intended that the embodiments of the invention be limited to the exact construction and operation illustrated and described, but rather all suitable modifications and equivalents falling within the scope thereof are intended to be covered.

[0115] Parts of the present invention that are not described in detail are well known to those skilled in the art.

Claims

1. A method for calibrating the installation error of an unmanned aerial vehicle airborne camera, characterized in that: The calibration method comprises: Step 1: Perform visual positioning and establish the error equation between the visual positioning information including the installation error and the reference data to obtain the installation error angle. δθ, δγ expressions; Step 2: According to the error equation, the Newton downhill method based on RANSAC is used to calibrate the installation error, including: 2.

1. Based on the error equation, the iterative format of Newton's downhill method is obtained: Among them, the definition y=δθ,z=δγ,x k represents the installation error at time k, x k+1 represents the installation error at time k; is the heading angle; θ is the pitch angle; γ is the roll angle; Δp is the visual positioning error directly below the drone; 2.

2. Set the initial value x0 of the installation error and the maximum number of iterations; 2.

3. Randomly select a set of sample data from all sample data, and solve the installation error angle x according to the Newton downhill method based on the iterative format k ; 2.

4. According to the installation error angle x k Verify all samples to obtain the corresponding visual positioning error directly below the UAV, and sum the visual positioning errors directly below multiple UAVs to obtain Δd; 2.

5. Perform random calculations on the remaining sample data according to steps 2.3 to 2.4 to obtain the corresponding Δd; 2.

6. After the iteration end condition is met, the smallest Δd among all Δd is selected as the final result.

2. The method for calibrating the installation error of an unmanned aerial vehicle airborne camera according to claim 1, characterized in that: The conditions for ending the iteration include: If the absolute value of the difference between the installation error angles calculated from two consecutive sample data is less than or equal to 1e -6 , then the iteration ends; or if the maximum number of iterations is met, then the iteration ends.

3. The method for calibrating the installation error of an unmanned aerial vehicle airborne camera according to claim 1, characterized in that: In step 2.3, the installation error angle x is solved according to the Newton downhill method based on the iterative format. k include: (1) Set the initial value of the iteration and obtain θ and γ determine the value of f(x); (2) Determine the value of f'(x) based on the initial value of the iteration and θ and γ; (3) Iteration format based on Newton's downhill method Calculate the kth iteration result; (4) Repeat the loop until convergence.

4. The method for calibrating the installation error of an unmanned aerial vehicle airborne camera according to claim 1, characterized in that: In step 2.2, the initial value x0 of the installation error and the maximum number of iterations are designed as follows: x0=[111], the number of iterations is 100.

5. A method for calibrating the installation error of an unmanned aerial vehicle airborne camera according to any one of claims 1 to 4, characterized in that: Visual positioning is performed in the following ways: The reference images taken by the drone in different postures and positions and their corresponding posture and position reference data are recorded through the drone's onboard camera, inertial navigation and satellite receiver; According to the attitude and position reference data, the vector between the ground point and the positioning point directly below the drone is obtained: The visual navigation coordinates of the positioning point directly below the UAV are calculated based on the vector between the ground point and the positioning point directly below the UAV.

6. The method for calibrating the installation error of an unmanned aerial vehicle airborne camera according to claim 5, characterized in that: The vector between the ground point and the positioning point directly below the drone is solved by the following formula in, is the inverse matrix of the attitude matrix at point A is the transformation matrix from the camera coordinate system to the carrier coordinate system, h A is the height at point A; θ A is the corresponding pitch angle at point A; A is the corresponding roll angle at point A.

7. The method for calibrating the installation error of a drone airborne camera according to claim 6, characterized in that: The visual navigation coordinates of the positioning point directly below the drone are calculated using the following formula Among them, R M is the principal radius of curvature of the Earth’s meridian, R N is the principal curvature radius of the circle; the attitude angle of the drone causes the main axis of the camera to intersect with the ground at one point, namely, matching point A2. is the longitude of point A2, is the latitude of point A2; is the navigation coordinates corresponding to the main point of the aerial image; Refers to the positioning information of the ground point in the camera coordinate system; Refers to the positioning information of the ground point in the calculation camera coordinate system; Refers to the vector containing the installation error between the ground point and the positioning point directly below the drone.

8. The method for calibrating the installation error of a drone airborne camera according to claim 7, characterized in that: The error equation between the visual positioning information including the installation error and the reference data is established in the following way to obtain the installation error angle: δθ, δγ expressions; Assume that the latitude and longitude of the ground point solved by visual navigation is the ideal true value, then the visual positioning error directly below the drone is obtained by the following formula: In the formula, in, δθ and δγ are the heading angle error, pitch angle error, and roll angle error when the camera is installed, respectively; The installation error angle is calculated as follows:

9. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the method according to any one of claims 1 to 8 is implemented.

Images