CN113221253B - Unmanned aerial vehicle control method and system for anchor bolt image detection - Google Patents
Unmanned aerial vehicle control method and system for anchor bolt image detection Download PDFInfo
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- CN113221253B CN113221253B CN202110609044.4A CN202110609044A CN113221253B CN 113221253 B CN113221253 B CN 113221253B CN 202110609044 A CN202110609044 A CN 202110609044A CN 113221253 B CN113221253 B CN 113221253B
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- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
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- G06T7/70—Determining position or orientation of objects or cameras
- G06T7/73—Determining position or orientation of objects or cameras using feature-based methods
- G06T7/75—Determining position or orientation of objects or cameras using feature-based methods involving models
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- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
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- G06T2207/30132—Masonry; Concrete
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- G06T2207/30—Subject of image; Context of image processing
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Abstract
The invention discloses an unmanned aerial vehicle control method and system for anchor bolt image detection, wherein the unmanned aerial vehicle control method comprises the following steps: establishing an unmanned aerial vehicle autonomous positioning model according to the initial coordinate position of the target anchor bolt, the coordinates and angles of the unmanned aerial vehicle and the carried camera; controlling the unmanned aerial vehicle to fly to a target anchor bolt in real time according to the unmanned aerial vehicle autonomous positioning model; in the flight process of the unmanned aerial vehicle, correcting the shooting angle of the camera in real time according to the coordinate positions of the unmanned aerial vehicle and the target anchor bolt; when the unmanned aerial vehicle flies to a shooting area corresponding to the target anchor bolt, the camera is controlled to acquire an image of the target anchor bolt according to the shooting angle of the camera. The technical scheme of the invention can solve the problems of low detection efficiency and poor safety caused by the fact that the number and the positions of the anchor bolts are detected manually in the prior art.
Description
Technical Field
The invention relates to the technical field of communication, in particular to an unmanned aerial vehicle control method and system for anchor bolt image detection.
Background
An exterior wall insulation system of a building generally comprises an exterior wall substrate, an insulation board and anchor bolts. Wherein, the crab-bolt is the most effective connected mode of guaranteeing that heated board and outer wall base member are firmly laminated. If the number of the anchor bolts is insufficient or the anchor bolts are not uniformly distributed, the hidden danger that the outer wall falls off is easily left in the later-stage use process of the outer wall heat-insulation system. Therefore, the number and the distribution positions of the anchor bolts are important factors for evaluating the safety and the reliability of the external wall insulation board system.
At present, the detection method for the number and the positions of the anchor bolts usually depends on manual detection. Specifically, a scaffold is manually arranged or a worker is hung by a cable to take a picture for detection; when the building height is lower, through setting up the scaffold frame, by artifical handheld equipment of shooing, the detection of shooing, when the crab-bolt in the different regions of building is shot to needs artifical transport scaffold frame to corresponding regional shooting. When the height of the building is high, a worker needs to take a picture by arranging the cable, the worker can take a picture by changing the height of the cable in the vertical direction, and the worker needs to change the position in the horizontal direction.
The detection mode of above-mentioned crab-bolt quantity and position, the spend time is longer, and detection efficiency is low to the security is not high, causes the accident hidden danger easily.
Disclosure of Invention
The invention provides an unmanned aerial vehicle control method and system for anchor bolt image detection, and aims to solve the problems of low detection efficiency and low safety of the detection mode of the number and the positions of anchor bolts in the prior art.
To achieve the above object, according to a first aspect of the present invention, there is provided a drone controlling method for anchor bolt image detection, including:
establishing an unmanned aerial vehicle autonomous positioning model according to the initial coordinate position of the target anchor bolt, the coordinates and angles of the unmanned aerial vehicle and the carried camera;
controlling the unmanned aerial vehicle to fly to a target anchor bolt in real time according to the unmanned aerial vehicle autonomous positioning model;
in the flight process of the unmanned aerial vehicle, correcting the shooting angle of the camera in real time according to the coordinate positions of the unmanned aerial vehicle and the target anchor bolt;
when the unmanned aerial vehicle flies to a shooting area corresponding to the target anchor bolt, the camera is controlled to acquire an image of the target anchor bolt according to the shooting angle of the camera.
Preferably, the method for controlling an unmanned aerial vehicle provided in the embodiment of the present application, according to the initial coordinate position of the target anchor bolt, the coordinates and angles of the unmanned aerial vehicle and the camera mounted thereon, includes the steps of:
acquiring an initial coordinate position of a target anchor bolt;
acquiring a coordinate position of the unmanned aerial vehicle and an attitude angle of a camera;
calculating to obtain the coordinates of the optical center of the camera according to the attitude angles of the unmanned aerial vehicle and the camera;
calculating to obtain the image point coordinates of the target anchor bolt in the camera according to the coordinates of the optical center of the camera and the initial coordinate position of the target anchor bolt;
and establishing an unmanned aerial vehicle autonomous positioning model according to the collinear geometric relationship of the initial coordinate position and the image point coordinate of the target anchor bolt and the coordinate of the optical center of the camera.
Preferably, the unmanned aerial vehicle control method provided in the embodiment of the present application, according to the coordinates of the optical center of the camera and the initial coordinate position of the target anchor bolt, calculates coordinates of an image point of the target anchor bolt in the camera, and includes:
acquiring a photo proportion suitable for a target anchor bolt;
and calculating to obtain the image point coordinates of the target anchor bolt in the camera according to the ratio of the initial coordinate position of the target anchor bolt to the image proportion.
Preferably, the method for controlling an unmanned aerial vehicle provided by the embodiment of the present application, according to the autonomous positioning model of the unmanned aerial vehicle, includes the step of controlling the unmanned aerial vehicle to fly to a target anchor bolt in real time, including:
establishing an overdetermined equation set related to the coordinate position of the unmanned aerial vehicle according to a multi-point geometric constraint relation specified by the autonomous positioning model;
solving an overdetermined equation set by using a least square estimation method, and acquiring a coordinate position of the unmanned aerial vehicle in the flying process in real time;
and adjusting the flight parameters of the unmanned aerial vehicle in a negative feedback manner according to the coordinate position of the unmanned aerial vehicle in the flight process and the initial coordinate position of the target anchor bolt.
Preferably, the method for controlling an unmanned aerial vehicle according to the embodiment of the present application, according to the coordinate positions of the unmanned aerial vehicle and the target anchor bolt, corrects the shooting angle of the camera in real time, and includes:
establishing an error equation according to the initial coordinate position of the unmanned aerial vehicle, and establishing a rotation matrix of the unmanned aerial vehicle by using the error equation;
forming a normal equation of the coordinate position and the attitude angle of the unmanned aerial vehicle by using the coefficient array and the constant array of the rotation matrix;
and solving the normal equation according to a least square method, and calculating the coordinate position of the unmanned aerial vehicle and the correction value of the shooting angle of the camera.
Preferably, the method for controlling an unmanned aerial vehicle, provided by the embodiment of the present application, includes the steps of controlling a camera to acquire an image of a target anchor bolt according to a shooting angle of the camera, including:
when the unmanned aerial vehicle flies to a shooting area corresponding to a target anchor bolt, acquiring the coordinate position of the target anchor bolt;
adjusting the shooting angle of the camera by using a steering device according to the preset shooting angle requirement and the coordinate position of the target anchor bolt;
and taking an image of the target anchor bolt according to the shooting angle.
According to a second aspect of the invention, there is provided a drone control system for anchor bolt image detection, comprising:
the model establishing module is used for establishing an unmanned aerial vehicle autonomous positioning model according to the initial coordinate position of the target anchor bolt, the coordinates and the angles of the unmanned aerial vehicle and the carried camera;
the flight control module is used for controlling the unmanned aerial vehicle to fly to the target anchor bolt in real time according to the unmanned aerial vehicle autonomous positioning model;
the shooting correction module is used for correcting the shooting angle of the camera in real time according to the coordinate positions of the unmanned aerial vehicle and the target anchor bolt in the flight process of the unmanned aerial vehicle;
and the image acquisition module is used for controlling the camera to acquire the image of the target anchor bolt according to the shooting angle of the camera when the unmanned aerial vehicle flies to the shooting area corresponding to the target anchor bolt.
Preferably, in the unmanned aerial vehicle control system provided in the embodiment of the present application, the model building module includes:
the first coordinate acquisition submodule is used for acquiring an initial coordinate position of a target anchor bolt;
the second coordinate acquisition submodule is used for acquiring the coordinate position of the unmanned aerial vehicle and the attitude angle of the camera;
the optical center coordinate calculation submodule is used for calculating and obtaining the coordinate of the optical center of the camera according to the attitude angles of the unmanned aerial vehicle and the camera;
the image point coordinate calculation submodule is used for calculating the image point coordinate of the target anchor bolt in the camera according to the coordinate of the optical center of the camera and the initial coordinate position of the target anchor bolt;
and the positioning model establishing submodule is used for establishing an unmanned aerial vehicle autonomous positioning model according to the collinear geometric relationship of the initial coordinate position and the image point coordinate of the target anchor bolt and the coordinate of the optical center of the camera.
Preferably, in the unmanned aerial vehicle control system provided in the embodiment of the present application, the flight control module includes:
the equation set establishing submodule is used for establishing an overdetermined equation set related to the coordinate position of the unmanned aerial vehicle according to the multipoint geometric constraint relation specified by the autonomous positioning model;
the third coordinate acquisition sub-module is used for solving an overdetermined equation set by using a least square estimation method and acquiring the coordinate position of the unmanned aerial vehicle in the flight process in real time;
and the flight parameter adjusting submodule is used for adjusting the flight parameters of the unmanned aerial vehicle in a negative feedback manner according to the coordinate position of the unmanned aerial vehicle in the flight process and the initial coordinate position of the target anchor bolt.
Preferably, in the unmanned aerial vehicle control system provided in the embodiment of the present application, the photographing correction module includes:
the rotation matrix calculation submodule is used for establishing an error equation according to the initial coordinate position of the unmanned aerial vehicle and establishing a rotation matrix of the unmanned aerial vehicle by using the error equation;
the normal equation calculation submodule is used for forming a normal equation of the coordinate position and the attitude angle of the unmanned aerial vehicle by using the coefficient array and the constant array of the rotation matrix;
and the angle correction submodule is used for solving the normal equation according to a least square method and calculating the correction value of the coordinate position of the unmanned aerial vehicle and the shooting angle of the camera.
In conclusion, the unmanned aerial vehicle control scheme for crab-bolt image detection that this application provided, according to the initial coordinate position of target crab-bolt, unmanned aerial vehicle and the coordinate and the angle of carrying the camera, just can establish unmanned aerial vehicle autonomous positioning model, this unmanned aerial vehicle autonomous positioning model is used for controlling the unmanned aerial vehicle location, and real-time control unmanned aerial vehicle flies to the target crab-bolt, in unmanned aerial vehicle's flight process, the shooting angle of real-time correction camera, when unmanned aerial vehicle reachs the shooting region, just can control the camera to acquire the image of target crab-bolt according to the shooting angle of camera, through the mode that above-mentioned control unmanned aerial vehicle obtained the crab-bolt image, can solve the problem that detection efficiency that manual detection crab-bolt caused among the prior art is low and the security is poor.
Drawings
In order to more clearly illustrate the embodiments or technical solutions of the present invention, the drawings used in the embodiments or technical solutions of the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the structures shown in the drawings without creative efforts.
Fig. 1 is a schematic structural diagram of an unmanned aerial vehicle for capturing an anchor bolt image according to an embodiment of the present invention;
fig. 2 is a schematic flowchart of a method for controlling an drone for detecting an anchor bolt image according to an embodiment of the present invention;
fig. 3 is a schematic flow chart of a method for establishing an autonomous positioning model of an unmanned aerial vehicle according to the embodiment shown in fig. 2;
FIG. 4 is a flowchart illustrating a method for calculating coordinates of image points according to the embodiment shown in FIG. 3;
fig. 5 is a schematic flow chart of a flight control method of the unmanned aerial vehicle according to the embodiment shown in fig. 2;
FIG. 6 is a schematic flowchart of a method for correcting a shooting angle of a camera according to the embodiment shown in FIG. 2;
FIG. 7 is a flowchart illustrating a method for acquiring an image of a target anchor according to the embodiment shown in FIG. 2;
fig. 8 is a schematic structural diagram of a drone control system for image detection of anchor bolts according to an embodiment of the present invention;
fig. 8 is a schematic structural diagram of a drone control system for anchor bolt image detection according to an embodiment of the present invention;
FIG. 9 is a block diagram of a model building module according to the embodiment shown in FIG. 8;
FIG. 10 is a schematic diagram of a flight control module provided in the embodiment shown in FIG. 8;
fig. 11 is a schematic structural diagram of a shooting correction module provided in the embodiment shown in fig. 8.
The implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
| Reference numerals | Name (R) | Reference numerals | Name (R) |
| 1 | Unmanned |
2 | |
| 3 | Supporting frame | 4 | |
| 5 | Video camera | 6 | |
| 7 | Steering mechanism |
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The main technical problems of the embodiment of the invention are as follows:
the existing detection mode of the number and the position of the anchor bolts is carried out manually, the mode of manual detection is long in time, the detection efficiency is low, the safety is low, and the accident potential is easily caused.
In order to solve the problem, this application uses the mode that unmanned aerial vehicle carried on the camera to shoot the crab-bolt and obtains the crab-bolt image, through this kind of mode, can effectively avoid the expense time that artifical detection crab-bolt quantity and position brought long, detection efficiency low scheduling problem. The unmanned aerial vehicle structure that this application embodiment adopted refers to fig. 1, and this an unmanned aerial vehicle for shoot crab-bolt image includes: unmanned aerial vehicle organism 1, wing 2, support frame 3 and control cloud platform 4, wherein, support frame 3 is installed to 2 lower extremes of wing. The unmanned aerial vehicle body 1 is provided with a control cloud deck 4; the control cloud deck 4 is internally provided with a camera 5, a microchip 6 and a steering mechanism 7. According to the coordinates of the unmanned aerial vehicle, the flight angle of the camera 5 and the coordinates of the anchor bolts, the unmanned aerial vehicle can be controlled to fly, the camera 5 is controlled to turn through the steering mechanism 7 by controlling the microchip in the holder 4, and the shooting angle of the camera 5 is changed.
According to the unmanned aerial vehicle control method for anchor bolt image detection provided by the following embodiment of the application, the unmanned aerial vehicle is controlled to fly towards the target anchor bolt and take a picture through the camera by acquiring the coordinates of the unmanned aerial vehicle and the shooting angle of the camera, so that the image of the target anchor bolt is acquired through the camera.
To solve the above problem, referring to fig. 2, fig. 2 is a schematic flowchart of a method for controlling an unmanned aerial vehicle for image detection of an anchor bolt according to an embodiment of the present invention. As shown in fig. 2, the drone control method for anchor bolt image detection includes:
s110: and establishing an unmanned aerial vehicle autonomous positioning model according to the initial coordinate position of the target anchor bolt, the coordinates and the angles of the unmanned aerial vehicle and the carried camera. In the process of controlling the unmanned aerial vehicle to fly to the target anchor bolt, the coordinate position of the target anchor bolt, the coordinate and the flight angle of the unmanned aerial vehicle are required to be acquired, the coordinate and the flight angle of the camera are acquired, the three-point collinear principle can be utilized to establish the unmanned aerial vehicle autonomous positioning model when the initial coordinate position of the target anchor bolt, the coordinate and the angle of the unmanned aerial vehicle and the camera are acquired.
Specifically, as shown in fig. 3, the step of establishing the autonomous positioning model of the unmanned aerial vehicle includes:
s111: acquiring an initial coordinate position of a target anchor bolt; the initial coordinate position of the target anchor bolt can be measured by measuring n target anchor bolts. n ground target point coordinates (X) i ，Y i ，Z i )。
S112: acquiring the coordinate position of the unmanned aerial vehicle and the attitude angle of the camera; specifically, the method includes parameters such as three-dimensional coordinates and attitude angles (such as yaw angle, pitch angle and roll angle) of the unmanned aerial vehicle, attitude angles (such as azimuth angle and altitude angle) of the camera, and the like, such as the flying height H of the unmanned aerial vehicle, and azimuth elements x in the camera 0 ，y 0 F, and a picture scale m; wherein x0 and y0 are coordinates of the image principal point in a camera frame coordinate system, and f is a vertical distance from the shooting center to the photo. Wherein, this unmanned aerial vehicle's coordinate position can be for the coordinate position of unmanned aerial vehicle fixed point.
S113: calculating to obtain the coordinates of the optical center of the camera according to the coordinate position of the unmanned aerial vehicle and the attitude angle of the camera; at this time, the position point coordinate (X) of the unmanned aerial vehicle needs to be determined u 0 、Y u 0 、Z u 0 ) Initial value of camera attitude angle (ω) 0 、ξ 0 、κ 0 ) Wherein, ω is 0 、ξ 0 、κ 0 Representing yaw, pitch and roll angles, respectively. After the coordinate position of the unmanned aerial vehicle is determined, the coordinate of the optical center of the camera can be obtained according to the position relation of the optical centers of the unmanned aerial vehicle and the camera and the obtained deviated attitude angle of the camera.
And setting the origin of the camera at the intersection point of the optical axis of the camera and the transverse axis, wherein the Z axis is a target pointed by the optical axis of the camera, and the pointing angle of the optical axis is represented by an azimuth angle alpha and a height angle beta, wherein the azimuth angle alpha is a rotating angle around an azimuth axis X, and the height angle beta is a rotating angle around a pitching axis Y.
For example, in the case of approximate vertical photography, each initial value of the coordinate position of the drone is as follows:
ω 0 ＝ξ 0 ＝κ 0 . Wherein, it is the position that the carrier centroid was located for a certain moment to establish the initial point, and the X axle represents by the ventral point to the back of the plane, and the Y axle represents the unmanned aerial vehicle cross axle, and the Z axle represents the unmanned aerial vehicle axis of ordinates. Omega 0 、ξ 0 And kappa 0 And represent rotations about the X, Y and Z axes. In a perfect flight state, all three values are 0.
S114: and calculating to obtain the coordinates of the image point of the target anchor bolt in the camera according to the coordinates of the optical center of the camera and the initial coordinate position of the target anchor bolt.
Specifically, as shown in fig. 4, the step of calculating coordinates of an image point of the target anchor bolt in the camera according to coordinates of the optical center of the camera and the initial coordinate position of the target anchor bolt includes:
s1141: and acquiring the picture proportion suitable for the target anchor bolt. Taking n ground target anchor bolts for measurement as an example, after the flight height of the unmanned aerial vehicle is obtained, the image point coordinate z of the height of the target anchor bolt is defaulted i I.e. equal to the flying height H, i.e. equal to the shot ratio m multiplied by the vertical distance f from the centre of the photograph to the shot, i.e. H = mf.
S1142: and calculating to obtain the image point coordinates of the target anchor bolt in the camera according to the ratio of the initial coordinate position of the target anchor bolt to the image proportion. Wherein, the coordinates (x) of the image points corresponding to the measured n ground target anchors are used i ，y i ) For example, the coordinates x of the image points i ＝X i /m，y i ＝Y i /m。
By obtaining the flying height, the photo proportion suitable for the target anchor bolt can be determined, and then the ratio of the initial coordinate position of the target anchor bolt to the photo proportion is used, so that the image point coordinate of the target anchor bolt can be accurately obtained. After the coordinates of the image points of the target anchor bolt are calculated, the necessary error correction is required.
S115: and establishing an unmanned aerial vehicle autonomous positioning model according to the collinear geometric relationship of the initial coordinate position and the image point coordinate of the target anchor bolt and the coordinate of the optical center of the camera.
The technical scheme that this application embodiment provided, initial coordinate position through obtaining the target crab-bolt, and the attitude angle according to unmanned aerial vehicle's coordinate position and camera, the calculation obtains the optical centre coordinate of camera, use this optical centre coordinate, and the initial coordinate position of target crab-bolt and the initial coordinate position of target crab-bolt, can calculate the image point coordinate that obtains the target crab-bolt, through the geometry principle of three-point collineation, use the initial coordinate position of target crab-bolt, the image point coordinate, and the coordinate of camera optical centre, just can establish unmanned aerial vehicle autonomous positioning model, obtain unmanned aerial vehicle's coordinate position in real time.
S120: and controlling the unmanned aerial vehicle to fly to the target anchor bolt in real time according to the unmanned aerial vehicle autonomous positioning model.
As a preferred embodiment, as shown in fig. 5, the step of controlling the drone to fly to the target anchor bolt in real time according to the autonomous positioning model of the drone includes:
s121: establishing an overdetermined equation set related to the coordinate position of the unmanned aerial vehicle according to a multi-point geometric constraint relation specified by the autonomous positioning model;
s122: solving an overdetermined equation set by using a least square estimation method, and acquiring a coordinate position of the unmanned aerial vehicle in the flying process in real time; the over-determined equation set is a part of an automatic positioning model and comprises an error equation, a rotation matrix and the like, and specific formulas can be referred to the following formulas for solving the correction values of the coordinate position and the attitude angle of the unmanned aerial vehicle.
S123: and adjusting the flight parameters of the unmanned aerial vehicle in a negative feedback manner according to the coordinate position of the unmanned aerial vehicle in the flight process and the initial coordinate position of the target anchor bolt.
The technical scheme that this application embodiment provided, the autonomous positioning model is according to the coordinate position of target crab-bolt, the principle of the three point collineation of coordinate of image point coordinate and camera light center is established, at the in-process of unmanned aerial vehicle flight, will keep above-mentioned three point collineation relation all the time, consequently need constantly adjust unmanned aerial vehicle's coordinate position, at the in-process that the autonomous positioning model keeps above-mentioned three point collineation relation, according to the coordinate of camera and unmanned aerial vehicle's position relation, can confirm the real-time coordinate position of unmanned aerial vehicle flight in-process. In the embodiment of the application, according to above-mentioned multiple spot set constraint relation establish the overdetermined equation set to use the least square method to estimate and solve above-mentioned overdetermined equation set, can acquire the coordinate position of unmanned aerial vehicle flight in-process in real time, and according to the coordinate position of unmanned aerial vehicle flight in-process, and the initial coordinate position of target crab-bolt, can adjust unmanned aerial vehicle's flight parameter with the negative feedback mode, including flying speed and unmanned aerial vehicle attitude angle etc..
S130: in the flight process of the unmanned aerial vehicle, the shooting angle of the camera is corrected in real time according to the coordinate positions of the unmanned aerial vehicle and the target anchor bolt.
As a preferred embodiment, as shown in fig. 6, in the unmanned aerial vehicle control method provided in the embodiment of the present application, the step S130: according to the coordinate position of unmanned aerial vehicle and target crab-bolt, the step of the shooting angle of real-time correction camera includes:
s131: and establishing an error equation according to the initial coordinate position of the unmanned aerial vehicle, and establishing a rotation matrix of the unmanned aerial vehicle by using the error equation.
S132: and forming a normal equation of the coordinate position and the attitude angle of the unmanned aerial vehicle by using the coefficient array and the constant array of the rotation matrix.
S133: and solving the normal equation according to a least square method, and calculating the coordinate position of the unmanned aerial vehicle and the correction value of the shooting angle of the camera. Wherein the coordinate position of the drone is the position point coordinate (X) of the drone u 、Y u 、Z u ) (ii) a Shooting angle of camera, i.e., attitude angle of camera: yaw, pitch and roll angles: ω, ξ and κ.
Wherein, solve unmanned aerial vehicle positional information through the collinearity equation at first, the collinearity equation is as follows:
in the formula (Fx) 0 And (Fy) 0 Sequentially initial values of Fx and Fy;
and
respectively the partial derivatives of Fx and Fy to each element in the unmanned aerial vehicle position information, namely the pose information of the camera; Δ X u 、ΔY u 、ΔZ u Δ ω, Δ ξ, and Δ κ are the position point coordinates X of the drone, respectively u 、Y u 、Z u And the increments of the initial values of the attitude angles ω, ξ, and κ of the camera.
Multiplying both sides of the equal sign of the above formula by
And transformed to give the following formula:
in the formula, vx and Vy are residual errors; a is ij The coefficients before each variable are taken as the coefficients; Δ X u 、ΔY u 、ΔZ u Δ ω, Δ ξ and Δ κ are to be evaluated, lx and ly are the differences between observed and calculated values of the image points in the x-axis and y-axis, respectively. The equation is a collinear condition equation linearized equation, namely an error equation; and the error of the unmanned plane position point and the camera attitude angle is adjusted.
Knowing that each ground target point lists two error equations of the error equation set according to the above formula, then n target points can list 2n equations, which are simply expressed in a matrix form, i.e. the rotation matrix is as follows:
v = AX-L; wherein, the first and the second end of the pipe are connected with each other,
X＝[ΔX u ΔY u ΔZ u Δω Δξ Δκ] T ；
and solving an equation set formed by the 2n equations to obtain the position point coordinates of the unmanned aerial vehicle and the increment of the attitude angle of the camera.
After the increment of the attitude angle is obtained, calculating the direction cosines respectively corresponding to omega, xi and kappa: cos ω, cos ξ and cos κ. Substituting the partial derivative of the direction cosine into the coordinate point of the unmanned aerial vehicle
And
the collinearity equations for the partial derivatives of ω, ξ, and κ can be simplified as:
Then, calculating the coordinate value (x) of the pixel point corresponding to each ground target anchor bolt point by point ic 、y ic ) And the coefficients and constant terms of each error equation.Error equations are described above.
After the coefficient and constant item of each error equation are obtained by calculation, the coefficient array A of the rotation matrix is calculated T A and constant matrix A T L, using the coefficient array A T A and constant matrix A T L constitutes the equation of the law.
Because of the rotation matrix: v = AX-L is an error equation set consisting of 2n equations, and the total number of equations is greater than the number of unknowns to be solved, so the least square method is adopted.
According to the principle of least square, if the sum of the squares of the residuals of each error equation is minimized, the optimal estimated value of the parameter to be solved satisfies:
V T v = min, converting the original complex problem into V T And V, solving the problem of the extreme value to be evaluated.
Using V T V is respectively corresponding to DeltaX u 、ΔY u 、ΔZ u Δ ω, Δ ξ and Δ κ are extremized.
Due to V T V＝(AX-L) T (AX-L)＝X T A T AX-2X T A T L+L T L, then
After finishing, obtaining:
A T AX＝A T L；
will be N = A T A represents coefficient array of equation system, and both sides of equation are multiplied by N -1 Then X is determined. Obtaining:
X＝N -1 A T l, the formula is DeltaX u 、ΔY u 、ΔZ u Solutions for Δ ω, Δ ξ, and Δ κ. The solving process needs iterative solving, and correction values of all elements of the coordinate points of the unmanned aerial vehicle and the attitude angles of the camera are compared with the specified line difference until the comparison result is converged.
To sum up, through using and predetermine the rotation matrix, calculate unmanned aerial vehicle's coordinate position and the direction cosine of the shooting angle of camera, this direction cosine is produced for unmanned aerial vehicle flight in-process, needs to pass through this direction cosine to the shooting angle of cameraAnd (5) correcting the degree. At the moment, the coordinate values of each pixel point corresponding to the target anchor bolt are calculated point by using the direction cosine, and an error equation set corresponding to each pixel point is constructed, so that the coordinate position of the unmanned aerial vehicle and the correction value of the shooting angle of the camera are calculated according to the error equation set, and the aim of accurately correcting the shooting angle of the camera is fulfilled. The error equation set comprises an error equation corresponding to each pixel point and a normal equation, the normal equation comprises a coefficient array and a constant array of pixel point coordinates, and the coefficient array A is used for solving the problem that the error equation set is not suitable for the pixel point coordinates T A and constant matrix A T L, according to the formula X = N -1 A T And L, solving the correction value of each element in the coordinate position and the camera attitude angle of the unmanned aerial vehicle. After the correction value is obtained, the correction value is summed with the corresponding initial value to obtain a new initial value of each element. After obtaining a new initial value of each element, checking whether the new initial value converges; specifically, the correction values of the elements of the solved coordinate position of the unmanned aerial vehicle and the attitude angle of the camera are compared with the specified line difference, usually only angle elements (elements included in the attitude angle of the camera) are compared, and when the correction values of the three elements are all smaller than a defined threshold value, the correction is successful.
S140: when the unmanned aerial vehicle flies to a shooting area corresponding to the target anchor bolt, the camera is controlled to acquire an image of the target anchor bolt according to the shooting angle of the camera.
As a preferred embodiment, as shown in fig. 7, the step of controlling the camera to acquire the image of the target anchor bolt according to the shooting angle of the camera includes:
s141: when the unmanned aerial vehicle flies to a shooting area corresponding to a target anchor bolt, acquiring the coordinate position of the target anchor bolt;
s142: adjusting the shooting angle of the camera by using a steering device according to the preset shooting angle requirement and the coordinate position of the target anchor bolt;
s143: and taking an image of the target anchor bolt according to the shooting angle.
The technical scheme that this application embodiment provided, when unmanned aerial vehicle flies to the shooting region that the target crab-bolt corresponds, obtain the coordinate position of target crab-bolt to according to predetermineeing the coordinate position that shoots the angle requirement (camera at three angles of three-dimensional coordinate) according to the target crab-bolt, rotate the camera, thereby reach the purpose that the scenic spot shot the target crab-bolt image.
To sum up, the unmanned aerial vehicle control method for anchor bolt image detection that this application embodiment provided, according to the initial coordinate position of target anchor bolt, the coordinate and the angle of unmanned aerial vehicle and carry-on camera, just can establish unmanned aerial vehicle autonomous positioning model, this unmanned aerial vehicle autonomous positioning model is used for controlling the unmanned aerial vehicle location, and real-time control unmanned aerial vehicle flies to the target anchor bolt, in unmanned aerial vehicle's flight process, the shooting angle of real-time correction camera, when unmanned aerial vehicle arrived and shoots the region, just can control the camera to acquire the image of target anchor bolt according to the shooting angle of camera, through the mode that the unmanned aerial vehicle obtained the image of above-mentioned control, can solve the problem that detection efficiency that manual detection anchor bolt caused among the prior art is low and the security is poor.
In addition, based on the same concept of the above method embodiment, the embodiment of the present invention further provides a patent of an unmanned aerial vehicle control system for anchor bolt image detection, which is used for implementing the above method of the present invention, and as a principle of solving problems of the embodiment of the unmanned aerial vehicle control system is similar to the above method, at least all beneficial effects brought by the technical solution of the above embodiment are achieved, and no further description is given here.
Referring to fig. 8, fig. 8 is a schematic structural diagram of an unmanned aerial vehicle control system for anchor bolt image inspection according to an embodiment of the present invention. As shown in fig. 8, the drone control system for anchor bolt image detection includes:
the model establishing module 110 is used for establishing an unmanned aerial vehicle autonomous positioning model according to the initial coordinate position of the target anchor bolt, the coordinates and angles of the unmanned aerial vehicle and the carried camera;
the flight control module 120 is used for controlling the unmanned aerial vehicle to fly to the target anchor bolt in real time according to the unmanned aerial vehicle autonomous positioning model;
the shooting correction module 130 is used for correcting the shooting angle of the camera in real time according to the coordinate positions of the unmanned aerial vehicle and the target anchor bolt in the flight process of the unmanned aerial vehicle;
and the image acquisition module 140 is configured to control the camera to acquire an image of the target anchor bolt according to the shooting angle of the camera when the unmanned aerial vehicle flies to the shooting area corresponding to the target anchor bolt.
To sum up, the unmanned aerial vehicle control system for crab-bolt image detection that this application embodiment provided, model establishment module 110 is according to the initial coordinate position of target crab-bolt, the coordinate and the angle of unmanned aerial vehicle and the year camera, just can establish unmanned aerial vehicle autonomous positioning model, this unmanned aerial vehicle autonomous positioning model is used for controlling the unmanned aerial vehicle location, and flight control module 120 real-time control unmanned aerial vehicle flies to the target crab-bolt, in unmanned aerial vehicle's flight process, shoot the shooting angle of correction module 130 real-time correction camera, when unmanned aerial vehicle reachs the shooting region, image acquisition module 140 just can control the camera to acquire the image of target crab-bolt according to the shooting angle of camera, through the mode that above-mentioned control unmanned aerial vehicle acquires the crab-bolt image, can solve the problem that detection efficiency that manual detection crab-bolt caused among the prior art is low and the security is poor.
As a preferred embodiment, as shown in fig. 9, in the unmanned aerial vehicle control system provided in the embodiment of the present application, the model building module 110 includes:
the first coordinate obtaining submodule 111 is used for obtaining an initial coordinate position of a target anchor bolt;
a second coordinate obtaining submodule 112, configured to obtain a coordinate position of the drone and an attitude angle of the camera;
the optical center coordinate calculation submodule 113 is used for calculating the coordinates of the optical center of the camera according to the attitude angles of the unmanned aerial vehicle and the camera;
the image point coordinate calculation submodule 114 is used for calculating the image point coordinate of the target anchor bolt in the camera according to the coordinate of the optical center of the camera and the initial coordinate position of the target anchor bolt;
and the positioning model establishing submodule 115 is used for establishing an unmanned aerial vehicle autonomous positioning model according to the collinear geometric relationship between the initial coordinate position and the image point coordinate of the target anchor bolt and the coordinate of the optical center of the camera.
As a preferred embodiment, as shown in fig. 10, in the unmanned aerial vehicle control system provided in the embodiment of the present application, the flight control module 120 includes:
the equation set establishing submodule 121 is used for establishing an overdetermined equation set related to the coordinate position of the unmanned aerial vehicle according to the multipoint geometric constraint relation specified by the autonomous positioning model;
the third coordinate obtaining sub-module 122 is configured to solve the overdetermined equation set by using a least square estimation method, and obtain a coordinate position of the unmanned aerial vehicle in the flight process in real time;
and the flight parameter adjusting submodule 123 is used for adjusting the flight parameters of the unmanned aerial vehicle in a negative feedback manner according to the coordinate position of the unmanned aerial vehicle in the flight process and the initial coordinate position of the target anchor bolt.
As a preferred embodiment, as shown in fig. 11, in the unmanned aerial vehicle control system provided in the embodiment of the present application, the photographing correction module 130 includes:
the rotation matrix calculation submodule 131 is configured to establish an error equation according to the initial coordinate position of the unmanned aerial vehicle, and establish a rotation matrix of the unmanned aerial vehicle by using the error equation;
a normal equation calculation submodule 132 configured to form a normal equation of the coordinate position and the attitude angle of the unmanned aerial vehicle by using the coefficient array and the constant array of the rotation matrix;
and an angle corrector module 133, configured to solve the normal equation according to a least square method, and calculate a correction value of the coordinate position of the unmanned aerial vehicle and the shooting angle of the camera.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It should be noted that in the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word "comprising" does not exclude the presence of elements or steps not listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The invention may be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by one and the same item of hardware. The usage of the words first, second and third, etcetera do not indicate any ordering. These words may be interpreted as names.
While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all such alterations and modifications as fall within the scope of the invention.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.
Claims (8)
1. An unmanned aerial vehicle control method for anchor bolt image detection is characterized by comprising the following steps:
establishing an unmanned aerial vehicle autonomous positioning model according to the initial coordinate position of the target anchor bolt, the coordinates and angles of the unmanned aerial vehicle and the carried camera;
controlling the unmanned aerial vehicle to fly to the target anchor bolt in real time according to the unmanned aerial vehicle autonomous positioning model;
in the flight process of the unmanned aerial vehicle, correcting the shooting angle of the camera in real time according to the coordinate positions of the unmanned aerial vehicle and the target anchor bolt;
when the unmanned aerial vehicle flies to a shooting area corresponding to the target anchor bolt, controlling the camera to acquire an image of the target anchor bolt according to the shooting angle of the camera;
according to the coordinate position of unmanned aerial vehicle and target crab-bolt, real-time correction the step of the shooting angle of camera includes:
establishing an error equation according to the initial coordinate position of the unmanned aerial vehicle, and establishing a rotation matrix of the unmanned aerial vehicle by using the error equation;
forming a normal equation of the coordinate position and the attitude angle of the unmanned aerial vehicle by using a coefficient array and a constant array of the rotation matrix;
solving the normal equation according to a least square method, and calculating a correction value of the coordinate position of the unmanned aerial vehicle and the shooting angle of the camera;
the method specifically comprises the following steps:
coordinate position of the drone, i.e. the position point coordinates (X) of the drone u 、Y u 、Z u ) (ii) a Shooting angle of camera, i.e., attitude angle of camera: yaw, pitch and roll angles: ω, ξ and κ;
firstly, the position information of the unmanned aerial vehicle is solved through a collinear equation, wherein the collinear equation is as follows:
in the formula (Fx) 0 And (Fy) 0 Sequentially initial values of Fx and Fy;
and
respectively the partial derivatives of Fx and Fy to each element in the unmanned aerial vehicle position information, namely the pose information of the camera; Δ X u 、ΔY u 、ΔZ u Δ ω, Δ ξ, and Δ κ are the position point coordinates X of the drone, respectively u 、Y u 、Z u And the increment of the initial values of the attitude angles omega, xi and kappa of the camera;
multiplying both sides of the equal sign of the above formula by
And transformed to give the following formula:
in the formula, vx and Vy are residual errors; a is ij The coefficients before each variable are taken as the coefficients; Δ X u 、ΔY u 、ΔZ u Delta omega, delta xi and delta kappa are to be evaluated, lx and ly are the difference between observed value and calculated value of image point on x axis and y axis respectively; the equation is a collinear condition equation linearized equation, namely an error equation; the error adjusting device is used for adjusting the errors of the unmanned aerial vehicle position point and the camera attitude angle;
given that each ground target point lists two error equations of the error equation set according to the above formula, then n target points can list 2n equations, which are simply expressed in a matrix form, i.e. the rotation matrix is as follows:
v = AX-L; wherein the content of the first and second substances,
X＝[ΔX u ΔY u ΔZ u Δω Δξ Δκ] T ；
solving an equation set formed by the 2n equations to obtain the position point coordinates of the unmanned aerial vehicle and the increment of the attitude angle of the camera;
after the increment of the attitude angle is obtained, calculating the direction cosines respectively corresponding to omega, xi and kappa: cos ω, cos ξ and cos κ; substituting the partial derivative of the direction cosine into the coordinate point of the unmanned aerial vehicle
And
the collinearity equations for the partial derivatives of ω, ξ, and κ can be simplified as:
then, calculating the coordinate value (x) of the pixel point corresponding to each ground target anchor bolt point by point ic 、y ic ) And the coefficients and constant terms of each error equation;
after calculating the coefficient and constant term of each error equation, calculating the coefficient array A of the rotation matrix T A and constant matrix A T L, using the coefficient array A T A and constant matrix A T L constitutes a normal equation;
because of the rotation matrix: v = AX-L is an error equation set composed of 2n equations, and the total number of the equations is more than the number of unknowns to be solved, so the least square method is adopted for solving;
according to the principle of least square, if the sum of the squares of the residuals of each error equation is minimized, the optimal estimated value of the parameter to be solved satisfies:
V T v = min, converting the original complex problem into V T V, solving a problem of an extreme value to be evaluated;
using V T V is respectively corresponding to DeltaX u 、ΔY u 、ΔZ u Solving extreme values of delta omega, delta xi and delta kappa;
due to V T V＝(AX-L) T (AX-L)＝X T A T AX-2X T A T L+L T L, then
After finishing, obtaining:
A T AX＝A T L；
will be N = A T A represents coefficient array of equation system, and both sides of equation are multiplied by N -1 Then X is solved; obtaining:
X＝N -1 A T l, the formula is DeltaX u 、ΔY u 、ΔZ u Solutions for Δ ω, Δ ξ, and Δ κ; the solving process needs iterative solving, and correction values of all elements of the coordinate points of the unmanned aerial vehicle and the attitude angles of the camera are compared with the specified line difference until the comparison result is converged.
2. The drone control method according to claim 1, wherein the step of establishing the autonomous positioning model of the drone according to the initial coordinate position of the target anchor bolt, the coordinates and angles of the drone and the camera carried thereon comprises:
acquiring an initial coordinate position of the target anchor bolt;
acquiring the coordinate position of the unmanned aerial vehicle and the attitude angle of the camera;
calculating to obtain the coordinates of the optical center of the camera according to the attitude angles of the unmanned aerial vehicle and the camera;
calculating to obtain the image point coordinates of the target anchor bolt in the camera according to the initial coordinate position of the target anchor bolt;
and establishing the unmanned aerial vehicle autonomous positioning model according to the collinear geometrical relationship of the initial coordinate position and the image point coordinate of the target anchor bolt and the coordinate of the optical center of the camera.
3. The drone controlling method according to claim 2, wherein the step of calculating coordinates of an image point of the target anchor bolt in the camera according to coordinates of an optical center of the camera and an initial coordinate position of the target anchor bolt comprises:
acquiring a photo proportion suitable for the target anchor bolt;
and calculating to obtain the coordinates of the image point of the target anchor bolt in the camera according to the ratio of the initial coordinate position of the target anchor bolt to the image proportion.
4. The drone controlling method according to claim 1, wherein the step of controlling the drone to fly towards the target anchor bolt in real time according to the drone autonomous positioning model includes:
establishing an overdetermined equation set related to the coordinate position of the unmanned aerial vehicle according to the multi-point geometric constraint relation specified by the autonomous positioning model;
solving the overdetermined equation set by using a least square estimation method, and acquiring the coordinate position of the unmanned aerial vehicle in the flying process in real time;
and regulating the flight parameters of the unmanned aerial vehicle in a negative feedback manner according to the coordinate position of the unmanned aerial vehicle in the flight process and the initial coordinate position of the target anchor bolt.
5. The drone controlling method according to claim 1, wherein the step of controlling the camera to acquire the image of the target anchor bolt according to the shooting angle of the camera includes:
when the unmanned aerial vehicle flies to a shooting area corresponding to the target anchor bolt, acquiring the coordinate position of the target anchor bolt;
adjusting the shooting angle of the camera by using a steering device according to the preset shooting angle requirement and the coordinate position of the target anchor bolt;
and shooting the image of the target anchor bolt according to the shooting angle.
6. An unmanned aerial vehicle control system for anchor bolt image detection, comprising:
the model establishing module is used for establishing an unmanned aerial vehicle autonomous positioning model according to the initial coordinate position of the target anchor bolt, the coordinates and the angles of the unmanned aerial vehicle and the carried camera;
the flight control module is used for controlling the unmanned aerial vehicle to fly to the target anchor bolt in real time according to the unmanned aerial vehicle autonomous positioning model;
the shooting correction module is used for correcting the shooting angle of the camera in real time according to the coordinate positions of the unmanned aerial vehicle and the target anchor bolt in the flight process of the unmanned aerial vehicle;
the image acquisition module is used for controlling the camera to acquire the image of the target anchor bolt according to the shooting angle of the camera when the unmanned aerial vehicle flies to the shooting area corresponding to the target anchor bolt;
a photographing correction module comprising:
the rotation matrix calculation submodule is used for establishing an error equation according to the initial coordinate position of the unmanned aerial vehicle and establishing a rotation matrix of the unmanned aerial vehicle by using the error equation;
the normal equation calculation submodule is used for forming a normal equation of the coordinate position and the attitude angle of the unmanned aerial vehicle by using the coefficient array and the constant array of the rotation matrix;
the angle correction submodule is used for solving the normal equation according to a least square method and calculating a correction value of the coordinate position of the unmanned aerial vehicle and the shooting angle of the camera;
the method specifically comprises the following steps:
coordinate position of the drone, i.e. the position point coordinates (X) of the drone u 、Y u 、Z u ) (ii) a Shooting angle of camera, i.e., attitude angle of camera: yaw, pitch and roll angles: ω, ξ and κ;
firstly, the position information of the unmanned aerial vehicle is solved through a collinear equation, wherein the collinear equation is as follows:
in the formula (Fx) 0 And (Fy) 0 Sequentially initial values of Fx and Fy;
and
respectively the partial derivatives of Fx and Fy to each element in the unmanned aerial vehicle position information, namely the pose information of the camera; Δ X u 、ΔY u 、ΔZ u Δ ω, Δ ξ, and Δ κ are the position point coordinates X of the drone, respectively u 、Y u 、Z u And the increment of the initial values of the attitude angles omega, xi and kappa of the camera;
multiplying both sides of the equal sign of the above formula by
And transformed to give the following formula:
in the formula, vx and Vy are residual errors; a is ij The coefficients before each variable are taken as the coefficients; Δ X u 、ΔY u 、ΔZ u Delta omega, delta xi and delta kappa are to be evaluated, lx and ly are the difference between observed value and calculated value of image point on x axis and y axis respectively; the equation is a collinear condition equation linearized equation, namely an error equation; the error adjusting device is used for adjusting the errors of the unmanned aerial vehicle position point and the camera attitude angle;
knowing that each ground target point lists two error equations of the error equation set according to the above formula, then n target points can list 2n equations, which are simply expressed in a matrix form, i.e. the rotation matrix is as follows:
v = AX-L; wherein the content of the first and second substances,
X＝[ΔX u ΔY u ΔZ u Δω Δξ Δκ] T ；
solving an equation set formed by the 2n equations to obtain the position point coordinates of the unmanned aerial vehicle and the increment of the attitude angle of the camera;
after the increment of the attitude angle is obtained, calculating the direction cosines respectively corresponding to omega, xi and kappa: cos ω, cos ξ and cos κ; substituting the partial derivative of the direction cosine into the coordinate point of the unmanned aerial vehicle
And
the collinearity equations for the partial derivatives of ω, ξ, and κ can be simplified as:
then, calculating the coordinate value (x) of the pixel point corresponding to each ground target anchor bolt point by point ic 、y ic ) And the coefficients and constant terms of each error equation;
after calculating the coefficient and constant term of each error equation, calculating the coefficient array A of the rotation matrix T A and constant matrix A T L, using the coefficient array A T A and constant matrix A T L constitutes a normal equation;
because of the rotation matrix: v = AX-L is an error equation set composed of 2n equations, and the total number of the equations is more than the number of unknowns to be solved, so the least square method is adopted for solving;
according to the principle of least square, if the sum of the squares of the residuals of each error equation is minimized, the optimal estimated value of the parameter to be solved satisfies:
V T v = min, converting the original complex problem into V T V, solving a problem of an extreme value to be evaluated;
using V T V is respectively corresponding to DeltaX u 、ΔY u 、ΔZ u Solving extreme values of delta omega, delta xi and delta kappa;
due to V T V＝(AX-L) T (AX-L)＝X T A T AX-2X T A T L+L T L, then
After finishing, obtaining:
A T AX＝A T L；
will be N = A T A represents coefficient array of equation system, and both sides of equation are multiplied by N -1 Then X is solved; obtaining:
X＝N -1 A T l, the formula is DeltaX u 、ΔY u 、ΔZ u Solutions for Δ ω, Δ ξ, and Δ κ; the solving process needs iterative solving, and correction values of all elements of the coordinate points of the unmanned aerial vehicle and the attitude angles of the camera are compared with the specified line difference until the comparison result is converged.
7. The drone control system of claim 6, wherein the model building module includes:
the first coordinate acquisition submodule is used for acquiring the initial coordinate position of the target anchor bolt;
the second coordinate acquisition submodule is used for acquiring the coordinate position of the unmanned aerial vehicle and the attitude angle of the camera;
the optical center coordinate calculation submodule is used for calculating the coordinates of the optical center of the camera according to the attitude angles of the unmanned aerial vehicle and the camera;
the image point coordinate calculation submodule is used for calculating the image point coordinate of the target anchor bolt in the camera according to the initial coordinate position of the target anchor bolt;
and the positioning model establishing submodule is used for establishing the unmanned aerial vehicle autonomous positioning model according to the collinear geometrical relationship of the initial coordinate position and the image point coordinate of the target anchor bolt and the coordinate of the optical center of the camera.
8. The drone control system of claim 6, wherein the flight control module includes:
the equation set establishing submodule is used for establishing an overdetermined equation set related to the coordinate position of the unmanned aerial vehicle according to the multipoint geometric constraint relation specified by the autonomous positioning model;
the third coordinate acquisition sub-module is used for solving the overdetermined equation set by using a least square estimation method and acquiring the coordinate position of the unmanned aerial vehicle in the flight process in real time;
and the flight parameter adjusting submodule is used for performing negative feedback adjustment on the flight parameters of the unmanned aerial vehicle according to the coordinate position of the unmanned aerial vehicle in the flight process and the initial coordinate position of the target anchor bolt.
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