Ultra-large plane / curved surface feature measurement method and system based on dual UAV cooperative light source targets

Abstract

The present invention discloses a method and system for measuring the features of a very large plane / curved surface based on a dual-UAV collaborative light source target, including: the present invention utilizes ultra-wideband tag positioning and a pulse signal arrival time difference algorithm to obtain the position of the UAV, combines the UAV's dynamic model and path planning algorithm, generates a smooth and accurate flight trajectory for the two UAVs, and realizes collaborative path planning of the dual UAVs. The present invention can perform precise measurements in complex environments, effectively solves the positioning problem of UAVs in complex environments, and improves the flight safety and stability of UAVs. The present invention can realize automatic measurement, reduce the complexity and difficulty of manual operation, and improve the convenience and reliability of measurement. This will greatly reduce manual errors and improve the accuracy and consistency of measurements.

Description

Technical Field

[0001] The invention belongs to the technical field of unmanned aerial vehicles, and relates to a method and system for measuring super-large plane / curved surface features based on dual unmanned aerial vehicles cooperative light source targets. Background Art

[0002] SAR antenna is an antenna that uses synthetic aperture radar technology for high-resolution imaging. It has a wide range of applications in earth observation, navigation, communication and other fields. However, the performance and imaging quality of SAR antennas depend not only on their electromagnetic characteristics, but also on the accuracy of their mechanical structure. The mechanical structure accuracy of SAR antennas mainly includes parameters such as the shape, size and flatness of the antenna panel, which will affect the performance indicators such as the antenna's pointing, attitude, phase and gain. During on-orbit operation, due to the complexity and variability of the space environment, the surface accuracy of the SAR antenna panel will change, resulting in a decrease in the performance and imaging quality of the antenna. For example, due to factors such as temperature changes, solar radiation, air resistance, and small collisions, the antenna panel will produce thermal stress, thermal deformation, and thermal expansion, causing the shape and size of the antenna panel to change, thereby affecting the antenna's pointing and attitude. In addition, due to the unevenness of the antenna panel, the phase and gain of the antenna will change, causing the amplitude of the echo signal to be modulated, resulting in blurred imaging and reduced resolution, thereby affecting the imaging results. Therefore, in order to ensure the performance and imaging quality of the SAR antenna, it is necessary to measure the surface accuracy of the antenna panel in real time on-orbit.

[0003] However, there are many difficulties and challenges in collecting information and measuring the shape and surface accuracy of large SAR planes on orbit. First, the size of a large SAR plane exceeds the range of a single measurement, which may make it difficult to cover the information of the entire plane in one acquisition. Multiple measurements and data splicing are required, which increases the time and complexity of the measurement. Secondly, in order to improve the accuracy and stability of the measurement, some reflective markers need to be pasted on the surface of the antenna as a reference for measurement, but this will damage the antenna coating, affect the electromagnetic characteristics and performance of the antenna, and also increase the weight of the antenna, reducing the reliability and life of the antenna. Thirdly, in order to measure the antenna surface on orbit, equipment such as mechanical arms and binocular photography are used, but the movement of these equipment will cause vibration and deformation of the antenna, which will in turn affect its shape and position, thereby affecting the performance and accuracy of the antenna. In addition, the installation and maintenance of these equipment will also increase the cost and risk of the antenna. Therefore, how to observe and measure large SAR planes on orbit with high precision, high efficiency and low cost is an urgent problem to be solved. Summary of the invention

[0004] The purpose of the present invention is to solve the problems in the prior art that multiple measurements and data stitching are required to measure a large SAR plane, reflective markers affect the electromagnetic characteristics and performance of the antenna, and photographic equipment causes vibration and deformation of the antenna, and to provide a method and system for measuring ultra-large plane / curved surface features based on dual UAV collaborative light source targets.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions:

[0006] The ultra-large plane / curved surface feature measurement method based on dual UAV cooperative light source targets includes:

[0007] Step 1: Based on the time difference between adjacent pulse signals sent by the drone and arriving at the base station, the three-dimensional coordinates of the drone are obtained;

[0008] Step 2: Based on the acquired three-dimensional coordinates of the drones, and in combination with the dynamic model and path planning algorithm of the drones, the flight routes of the two drones are determined; the two drones are separated by a preset distance and move along the same flight route, and the two drones carry an optical projector and a binocular vision camera respectively;

[0009] Step 3: After the drone carrying the optical projector projects the light source target onto the antenna panel, the drone carrying the binocular vision camera photographs the light source target on the antenna panel to obtain the three-dimensional coordinates of the light source target;

[0010] Step 4: Repeat step 3 until the binocular vision camera captures a complete image of the antenna panel; perform coordinate transformation on the overlapping area of ​​the light source target of two adjacent images, convert the three-dimensional coordinate points of the light source target on all subsequent images to the coordinate system of the first image, fit and calculate the errors of the plane / curved surface, and obtain the characteristics of the entire antenna panel.

[0011] A further improvement of the present invention is:

[0012] Furthermore, based on the time difference between adjacent pulse signals sent by the drone and arriving at the base station, the three-dimensional coordinates of the drone are obtained. Specifically, the ultra-wideband tag carried by the drone periodically sends pulse signals to the base station, and the positioning method based on the time difference between adjacent pulse signals is used to obtain the three-dimensional coordinates of the drone. The specific method is as follows:

[0013] Assume that there are G randomly distributed base stations in three-dimensional space, receiving signals transmitted by the same target signal source, and the coordinates of the base stations are u i =[x i ,y i ,z i], i = 1, 2, ..., G, the target position coordinates are u0 = [x0, y0, z0]; then the time difference between the target signal reaching the i-th receiver and the first receiver is:

[0014]

[0015] Among them, c represents the signal propagation speed; G is not less than 4;

[0016] The positioning method using the arrival time difference of adjacent pulse signals obtains the three-dimensional coordinates of the drone, specifically:

[0017] The measured value τ of the i-th base station i1 and theoretical value The differences between are:

[0018]

[0019] The residual sum of squares of all base stations is:

[0020]

[0021] By iteratively updating the position estimate F(u), the value of the objective function is gradually reduced until a position estimate is found that minimizes the sum of squared residuals:

[0022]

[0023] Among them, u k is the position estimate of the kth iteration, a k is the step size of the kth iteration, is the objective function in F(u k ) is the gradient at .

[0024] Furthermore, based on the obtained three-dimensional coordinates of the UAVs, combined with the UAVs' dynamic model and path planning algorithm, the flight routes of the two UAVs are determined, specifically:

[0025] Translation of the center of mass of the drone:

[0026]

[0027] The drone rotates around the center of mass:

[0028]

[0029] Where F is the total external force acting on the drone, m is the mass of the drone, v is the velocity of the drone, M is the total external torque acting on the drone, J is the inertia matrix, and ω is the angular velocity;

[0030] Based on global path planning, an optimal and safe flight path is preset in a known environment as the flight route of the drone.

[0031] Furthermore, the three-dimensional coordinates of the light source target are obtained, specifically: using an optical projector to project the light source target on the plane / curved surface to be measured; using a binocular vision camera to capture an image of the antenna panel containing the light source target, and performing epipolar correction on the image captured by the binocular vision camera, and then using threshold segmentation to highlight the light source target, and obtaining the pixel coordinates p(x, y) of the light source target; and converting the pixel coordinates p(x, y) of the light source target to the image coordinate system, and using the triangulation principle to obtain the coordinates P(X, Y, Z) of the light source target in the camera coordinate system.

[0032] Furthermore, assuming that (u0, v0) in the image coordinate system corresponds to the origin of the pixel coordinate system, the projection coordinates of the pixel on the x and y axes are dx and dy, respectively, that is, 1piexl = dx (mm) = dy (mm); then the coordinates (u, v) of the pixel in the image coordinate system are expressed as:

[0033]

[0034] Converted to matrix and vector form:

[0035]

[0036] Since the image coordinate system and the camera coordinate system are parallel, the focal length f of the camera is the length of the line segment connecting the origins of the two coordinate systems, so the projection relationship between the two coordinate systems is established; suppose there is a point P(X c ,Y c ,Z c ), according to the triangle similarity principle:

[0037]

[0038] That is, we get:

[0039]

[0040] It can be expressed in matrix and vector form as:

[0041]

[0042] The distance x between the two imaging points and the edge of the camera can be obtained by measuring l 、x r , then the parallax d of the actual point in the left and right cameras is:

[0043] d=|x l -x r | (12)

[0044] According to the triangle similarity principle, the z-axis coordinate value Z of point P can be obtained c :

[0045]

[0046] Where: f is the focal length of the camera; T is the distance between the optical centers of the left and right cameras, i.e. the baseline distance;

[0047] Any point P(X c ,Y c ,Z c ) to pixel coordinate system is:

[0048]

[0049] Thereby obtaining the three-dimensional coordinates of the light source target in the camera coordinate system.

[0050] Furthermore, coordinate transformation is performed on the overlapping area of ​​the light source target, and the three-dimensional coordinate points of the light source target on all subsequent images are converted to the coordinate system of the first image. The features of the plane / curved surface to be measured are calculated and analyzed to obtain the features of the entire antenna panel, specifically:

[0051] The point correspondence between the point clouds in the camera coordinate system obtained by two consecutive images is obtained by the iterative closest point algorithm; the iterative closest point algorithm finds the best transformation between the source point cloud and the target point cloud to align the two to the greatest extent; then the distance value between the source point cloud and the target point cloud after matching by the iterative closest point algorithm is calculated for each point. The specific method is as follows:

[0052]

[0053] Among them, F(R,t,i) is the distance between the i-th point in the source point cloud and the corresponding point in the target point cloud, R is the optimal rotation matrix obtained by the iterative closest point algorithm, and t is the translation vector obtained by the iterative closest point algorithm. is a point in the source point cloud, is the corresponding point in the target point cloud; the source point cloud is the coordinate point extracted from the image taken at the Kth time; the target point cloud is the coordinate point extracted from the image taken at the K+1th time;

[0054] Select the Q points closest to each other and solve the coordinate transformation matrix through singular value decomposition, as follows:

[0055] Use the overlapping area of ​​the light source target to perform coordinate transformation. is the three-dimensional coordinate of the common point in the A coordinate system, is the three-dimensional coordinate of the common point in the B coordinate system; obtained from the three-dimensional coordinate transformation model, the three-dimensional coordinate transformation equations of the A and B coordinate systems are as follows:

[0056]

[0057]

[0058] Where: T represents the translation vector of the coordinate origin, s is the scale factor, R is the rotation matrix from the A coordinate system to the B coordinate system, and R_T represents the homogeneous transformation matrix;

[0059] After obtaining the transformation matrix of the two coordinate systems using the common target in the overlapped area, all points in the B coordinate system are transformed into the A coordinate system using the matrix; then the n coordinate points obtained by binocular vision measurement are fitted into a plane / surface based on the least squares method, and the fitting surface is used as the reference surface of the spaceborne SAR antenna. Each spatial point is regarded as a point on the actual fitting surface, and the deviation of each point from the fitting surface is obtained, thereby obtaining its plane / surface error;

[0060] For large planes, the offset is:

[0061]

[0062] Where: z ij is the coordinate of the original coordinate point in the Z direction; z is the coordinate of the projection of the point on the reference plane; It is the legalization factor;

[0063] The flatness error is defined as:

[0064] E=max (e i )-min(e i ) (19)

[0065] Where, E is the flatness error;

[0066] For large surfaces, all points of the actual surface are projected onto a surface tangent to the reference surface, and then the maximum height difference of the projection points is calculated to obtain the surface error.

[0067] Ultra-large plane / curved surface feature measurement system based on dual UAV cooperative light source targets, including:

[0068] A first acquisition module, wherein the first acquisition module acquires the three-dimensional coordinates of the drone based on the time difference between adjacent pulse signals sent by the drone and arriving at the base station;

[0069] A determination module, wherein the determination module determines the flight paths of the two drones based on the acquired three-dimensional coordinates of the drones and in combination with a dynamic model and a path planning algorithm of the drones;

[0070] A second acquisition module, in which after the drone carrying the optical projector projects the light source target onto the antenna panel, the drone carrying the binocular vision camera photographs the light source target on the antenna panel to obtain the three-dimensional coordinates of the light source target;

[0071] A coordinate transformation module is used to transform the overlapping area of ​​the light source target of two adjacent images, convert the three-dimensional coordinate points of the light source target on all subsequent images to the coordinate system of the first image, fit and calculate the errors of the plane / curved surface, and obtain the characteristics of the entire antenna panel.

[0072] A terminal device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above method when executing the computer program.

[0073] A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above method are implemented.

[0074] Compared with the prior art, the present invention has the following beneficial effects:

[0075] The present invention uses ultra-wideband tag positioning and pulse signal arrival time difference algorithm to obtain the position of the drone, and combines the dynamic model and path planning algorithm of the drone to generate a smooth and accurate flight trajectory for the two drones, thereby realizing the collaborative path planning of the two drones. The present invention can perform precise measurements in complex environments, effectively solve the positioning problem of drones in complex environments, and improve the flight safety and stability of drones.

[0076] Furthermore, the present invention can realize automatic measurement, reduce the complexity and difficulty of manual operation, and improve the convenience and reliability of measurement. This will greatly reduce manual errors and improve the accuracy and consistency of measurement.

[0077] Furthermore, the present invention measures antenna panel data through the collaborative flight of two UAVs, without the need to stick any markers on the antenna surface, thus avoiding damage and impact on the antenna. At the same time, there is no need to use equipment such as a robotic arm, thus reducing interference and risk to the antenna. The present invention achieves fast, accurate, and reliable measurement of ultra-large plane / curved surface features, and provides basic data for subsequent calculation and analysis of features such as shape, size, and direction. BRIEF DESCRIPTION OF THE DRAWINGS

[0078] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0079] Figure 1 It is a schematic diagram of the process of measuring the characteristics of a very large plane / curved surface based on dual UAV cooperative light source targets of the present invention;

[0080] Figure 2 It is a schematic flow chart of a method for measuring super-large plane / curved surface features based on dual UAV cooperative light source targets of the present invention;

[0081] Figure 3 It is a structural schematic diagram of the ultra-large plane / curved surface feature measurement system based on dual UAV cooperative light source targets of the present invention;

[0082] Figure 4 It is a schematic diagram of the technical solution flow of an embodiment of the present invention;

[0083] Figure 5 This is a schematic diagram of the structure of the optical projector and UWB tag carried by the drone according to an embodiment of the present invention;

[0084] Figure 6 This is a schematic diagram of the structure of a drone carrying binocular vision and UWB tags according to an embodiment of the present invention;

[0085] Figure 7 A schematic diagram of a drone path planning according to an embodiment of the present invention;

[0086] Figure 8 Schematic diagram of coordinate transformation process according to an embodiment of the present invention. DETAILED DESCRIPTION

[0087] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, 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 part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings here can be arranged and designed in various different configurations.

[0088] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention claimed for protection, but merely represents selected embodiments of the present invention. Based on the embodiments of 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.

[0089] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, further definition and explanation thereof is not required in subsequent drawings.

[0090] In the description of the embodiments of the present invention, it should be noted that if the terms "upper", "lower", "horizontal", "inner", etc. indicate an orientation or positional relationship based on the orientation or positional relationship shown in the drawings, or the orientation or positional relationship in which the product of the invention is usually placed when in use, it is only for the convenience of describing the present invention and simplifying the description, and does not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention. In addition, the terms "first", "second", etc. are only used to distinguish the description, and cannot be understood as indicating or implying relative importance.

[0091] In addition, if the term "horizontal" appears, it does not mean that the component must be absolutely horizontal, but can be slightly tilted. For example, "horizontal" only means that its direction is more horizontal than "vertical", which does not mean that the structure must be completely horizontal, but can be slightly tilted.

[0092] In the description of the embodiments of the present invention, it is also necessary to explain that, unless otherwise clearly specified and limited, the terms "set", "install", "connect", and "connect" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal connection of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0093] The present invention is further described in detail below in conjunction with the accompanying drawings:

[0094] See also Figure 1 The present invention discloses a schematic diagram of a process for measuring the characteristics of a large plane / curved surface based on a dual-UAV cooperative light source target, including: a large plane 1, a UAV 2 carrying an optical projector and a UWB tag, and a UAV 3 carrying a binocular vision camera and a UWB tag. Through the cooperation of the dual UAVs, the optical projector is used to generate a light source target on the antenna panel, and the binocular vision camera is used to capture an image containing the light source target.

[0095] See also Figure 2 The present invention discloses a method for measuring ultra-large plane / curved surface features based on dual UAV cooperative light source targets, comprising:

[0096] S101, obtaining the three-dimensional coordinates of the drone based on the time difference between adjacent pulse signals sent by the drone and arriving at the base station.

[0097] The ultra-wideband (UWB) tag carried by the drone periodically sends a pulse signal to the UWB base station, and then uses the pulse signal arrival time difference (TDOA) positioning method to calculate the three-dimensional coordinates of the drone. The specific method is as follows:

[0098] Assume that there are G (G ≥ 4) randomly distributed base stations in three-dimensional space, receiving signals transmitted by the same target signal source, and the base station position coordinates are u i =[x i ,y i ,z i ], i = 1, 2, ..., G, the target position coordinates are u0 = [x0, y0, z0]. Then the time difference between the target signal reaching the i-th receiver and the reference receiver (the first receiver) is:

[0099]

[0100] In the formula, c represents the signal propagation speed;

[0101] Since the TDOA positioning problem is to solve the intersection of multiple hyperbolic equations, but in practical applications, there are errors in TDOA measurement, which will make the equation group have no strict solution, so it is necessary to optimize the approximate solution method to solve it. The nonlinear least squares method is used to fit the error between the measured data and the theoretical model. By minimizing the residual sum of squares, an approximate solution can be obtained. The specific steps are:

[0102] The measured value τ of the i-th base station i1 and theoretical value The differences between are:

[0103]

[0104] The residual sum of squares of all base stations is:

[0105]

[0106] By iteratively updating the position estimate F(u), the value of the objective function is gradually reduced until a position estimate is found that minimizes the sum of squared residuals:

[0107]

[0108] Among them, u k is the position estimate of the kth iteration, a k is the step size of the kth iteration, is the objective function in F(u k ) is the gradient at .

[0109] S102, based on the obtained three-dimensional coordinates of the drones, and in combination with the dynamic model and path planning algorithm of the drones, the flight routes of the two drones are determined; the two drones are separated by a preset distance and move along the same flight route, and the two drones carry an optical projector and a binocular vision camera respectively.

[0110] In the dual drones, one drone is equipped with an optical projector, which is responsible for projecting a high-contrast, regular light source target on the target plane or curved surface; the other drone is equipped with a binocular vision camera, which is used to capture the image of the light source target and calculate the three-dimensional coordinates of the target. This collaborative working mode can improve measurement efficiency, allowing drones to quickly and accurately complete a wide range of feature measurement tasks.

[0111] After using TDOA to measure the three-dimensional coordinates of the UAV, the UAV dynamics model is used to achieve closed-loop control of the UAV position. The specific method is as follows:

[0112] Translation of the center of mass of the drone:

[0113]

[0114] The drone rotates around the center of mass:

[0115]

[0116] Among them, F is the total external force acting on the drone, m is the mass of the drone, v is the speed of the drone, M is the total external torque acting on the drone, J is the inertia matrix, and ω is the angular velocity.

[0117] Finally, global path planning is used to preset an optimal and safe flight path in a known environment, thereby improving the accuracy and efficiency of the measurement.

[0118] S103, after the drone carrying the optical projector projects the light source target onto the antenna panel, the drone carrying the binocular vision camera photographs the light source target on the antenna panel to obtain the three-dimensional coordinates of the light source target.

[0119] First, an optical projector is used to project a high-contrast, regular light source target on the plane / curved surface to be measured to improve the brightness and stability of the light source target; then a binocular vision camera is used to capture the antenna panel image containing the light source target, and then the images of the left and right cameras are polar corrected and the light source target is highlighted by threshold segmentation to obtain its pixel coordinates p(x, y), which are then converted to the image coordinate system. Then, the coordinates P(X, Y, Z) of the light source target in the camera coordinate system are obtained by using the triangulation principle. The specific steps are as follows:

[0120] The pixel and image coordinate systems are in the same plane, but their coordinate origins and units are different. The coordinate unit of the former is pixel (pixel), while the coordinate unit of the latter is mm. The conversion can be achieved only by transforming the x and y coordinates. Assume that (u0, v0) in the image coordinate system corresponds to the origin of the pixel coordinate system, and the projection coordinates of each pixel on the x and y axes are dx and dy respectively, that is, 1piexl = dx (mm) = dy (mm). Then the coordinates of the pixel coordinates in the line coordinate system (u, v) are as follows:

[0121]

[0122] Converted to matrix and vector form:

[0123]

[0124] Since the image coordinate system and the camera coordinate system are parallel, the focal length f of the camera is the length of the line segment connecting the origins of the two coordinate systems, so the projection relationship between the two coordinate systems can be established. Assume that there is a point P(X c ,Y c ,Z c ), according to the triangle similarity principle:

[0125]

[0126] You can get:

[0127]

[0128] It can be expressed in matrix and vector form as:

[0129]

[0130] The distance x between the two imaging points and the edge of the camera can be obtained by measuring l 、x r , then the parallax d of the actual point in the left and right cameras is:

[0131] d=|x l -x r |

[0132] According to the triangle similarity principle, the z-axis coordinate value Z of point P can be obtained c :

[0133]

[0134] Where: f is the focal length of the camera; T is the distance between the optical centers of the left and right cameras, i.e. the baseline distance.

[0135] Any point P(X c ,Yc ,Z c ) to pixel coordinate system is:

[0136]

[0137] Thereby obtaining the three-dimensional coordinates of the light source target in the camera coordinate system.

[0138] S104, repeat S103 until the binocular vision camera captures a complete image of the antenna panel; and perform coordinate transformation on the overlapping area of ​​the light source target of two adjacent captured images, convert the three-dimensional coordinate points of the light source target on all subsequent images to the coordinate system of the first image, fit and calculate the errors of the plane / curved surface, and obtain the characteristics of the entire antenna panel.

[0139] First, the iterative closest point algorithm (ICP) is used to obtain the point correspondence between the point clouds in the camera coordinate system obtained from two consecutive images. ICP can find the best transformation between the source point cloud (the coordinate points extracted from the image taken at the Kth time) and the target point cloud (the coordinate points extracted from the image taken at the K+1th time) to align the two to the greatest extent. Then for each point, the distance value between the source point cloud and the target point cloud after ICP matching is calculated. The specific method is as follows:

[0140]

[0141] Among them, F(R,t,i) is the distance between the i-th point in the source point cloud and the corresponding point in the target point cloud, R is the best rotation matrix obtained by ICP, and t is the translation vector obtained by ICP. is a point in the source point cloud, are the corresponding points in the target point cloud.

[0142] To improve the matching accuracy, select the Q points with the closest distance, where Q represents the number of points with the closest distance selected from the coordinate points with successful matching obtained in the Kth shot and the K+1th shot, and then use the singular value decomposition (SVD) to solve the coordinate transformation matrix, as follows:

[0143] Use the overlapping area of ​​the light source target to perform coordinate transformation. is the three-dimensional coordinate of the common point in the A coordinate system, is the three-dimensional coordinate of the common point in the B coordinate system. According to the three-dimensional coordinate transformation model, the three-dimensional coordinate transformation equations of the A and B coordinate systems are as follows:

[0144]

[0145]

[0146] Among them, T represents the translation vector of the coordinate origin, s is the scale factor, R is the rotation matrix from the A coordinate system to the B coordinate system, and R_T represents the homogeneous transformation matrix.

[0147] After obtaining the transformation matrix of the two coordinate systems using the common target in the overlapped area, use the matrix to transform all points in the B coordinate system to the A coordinate system. Then, the n coordinate points obtained by binocular vision measurement are fitted into a plane / curved surface based on the least squares method, and the fitted surface is used as the reference surface of the spaceborne SAR antenna. Each spatial point is regarded as a point on the actual fitting surface, and the deviation of each point to the fitting surface is obtained, thereby obtaining its plane / curved surface error;

[0148] For large planes, the offset is:

[0149]

[0150] Where: z ij is the coordinate of the original coordinate point in the Z direction; z is the coordinate of the projection of the point on the reference plane; It is the legalization factor;

[0151] The flatness error is defined as:

[0152] E=max(e i )-min(e i )

[0153] Where E is the flatness error.

[0154] For large surfaces, all points on the actual surface are projected onto a surface tangent to the reference surface, and then the maximum height difference of the projected points is calculated as the surface error.

[0155] See also Figure 3 The present invention discloses a large plane / curved surface feature measurement system based on dual UAV cooperative light source targets, comprising:

[0156] A first acquisition module, wherein the first acquisition module acquires the three-dimensional coordinates of the drone based on the time difference between adjacent pulse signals sent by the drone and arriving at the base station;

[0157] A determination module, wherein the determination module determines the flight paths of the two drones based on the acquired three-dimensional coordinates of the drones and in combination with a dynamic model and a path planning algorithm of the drones;

[0158] A second acquisition module, in which after the drone carrying the optical projector projects the light source target onto the antenna panel, the drone carrying the binocular vision camera photographs the light source target on the antenna panel to obtain the three-dimensional coordinates of the light source target;

[0159] A coordinate transformation module is used to transform the overlapping area of ​​the light source target of two adjacent images, convert the three-dimensional coordinate points of the light source target on all subsequent images to the coordinate system of the first image, fit and calculate the errors of the plane / curved surface, and obtain the characteristics of the entire antenna panel.

[0160] Example: See Figure 4 , which shows a schematic diagram of the technical solution process of the present invention. UWB technology is used between two drones, which has high positioning accuracy in complex environments. The position of the drone is obtained by measuring the time difference of arrival of the pulse signal (TDOA). Finally, based on the dynamic model of the drone and combined with global path planning, an optimal and safe flight path is preset to improve the accuracy and efficiency of the measurement. In addition, drone 1 carries a binocular vision camera to shoot the light source target projected on the antenna panel by the optical projector carried by drone 2. The coordinates of the light source target in the camera coordinate system are obtained by extracting pixel coordinates and using the triangulation principle. Through coordinate transformation, all coordinates are unified into the same coordinate system, and the least squares method is used to fit the features of the super-large plane / curved surface.

[0161] See also Figure 5 , the figure shows the structure of the drone carrying the optical point meter and the UWB tag, which includes: drone 1, UWB tag 2, laser emitter 3, grating 4, grating limit cover 5. The laser emitter 3, grating 4, and grating limit cover 5 constitute the optical point meter. In order to achieve the purpose of the present invention, the drone 3 is equipped so that the optical point meter can be fixedly connected to the drone 1 through bolts and nuts, and the UWB tag 2 can be tightly connected to the drone 1 through the UWB's own thread.

[0162] See also Figure 6 , the figure shows the structure of a drone carrying a binocular vision camera and a UWB tag, including: a drone 1, a UWB tag 2, and a camera 3. The binocular vision camera is composed of two cameras 3 with the same configuration. In order to achieve the purpose of the present invention, the drone 1 is tooled so that the camera 3 can be fixedly connected to the drone 1 through bolts and nuts, and the UWB tag 2 can be tightly connected to the drone 1 through the UWB's own thread.

[0163] See also Figure 7 ,The figure shows the UAV path planning process. The UWB tag carried by the UAV is used to periodically send pulse signals to the UWB base station. The UWB base station is used to receive and measure the arrival time of the pulse signal. The loT positioning platform receives the base station measurement data and uses the arrival time difference to complete high-precision positioning. Combined with the UAV's dynamic model and global path planning, an optimal and safe flight path is preset.

[0164] See also Figure 8, Schematic diagram of the coordinate transformation process. The specific steps are as follows: After obtaining the three-dimensional coordinates of the light source target in the multiple sets of images in the corresponding camera coordinate system, first use the iterative closest point algorithm (ICP) to obtain the point correspondence between the point clouds in the camera coordinate system obtained by the two adjacent images. ICP can find the optimal transformation between the source point cloud (the coordinate points extracted from the image taken for the Kth time) and the target point cloud (the coordinate points extracted from the image taken for the K+1th time) to align the two to the greatest extent. Then for each point, calculate the distance value between the source point cloud and the target point cloud after ICP matching. The specific method is as follows:

[0165]

[0166] Among them, F(R,t,i) is the distance between the i-th point in the source point cloud and the corresponding point in the target point cloud, R is the best rotation matrix obtained by ICP, and t is the translation vector obtained by ICP. is a point in the source point cloud, are the corresponding points in the target point cloud.

[0167] To improve the matching accuracy, select the Q points closest to each other, and then use singular value decomposition (SVD) to solve the coordinate transformation matrix, as follows:

[0168] Use the overlapping area of ​​the light source target to perform coordinate transformation. is the three-dimensional coordinate of the common point in the A coordinate system, is the three-dimensional coordinate of the common point in the B coordinate system. According to the three-dimensional coordinate transformation model, the three-dimensional coordinate transformation equations of the A and B coordinate systems are as follows:

[0169]

[0170]

[0171] Where: T represents the translation vector of the coordinate origin, s is the scale factor, R is the rotation matrix from the A coordinate system to the B coordinate system, and R_T represents the homogeneous transformation matrix.

[0172] After obtaining the transformation matrix of the two coordinate systems using the common target in the overlapped area, use the matrix to transform all points in the B coordinate system to the A coordinate system. Then, the n coordinate points obtained by binocular vision measurement are fitted into a plane / curved surface based on the least squares method, and the fitted surface is used as the reference surface of the spaceborne SAR antenna. Each spatial point is regarded as a point on the actual fitting surface, and the deviation of each point to the fitting surface is obtained, thereby obtaining its plane / curved surface error;

[0173] For large planes, the offset is:

[0174]

[0175] Where: z ij is the coordinate of the original coordinate point in the Z direction; z is the coordinate of the projection of the point on the reference plane; It is the legalization factor;

[0176] The flatness error is defined as:

[0177] E=max(e i )-min(e i )

[0178] Where E is the flatness error.

[0179] For large surfaces, all points on the actual surface are projected onto a surface tangent to the reference surface, and then the maximum height difference of the projected points is calculated as the surface error.

[0180] A terminal device is provided in one embodiment of the present invention. The terminal device of this embodiment includes: a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps in the above-mentioned method embodiments are implemented. Alternatively, when the processor executes the computer program, the functions of the modules / units in the above-mentioned device embodiments are implemented.

[0181] The computer program may be divided into one or more modules / units, and the one or more modules / units are stored in the memory and executed by the processor to accomplish the present invention.

[0182] The terminal device may be a computing device such as a desktop computer, a notebook, a PDA, a cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0183] The processor can be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.

[0184] The memory may be used to store the computer programs and / or modules, and the processor implements various functions of the terminal device by running or executing the computer programs and / or modules stored in the memory and calling the data stored in the memory.

[0185] If the module / unit integrated in the terminal device is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0186] The above are only preferred embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for measuring ultra-large plane / curved surface features based on dual UAV cooperative light source targets, characterized in that: include: Step 1: Based on the time difference between adjacent pulse signals sent by the drone and arriving at the base station, the three-dimensional coordinates of the drone are obtained; Step 2: Based on the acquired three-dimensional coordinates of the drones, and in combination with the dynamic model and path planning algorithm of the drones, the flight routes of the two drones are determined; the two drones are separated by a preset distance and move along the same flight route, and the two drones carry an optical projector and a binocular vision camera respectively; Step 3: After the drone carrying the optical projector projects the light source target onto the antenna panel, the drone carrying the binocular vision camera photographs the light source target on the antenna panel to obtain the three-dimensional coordinates of the light source target; Step 4: Repeat step 3 until the binocular vision camera captures a complete image of the antenna panel; perform coordinate transformation on the overlapping area of ​​the light source target of two adjacent captured images, convert the three-dimensional coordinate points of the light source target on all subsequent images to the coordinate system of the first image, fit and calculate the error of the plane / curved surface, and obtain the characteristics of the entire antenna panel, specifically: Step 4.1: Obtain the point correspondence between the point clouds in the camera coordinate system obtained by two consecutive images through the iterative closest point algorithm; the iterative closest point algorithm finds the best transformation between the source point cloud and the target point cloud to align the two to the greatest extent; then calculate the distance value between the source point cloud and the target point cloud after matching through the iterative closest point algorithm for each point; Step 4.2: Select the Q points closest to each other and solve the coordinate transformation matrix by singular value decomposition, as follows: Step 4.2.1: Use the overlapping area of ​​the light source target to perform coordinate transformation, and obtain the three-dimensional coordinate transformation equations of the A coordinate system and the B coordinate system from the three-dimensional coordinate transformation model; Step 4.2.2: After obtaining the transformation matrix of the two coordinate systems using the common target in the overlapping area, use the matrix to transform all points in the B coordinate system to the A coordinate system; then use the least squares method to fit the n coordinate points obtained by binocular vision measurement into a plane / surface, and use the fitting surface as the reference surface of the spaceborne SAR antenna. Take each spatial point as a point on the actual fitting surface, obtain the deviation of each point to the fitting surface, and then obtain its plane / surface error.

2. The method for measuring super-large plane / curved surface features based on dual UAV cooperative light source targets according to claim 1 is characterized in that: The method of obtaining the three-dimensional coordinates of the drone based on the time difference between adjacent pulse signals sent by the drone and arriving at the base station is specifically: the ultra-wideband tag carried by the drone periodically sends pulse signals to the base station, and the three-dimensional coordinates of the drone are obtained by using the positioning method of the time difference between adjacent pulse signals arriving. The specific method is as follows: Assume that there are G randomly distributed base stations in three-dimensional space, receiving signals transmitted by the same target signal source, and the coordinates of the base stations are , i=1,2,…,G, the target position coordinates are ; Then the time difference between the target signal reaching the i-th receiver and the first receiver is: Where, c represents the signal propagation speed; G is not less than 4; The positioning method using the arrival time difference of adjacent pulse signals is used to obtain the three-dimensional coordinates of the drone, specifically: The measured value of the i-th base station and theoretical value The differences between are: The residual sum of squares of all base stations is: Update the position estimate by iteratively , gradually reducing the value of the objective function until a position estimate is found that minimizes the sum of squared residuals: in, is the position estimate for the kth iteration, is the step size of the kth iteration, The objective function is The gradient at .

3. The method for measuring super-large plane / curved surface features based on dual UAV cooperative light source targets according to claim 2 is characterized in that: The flight routes of the two drones are determined based on the obtained three-dimensional coordinates of the drones and in combination with the dynamic model and path planning algorithm of the drones, specifically: Translation of the center of mass of the drone: The drone rotates around the center of mass: in, is the net external force acting on the drone, is the quality of the drone, is the speed of the drone, is the total external torque acting on the drone, is the inertia matrix, is the angular velocity; Based on global path planning, an optimal and safe flight path is preset in a known environment as the flight route of the drone.

4. The method for measuring super-large plane / curved surface features based on dual UAV cooperative light source targets according to claim 3 is characterized in that: The three-dimensional coordinates of the light source target are obtained by: using an optical projector to project the light source target on the plane / curved surface to be measured; using a binocular vision camera to capture an image of the antenna panel containing the light source target, and performing epipolar correction on the image captured by the binocular vision camera, and then using threshold segmentation to highlight the light source target to obtain the pixel coordinates of the light source target. ; And the pixel coordinates of the light source target Convert to the image coordinate system and use the triangulation principle to obtain the coordinates of the light source target in the camera coordinate system .

5. The method for measuring super-large plane / curved surface features based on dual UAV cooperative light source targets according to claim 4 is characterized in that: Assume that the image coordinate system Corresponding to the origin of the pixel coordinate system, the projection coordinates of the pixel on the x and y axes are dx and dy respectively, that is, ; then the pixel coordinates in the graph coordinate system are It is expressed as: Converted to matrix and vector form: Since the image coordinate system and the camera coordinate system are parallel, the focal length of the camera That is, the length of the line segment connecting the origins of the two coordinate systems, then the projection relationship between the two coordinate systems is established; assuming that there is a point in the camera coordinate system , according to the triangle similarity principle: That is, we get: It can be expressed in matrix and vector form as: The distance between the two imaging points and the edge of the camera can be obtained by measuring , , then the parallax d of the actual point in the left and right cameras is: The z-axis coordinate value of point P can be obtained by the triangle similarity principle : Where: f is the focal length of the camera; T is the distance between the optical centers of the left and right cameras, i.e. the baseline distance; Any point in the camera coordinate system The conversion to pixel coordinate system is: Thereby obtaining the three-dimensional coordinates of the light source target in the camera coordinate system.

6. The method for measuring super-large plane / curved surface features based on dual UAV cooperative light source targets according to claim 5 is characterized in that: Step 4.1 The specific method is as follows: in, is the distance between the i-th point in the source point cloud and the corresponding point in the target point cloud, R is the optimal rotation matrix obtained by the iterative closest point algorithm, t is the translation vector obtained by the iterative closest point algorithm, is a point in the source point cloud, is the corresponding point in the target point cloud; the source point cloud is the coordinate point extracted from the image taken at the Kth time; the target point cloud is the coordinate point extracted from the image taken at the K+1th time; Step 4.2.1 is as follows: Use the overlapping area of ​​the light source target to perform coordinate transformation. is the three-dimensional coordinate of the common point in the A coordinate system, is the three-dimensional coordinate of the common point in the B coordinate system; obtained from the three-dimensional coordinate transformation model, the three-dimensional coordinate transformation equations of the A and B coordinate systems are as follows: Where: T represents the translation vector of the coordinate origin, s is the scale factor, R is the rotation matrix from the A coordinate system to the B coordinate system, and R_T represents the homogeneous transformation matrix; In step 4.2.2, for large planes, the deviation is: in: is the coordinate of the original coordinate point in the Z direction; z is the coordinate of the projection of the point on the reference plane; It is the legalization factor; The flatness error is defined as: Where, E is the flatness error; For large surfaces, all points of the actual surface are projected onto a surface tangent to the reference surface, and then the maximum height difference of the projection points is calculated to obtain the surface error.

7. The ultra-large plane / curved surface feature measurement system based on dual UAV cooperative light source targets is characterized by: include: A first acquisition module, wherein the first acquisition module acquires the three-dimensional coordinates of the drone based on the time difference between adjacent pulse signals sent by the drone and arriving at the base station; A determination module, wherein the determination module determines the flight paths of the two drones based on the acquired three-dimensional coordinates of the drones and in combination with a dynamic model and a path planning algorithm of the drones; A second acquisition module, in which after the drone carrying the optical projector projects the light source target onto the antenna panel, the drone carrying the binocular vision camera photographs the light source target on the antenna panel to obtain the three-dimensional coordinates of the light source target; The coordinate transformation module performs coordinate transformation on the overlapping area of ​​the light source target of two adjacent images, converts the three-dimensional coordinate points of the light source target on all subsequent images to the coordinate system of the first image, fits and calculates the error of the plane / curved surface, and obtains the characteristics of the entire antenna panel, specifically: The point correspondence between the point clouds in the camera coordinate system obtained by two consecutive images is obtained by the iterative closest point algorithm; the iterative closest point algorithm finds the best transformation between the source point cloud and the target point cloud to align the two to the greatest extent; then, for each point, the distance value between the source point cloud and the target point cloud after matching by the iterative closest point algorithm is calculated; Select the Q points closest to each other and solve the coordinate transformation matrix through singular value decomposition, as follows: The overlapping area of ​​the light source target is used for coordinate transformation, and the three-dimensional coordinate transformation equations of the A coordinate system and the B coordinate system are obtained from the three-dimensional coordinate transformation model; after obtaining the transformation matrix of the two coordinate systems using the common target in the overlapping area, all points in the B coordinate system are transformed into the A coordinate system using the matrix; then, the n coordinate points obtained by binocular vision measurement are fitted into a plane / surface based on the least squares method, and the fitting surface is used as the reference surface of the spaceborne SAR antenna shape. Each spatial point is regarded as a point on the actual fitting surface, and the deviation of each point from the fitting surface is obtained, thereby obtaining its plane / surface error.

8. A terminal 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 steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

Images