Pose measurement method and device based on satellite target, medium and equipment

Abstract

The embodiment of the invention discloses a pose measurement method and device based on a satellite target, a medium and equipment, and relates to the technical field of satellite pose measurement, and the method comprises the steps: eliminating an abnormal region in a binary image through employing a recursive small region elimination technology, and obtaining a target region image; and obtaining relative attitude information of the target satellite and solving relative pose information according to the center coordinates of the reference marks and the center coordinates of the coplanar reference marks in the image. According to the method, the target image is binarized through the gray threshold to highlight the region where the reference mark is located, and the abnormal region generated by noise in the binarized image is eliminated by using the recursive small region elimination technology, so that the influence of other non-target patterns is reduced, and the accuracy of the target image is improved. And the center of the reference mark and the center of the coplanar reference mark in the image are obtained by using the edge coordinates of the target area, and the relative attitude of the satellite is solved, so that the attitude measurement precision of the satellite is improved.

Description

Technical Field

[0001] The present application relates to the technical field of satellite posture measurement, and in particular to a posture measurement method, device, medium and equipment based on a satellite target. Background Art

[0002] Satellite posture measurement involves determining the position and posture of the satellite in space. Satellite posture refers to the spatial pointing state of the satellite body in orbit. The origin of the rectangular coordinate system is placed on the star body. The Z axis pointing to the ground reflects the yaw direction, the Y axis reflects the pitch direction, and the X axis reflects the roll direction. Three-axis stabilization, spin stabilization, gravity gradient stabilization, etc. are usually used to maintain the stability of the posture. The existing technology realizes the real-time output of the posture parameters of the moving target in the video sequence for irregularly moving targets. However, due to many factors, the separation of the target features in the video image used for measurement is not accurate enough, which affects the accuracy of posture measurement. Summary of the invention

[0003] The main purpose of this application is to provide a satellite target-based posture measurement method, device, medium and equipment, aiming to solve the problem of low accuracy of satellite posture measurement in the prior art.

[0004] To achieve the above objectives, the technical solutions adopted in the embodiments of the present application are as follows:

[0005] In a first aspect, an embodiment of the present application provides a posture measurement method based on a satellite target, comprising the following steps:

[0006] Binarizing the target image of the target satellite based on the grayscale threshold to obtain a binary image;

[0007] The abnormal areas in the binary image are eliminated by using the recursive small area elimination technology to obtain the target area image; wherein the judgment condition of the recursive small area elimination is that the number of areas is equal to the number of reference marks;

[0008] According to the edge coordinates of the target area in the target area image, the center coordinates of the reference mark and the center coordinates of the coplanar reference mark in the image are obtained;

[0009] Obtaining relative attitude information of the target satellite according to the center coordinates of the reference mark and the center coordinates of the coplanar reference mark in the image;

[0010] The relative position and attitude information of the target satellite is obtained based on the relative position, coordinate transformation relationship and the relative attitude information of the target satellite.

[0011] In a possible implementation manner of the first aspect, obtaining the center coordinates of the reference mark and the center coordinates of the coplanar reference mark in the image according to the edge coordinates of the target area in the target area image, the method further includes:

[0012] Eliminate the stray points on the contour of the target area to obtain contour information;

[0013] Based on the Hu invariant moment principle, the most similar target regions whose number is equal to the number of reference markers are obtained in the contour information.

[0014] In a possible implementation manner of the first aspect, obtaining the center coordinates of the reference mark and the center coordinates of the coplanar reference mark in the image according to the edge coordinates of the target area in the target area image includes:

[0015] Obtaining the center coordinates of the reference mark according to the edge coordinates of the target area in the target area image;

[0016] The center coordinates of the coplanar reference markers in the image are obtained based on the coordinate average of the center coordinates of the coplanar reference markers.

[0017] In a possible implementation manner of the first aspect, obtaining relative attitude information of the target satellite according to the center coordinates of the reference marker and the center coordinates of the coplanar reference marker in the image includes:

[0018] Obtaining a relative pitch angle and a relative yaw angle of the target satellite according to the center coordinates of the coplanar reference marks in the image and the center coordinates of the non-coplanar reference marks;

[0019] The relative rolling angle is obtained according to the position of the line connecting the central coordinates of the reference marks relative to the physical coordinate axis of the image.

[0020] In a possible implementation manner of the first aspect, before obtaining the relative position and posture information of the target satellite according to the relative position, the coordinate transformation relationship, and the relative posture information of the target satellite, the method further includes:

[0021] The relative position is obtained according to the first pixel spacing, the second pixel spacing and the focal length of the camera; wherein the first pixel spacing is the pixel distance between two determined imaging points on the target image when the distance between the tracking satellite and the target satellite in the optical axis direction is the first distance, and the second pixel spacing is the pixel distance between the two determined imaging points on the target image when the distance is the second distance.

[0022] In a possible implementation manner of the first aspect, before binarizing the target image of the target satellite based on the grayscale threshold and obtaining the binarized image, the method further includes:

[0023] Performing filtering processing on the target image to obtain a filtered target image;

[0024] The target image of the target satellite is binarized based on the grayscale threshold to obtain a binarized image, including:

[0025] The filtered target image of the target satellite is binarized based on the grayscale threshold to obtain a binarized image.

[0026] In a possible implementation manner of the first aspect, the recursive small area elimination technology is a recursive small area elimination technology based on a gradually increasing number of pixels.

[0027] In a second aspect, an embodiment of the present application provides a posture measurement device based on a satellite target, comprising:

[0028] A binarization module, which is used to binarize the target image of the target satellite based on a grayscale threshold to obtain a binarized image;

[0029] The elimination module is used to eliminate abnormal areas in the binary image by using recursive small area elimination technology to obtain the target area image; wherein the judgment condition of recursive small area elimination is that the number of areas is equal to the number of reference marks;

[0030] A center acquisition module, which is used to obtain the center coordinates of the reference mark and the center coordinates of the coplanar reference mark in the image according to the edge coordinates of the target area in the target area image;

[0031] The attitude acquisition module is used to obtain the relative attitude information of the target satellite according to the center coordinates of the reference mark and the center coordinates of the coplanar reference mark in the image;

[0032] The posture acquisition module is used to obtain the relative posture information of the target satellite based on the relative position, coordinate conversion relationship and relative posture information of the target satellite.

[0033] In a third aspect, an embodiment of the present application provides a computer-readable storage medium storing a computer program. When the computer program is loaded and executed by a processor, it implements a satellite target-based posture measurement method as provided in any one of the first aspects above.

[0034] In a fourth aspect, an embodiment of the present application provides an electronic device, including a processor and a memory, wherein:

[0035] The memory is used to store computer programs;

[0036] The processor is used to load and execute a computer program so that the electronic device executes a satellite target-based posture measurement method as provided in any one of the first aspects above.

[0037] Compared with the prior art, the beneficial effects of this application are:

[0038] The embodiments of the present application propose a method, device, medium and equipment for measuring the position and posture of a satellite target. The method includes: binarizing the target image of the target satellite based on a grayscale threshold to obtain a binary image; using a recursive small area elimination technique to eliminate abnormal areas in the binary image to obtain a target area image; wherein the judgment condition for recursive small area elimination is that the number of areas is equal to the number of reference marks; according to the edge coordinates of the target area in the target area image, the center coordinates of the reference mark and the center coordinates of the coplanar reference mark in the image are obtained; according to the center coordinates of the reference mark and the center coordinates of the coplanar reference mark in the image, the relative posture information of the target satellite is obtained; according to the relative position, the coordinate conversion relationship and the relative posture information of the target satellite, the relative position and posture information of the target satellite is obtained. This application first binarizes the target image through a grayscale threshold to highlight the area where the benchmark mark is located. Secondly, it uses recursive small area elimination technology to eliminate abnormal areas caused by noise in the binary image, reduce the influence of other non-target patterns, and set the elimination judgment conditions so that the number of retained areas is equal to the number of benchmark marks of the target. Then, the edge coordinates of the target area representing the benchmark mark are used to obtain the center of the benchmark mark itself and the center of the coplanar benchmark mark in the image, solve the relative attitude of the satellite, and combine the relative position and coordinate conversion relationship to finally obtain the relative attitude of the satellite, thereby improving the satellite's posture measurement accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 A schematic diagram of the structure of an electronic device of a hardware operating environment involved in an embodiment of the present application;

[0040] Figure 2 A schematic diagram of a flow chart of a satellite target-based posture measurement method provided in an embodiment of the present application;

[0041] Figure 3 A schematic diagram of a binary image in a satellite target-based pose measurement method provided in an embodiment of the present application;

[0042] Figure 4 for Figure 3 The image after edge detection of the binarized image shown;

[0043] Figure 5 A schematic diagram of another binary image in the satellite target-based pose measurement method provided in an embodiment of the present application;

[0044] Figure 6 for Figure 5 The image after edge detection of the binarized image shown;

[0045] Figure 7A schematic diagram of the center points of coplanar reference marks in an image in a satellite target-based pose measurement method provided in an embodiment of the present application;

[0046] Figure 8 A schematic diagram of a satellite target-based pose measurement method provided in an embodiment of the present application in one implementation manner;

[0047] Fig. 9 A schematic diagram of a module of a satellite target-based posture measurement device provided in an embodiment of the present application;

[0048] Markings in the figure: 101 - processor, 102 - communication bus, 103 - network interface, 104 - user interface, 105 - memory. DETAILED DESCRIPTION

[0049] It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0050] Refer to the attached Figure 1 , attached Figure 1 This is a schematic diagram of the structure of an electronic device of the hardware operating environment involved in the embodiment of the present application. The electronic device may include: a processor 101, such as a central processing unit (CPU), a communication bus 102, a user interface 104, a network interface 103, and a memory 105. Among them, the communication bus 102 is used to realize the connection and communication between these components. The user interface 104 may include a display screen (Display), an input unit such as a keyboard (Keyboard), and the user interface 104 may also include a standard wired interface and a wireless interface. The network interface 103 may optionally include a standard wired interface and a wireless interface (such as a wireless fidelity (WIreless-FIdelity, WI-FI) interface). The memory 105 may optionally be a storage device independent of the aforementioned processor 101. The memory 105 may be a high-speed random access memory (RAM) memory, or it may be a stable non-volatile memory (NVM), such as at least one disk storage; the processor 101 may be a general-purpose processor, including a central processing unit, a network processor, etc., or it may be a digital signal processor, an application-specific integrated circuit, a field programmable gate array or other programmable logic device, a discrete gate or transistor logic device, or a discrete hardware component.

[0051] Those skilled in the art will appreciate that Figure 1 The structure shown in the figure does not constitute a limitation on the electronic device, and may include more or less components than shown in the figure, or combine certain components, or arrange the components differently.

[0052] As attached Figure 1 As shown, the memory 105 as a storage medium may include an operating system, a network communication module, a user interface module, and a satellite target-based posture measurement device.

[0053] In the attached Figure 1 In the electronic device shown, the network interface 103 is mainly used for data communication with the network server; the user interface 104 is mainly used for data interaction with the user; the processor 101 and the memory 105 in the present application can be set in the electronic device, and the electronic device calls the satellite target-based posture measurement device stored in the memory 105 through the processor 101, and executes the satellite target-based posture measurement method provided in the embodiment of the present application.

[0054] Refer to the attached Figure 2 Based on the hardware device of the foregoing embodiment, the embodiment of the present application provides a posture measurement method based on a satellite target, comprising the following steps:

[0055] S10: Binarizing the target image of the target satellite based on the grayscale threshold to obtain a binary image.

[0056] In the specific implementation process, the target satellite is the satellite that needs to be measured for posture. For its binarization, the quality of the output binary image is determined by setting the grayscale threshold. The grayscale peak value that gradually decreases from grayscale 255 can be used to determine the concentrated area of ​​the LED reference mark grayscale, and the peak grayscale value that exceeds a certain number of pixels in the range of 245-255 is found. In addition, considering that the grayscale of the edge of the area is smaller than that of the center of the area, the binarization threshold is set to be several grayscales smaller than that of the center of the area, thereby segmenting the target from the background, as shown in the attached figure. Figure 3 and attached Figure 5 As shown in the figure, they are different binary images. The binarization based on the grayscale threshold can be expressed as:

[0057]

[0058] Among them, f(x, y) is the input image, g(x, y) is the output image, Thresh is the grayscale threshold, and the grayscale threshold can also be written as follows:

[0059] Thresh=Thresh[(x,y),f(x,y),h(x,y)]

[0060] Among them, f(x,y) is the grayscale value at the pixel point (x,y), and h(x,y) represents the local properties of the point area.

[0061] In one embodiment, before binarizing the target image of the target satellite based on the grayscale threshold and obtaining the binarized image, the method further includes:

[0062] Performing filtering processing on the target image to obtain a filtered target image;

[0063] The target image of the target satellite is binarized based on the grayscale threshold to obtain a binarized image, including:

[0064] The filtered target image of the target satellite is binarized based on the grayscale threshold to obtain a binarized image.

[0065] In the specific implementation process, after the cooperative target image is collected by the optical camera, the image can be filtered to remove noise to improve image quality, that is, the noise of the target image can be suppressed while retaining the image detail features as much as possible. For example, the Gaussian smoothing filtering method is a linear smoothing filtering method. The basic principle is to use the Gaussian function to perform convolution operations on the image to achieve image smoothing and noise reduction.

[0066] S20: using a recursive small region elimination technique to eliminate abnormal regions in the binary image, and obtain a target region image; wherein the judgment condition for the recursive small region elimination is that the number of regions is equal to the number of reference marks.

[0067] In the specific implementation process, the image after binarization may have small areas due to the existence of noise, and the number of areas is greater than the number of benchmark marks, so these areas are eliminated using recursive small area elimination technology, such as using recursive small area elimination technology with gradually increasing number of pixels, the number of pixels increases gradually from 5 to 30, and the number of areas is used as the benchmark mark number as the judgment condition for elimination. For example, 5 benchmark marks are set, and the elimination judgment condition is set to the number of areas as 5. The binarized image after eliminating the abnormal area is the target area image.

[0068] S30: Obtaining the center coordinates of the reference mark and the center coordinates of the coplanar reference marks in the image according to the edge coordinates of the target area in the target area image.

[0069] In the specific implementation process, the posture measurement is mainly based on the detection of the LED reference mark of the cooperative target, and the subsequent posture estimation is performed. The target area mentioned here is the area representing the reference mark. These areas are elliptical, and the edge coordinates can be obtained using the edge detection algorithm, as shown in the attached figure. Figure 4 and attached Figure 6 , respectively Figure 3 and attached Figure 5The binary image shown is an image after edge detection, and then the center point of each target area is obtained, including a stereo point and multiple coplanar center points. For example, in the present embodiment, when five reference marks are used, a stereo point and the center coordinates of four coplanar reference marks in the image are included.

[0070] In one embodiment, according to the edge coordinates of the target area in the target area image, the center coordinates of the reference mark and the center coordinates of the coplanar reference mark in the image are obtained, and the method further includes:

[0071] Eliminate the stray points on the contour of the target area to obtain contour information;

[0072] Based on the Hu invariant moment principle, the most similar target regions whose number is equal to the number of reference markers are obtained in the contour information.

[0073] In the specific implementation process, after obtaining the edge coordinate data of each area in the binary image, it is very important to detect the required reference marks. Although the background in the space scene is relatively simple and there is less noise after filtering, the accurate detection of the elliptical area representing the reference mark is the guarantee for the subsequent pose calculation. There are two main steps: preliminary contour recognition and precise ellipse recognition based on similarity. First, the pattern matching is performed using the size of the elliptical area imaged by the target pattern in the image. After finding the point with the largest area as the starting point, the coordinates of the center points of all four areas in the image pixel coordinate system are calculated and averaged to obtain the center p of the entire target pattern. The remaining four images are then matched using the RANSAC algorithm.

[0074] Specifically, by searching for extreme points in the up, down, left, and right directions from the edge points and calculating the distance between them, the radius of the circumscribed circle of the contour is obtained, and the contour is preliminarily identified by combining the prior knowledge of the target. Suppose the edge point coordinate array C of a certain contour is C = {(x i ,y i ),1≤i≤n}, n is the number of edge points, then the area of ​​the region is:

[0075]

[0076] Any two points a on the edge of the contour i (x i ,y i ) and a j (x j ,y j ) is:

[0077]

[0078] Search the leftmost endpoint a0, the rightmost endpoint a1, the topmost endpoint a2, the bottommost endpoint a3 from the edge points, and the distance d between them ai,j Half of the maximum value in is the radius R of the circumscribed circle of the contour:

[0079] R = max{d ai,j} / 2,i,j=0,1,2,3

[0080] After obtaining some attributes of the contour edge, we can perform preliminary recognition of the contour based on the prior knowledge of the target and remove some stray points caused by environmental factors such as lighting. The final elliptical contour is:

[0081] 1) The circumscribed circle radii of the ellipse contours are similar in size and are both greater than the circumscribed circle radius threshold (T R );

[0082] 2) The number of edge points contained in the ellipse contour is similar and all of them are greater than the edge point number threshold (T n );

[0083] 3) After perspective transformation, the lens distortion will not be too large, and the circularity index c = S / (π.R 2 ) will not be too small.

[0084] After preliminary recognition of the contour, some elliptical areas can be detected. The number of elliptical areas is uncertain, but the desired elliptical contours are those that meet the number of reference marks. For the similarity detection method, the Hu invariant moment method is used. The algorithm calculates the similarity coefficients of all contours and the marked elliptical contours, and selects the contours by the similarity coefficients to retain the desired contours, as follows:

[0085] For contour A = {(x i ,y i ),1≤i≤n}, its p,q order statistical moments are:

[0086] α p,q =∑x i p .y i q

[0087] Then define the u and v order central moments of the edge as:

[0088]

[0089] In the formula,

[0090] Normalizing the central moment of the above formula, the normalized central moment is:

[0091]

[0092] Based on the first three central moments, the Hu moment set can be obtained:

[0093] h1 = γ 2,0 + γ 0,2

[0094] h2 = (γ 2,0 - γ 0,2 ) 2 + 4γ 1,1 2

[0095] h3 = (γ 3,0 - 3γ 1,2 ) 2 +(3γ 2,1 - γ 0,3 ) 2

[0096] h4 = (γ 3,0 + γ 1,2 ) 2 +(γ 2,1 + γ 0,3 ) 2

[0097] h5 = (γ 3,0 - 3γ 1,2 )(γ 3,0 + γ 1,2 )[(γ 3,0 + γ 1,2 ) 2 - 3(γ 2,1 + γ 0,3 ) 2 +(3γ 2,1 - γ 0,3 )(γ 2,1 + γ 0,3 )[3(γ 3,0 + γ 1,2 ) 2 -(γ 2,1 + γ 0,3 ) 2

[0098] h6 = (γ 2,0 - γ 0,2 )[(γ 3,0 + γ 1,2 ) 2 -(γ 2,1 + γ 0,3 ) 2 + 4γ 1,1 (γ 3,0 + γ 1,2 )(γ 2,1 + γ​0,3 )

[0099] h7=(3γ 2,1 -γ 0,3 )(γ 3,0 +γ 1,2 )[(γ 3,0 +γ 1,2 ) 2 -3(γ 2,1 +γ 0,3 ) 2 ]-(γ 3,0 -3γ 1,2 )(γ 2,1 +γ 0,3 )[3(γ 3,0 +γ 1,2 ) 2 -(γ 2,1 +γ 0,3 ) 2 ]

[0100] Each function in the above formula is invariant to translation, rotation and scale changes.

[0101] Suppose another contour B = {(x i ,y i ),1≤i≤m}, then according to the invariant moment characteristics of the edge shape, the three shape similarity coefficients with the contour A are:

[0102]

[0103] in,

[0104] When the two contours are exactly the same, S1=S2=S3=0. On the contrary, the worse the similarity between the two contours, the greater the similarity coefficient. By calculating the similarity coefficients of all contours and the marked elliptical contour, it can be seen that the similarity coefficient of the elliptical contour is much less than 0.05. By comparing the similarity coefficients, if S i If (i=1,2,3)>0.05, it is considered that it does not belong to the required target elliptical contour and is deleted and discarded. The HU invariant moment method is used to accurately identify the required elliptical area, and the five most similar elliptical areas are found from the image as the final pattern for pose solution.

[0105] In one embodiment, obtaining the center coordinates of the reference mark and the center coordinates of the coplanar reference marks in the image according to the edge coordinates of the target area in the target area image includes:

[0106] Obtaining the center coordinates of the reference mark according to the edge coordinates of the target area in the target area image;

[0107] The center coordinates of the coplanar reference markers in the image are obtained based on the coordinate average of the center coordinates of the coplanar reference markers.

[0108] In the specific implementation process, five reference marks are still used as an example. The above steps obtain the coordinates of the center points of the five pattern areas in the image. The coordinates of the four points in the image are (u a ,v a ), (u b ,v b ), (u c ,v c ), (u d ,v d ), as attached Figure 7 The coordinates of the three-dimensional points in the image are (u e ,v e ), as attached Figure 7 The area shown in e. Using (u p ,v p ) indicates that the center coordinates of the center p of the four coplanar patterns in the image are:

[0109]

[0110] S40: Obtaining relative attitude information of the target satellite according to the center coordinates of the reference mark and the center coordinates of the coplanar reference marks in the image.

[0111] In the specific implementation process, the solution of the relative attitude is the solution of the relative pitch angle, relative yaw angle and relative roll angle. The relative attitude information is represented by the center coordinate information of the reference mark of the target mentioned above, and the relative attitude can be obtained. Specifically:

[0112] According to the center coordinates of the fiducial marker and the center coordinates of the coplanar fiducial marker in the image, the relative attitude information of the target satellite is obtained, including:

[0113] Obtaining a relative pitch angle and a relative yaw angle of the target satellite according to the center coordinates of the coplanar reference marks in the image and the center coordinates of the non-coplanar reference marks;

[0114] The relative rolling angle is obtained according to the position of the line connecting the central coordinates of the reference marks relative to the physical coordinate axis of the image.

[0115] In the specific implementation process, the relative pitch angle θ pix and the relative yaw angle ψ pix It can be expressed by the number of pixels that the non-coplanar point deviates from the center of the four coplanar points, as follows:

[0116]

[0117] Relative roll angle Indicates that the center point of the pattern after spot imaging (u a ,v a ) and the center point (u c ,v c ), that is, as shown in the attached Figure 7 The angle between the straight line connecting the center points of regions a and c and the positive direction of the X-axis of the target coordinate system is expressed as follows:

[0118]

[0119] If W x =0,W y >0, then

[0120] If W x =0,W y <0, then

[0121] If W x >0,W y ≥0, then

[0122] If W x >0,W y <0, then

[0123] If W x <0, then

[0124] If W x =0,W y =0, then

[0125] S50: Obtaining relative position and posture information of the target satellite according to the relative position, coordinate conversion relationship and relative posture information of the target satellite.

[0126] In the specific implementation process, by solving the relative position and then combining it with the relative attitude, and adding the coordinate transformation relationship, such as the coordinate transformation relationship between the camera coordinate system and the tracking satellite's center of mass coordinate system, and the coordinate transformation relationship between the target coordinate system and the target satellite's center of mass coordinate system, the specific value of the relative posture between the target satellite and the tracking satellite's center of mass coordinate system can be solved. The relative position can be solved in the following way, namely:

[0127] Before obtaining the relative position and attitude information of the target satellite according to the relative position, the coordinate conversion relationship and the relative attitude information of the target satellite, the method further includes:

[0128] The relative position is obtained according to the first pixel spacing, the second pixel spacing and the focal length of the camera; wherein the first pixel spacing is the pixel distance between two determined imaging points on the target image when the distance between the tracking satellite and the target satellite in the optical axis direction is the first distance, and the second pixel spacing is the pixel distance between the two determined imaging points on the target image when the distance is the second distance.

[0129] In the specific implementation process, the following formula is used for solution:

[0130]

[0131] Wherein, D is the pixel spacing between two determined imaging points on the target pattern in the image pixel coordinate system when the tracking satellite and the target satellite are at a determined distance K in the optical axis direction, i.e., the first pixel spacing; d is the pixel spacing between two determined imaging points on the above target pattern in the image pixel coordinate system when they are at an arbitrary distance d, i.e., the second pixel spacing; f is the focal length of the camera; and R is the actual distance in the optical axis direction from the origin of the camera coordinate system to the origin of the target coordinate system on the target satellite.

[0132] In this embodiment, the target image is first binarized by a grayscale threshold to highlight the area where the reference mark is located. Secondly, a recursive small area elimination technique is used to eliminate abnormal areas in the binary image due to noise, reduce the influence of other non-target patterns, and set the elimination judgment conditions so that the number of retained areas is equal to the number of reference marks of the target. Then, the edge coordinates of the target area representing the reference mark are used to obtain the center of the reference mark itself and the center of the coplanar reference mark in the image, solve the relative attitude of the satellite, and combine the relative position and coordinate conversion relationship to finally obtain the relative attitude of the satellite, thereby improving the satellite's posture measurement accuracy.

[0133] As attached Figure 8 As shown in the attached Figure 8 The present application is further described below in the following embodiments:

[0134] First, the target image is acquired and preprocessed, that is, Gaussian smoothing filter processing, and the image is gray-scale binarized under the condition of a custom threshold, and the pixels are set to 0 or 1 to highlight the area of ​​the target reference mark.

[0135] After the binarization is completed, the abnormal areas are recursively eliminated. The number of areas N is set to 5 according to the number of benchmark marks, which is the judgment condition for elimination. If the number of areas is less than 5, then the position of the benchmark mark is estimated to make up 5 areas. If the number of areas is greater than 5, the similarity is judged and the 5 areas with the highest similarity are retained, so that the final number of areas is maintained at the set 5.

[0136] Then, the separation accuracy of the reference mark area is improved. The ellipse edge coordinate data can be extracted through Canny edge detection to obtain the ellipse circumscribed circle contour, and then the similarity coefficient is calculated through Hu invariant moment to select the extracted contour. The spot centroid can be further extracted through the tangent outer frame focus method, and the three-dimensional image reconstruction can be further performed through the IEPNP algorithm to improve the calculation accuracy, and finally realize the relative attitude and position solution of the satellite.

[0137] Refer to the attached Fig. 9 Based on the same inventive concept as in the above-mentioned embodiment, the embodiment of the present application further provides a posture measurement device based on a satellite target, comprising:

[0138] A binarization module, which is used to binarize the target image of the target satellite based on a grayscale threshold to obtain a binarized image;

[0139] The elimination module is used to eliminate abnormal areas in the binary image by using recursive small area elimination technology to obtain the target area image; wherein the judgment condition of recursive small area elimination is that the number of areas is equal to the number of reference marks;

[0140] A center acquisition module, which is used to obtain the center coordinates of the reference mark and the center coordinates of the coplanar reference mark in the image according to the edge coordinates of the target area in the target area image;

[0141] The attitude acquisition module is used to obtain the relative attitude information of the target satellite according to the center coordinates of the reference mark and the center coordinates of the coplanar reference mark in the image;

[0142] The posture acquisition module is used to obtain the relative posture information of the target satellite based on the relative position, coordinate conversion relationship and relative posture information of the target satellite.

[0143] Those skilled in the art should understand that the division of the various modules in the embodiment is merely a division of logical functions, and can be fully or partially integrated into one or more actual carriers in actual applications, and these modules can all be implemented in the form of software called by a processing unit, or all in the form of hardware, or in the form of a combination of software and hardware. It should be noted that each module in the satellite target-based posture measurement device in this embodiment corresponds one-to-one to each step in the satellite target-based posture measurement method in the aforementioned embodiment. Therefore, the specific implementation method of this embodiment can refer to the implementation method of the aforementioned satellite target-based posture measurement method, which will not be repeated here.

[0144] Based on the same inventive concept as in the aforementioned embodiments, an embodiment of the present application further provides a computer-readable storage medium storing a computer program. When the computer program is loaded and executed by a processor, a satellite target-based posture measurement method as provided in an embodiment of the present application is implemented.

[0145] Based on the same inventive concept as in the above-mentioned embodiment, an embodiment of the present application further provides an electronic device, including a processor and a memory, wherein:

[0146] The memory is used to store computer programs;

[0147] The processor is used to load and execute a computer program so that the electronic device executes a satellite target-based posture measurement method as provided in an embodiment of the present application.

[0148] In some embodiments, the computer readable storage medium may be a memory such as FRAM, ROM, PROM, EPROM, EEPROM, flash memory, magnetic surface memory, optical disk, or CD-ROM; or various devices including one or any combination of the above memories. The computer may be various computing devices including intelligent terminals and servers.

[0149] In some embodiments, executable instructions may be in the form of a program, software, software module, script or code, written in any form of programming language (including compiled or interpreted languages, or declarative or procedural languages), and may be deployed in any form, including as a stand-alone program or as a module, component, subroutine or other unit suitable for use in a computing environment.

[0150] As an example, executable instructions may, but need not, correspond to a file in a file system, may be stored as part of a file that stores other programs or data, such as in one or more scripts in a HyperText Markup Language (HTML) document, in a single file dedicated to the program in question, or in multiple coordinated files (e.g., files storing one or more modules, subroutines, or code portions).

[0151] By way of example, executable instructions may be deployed to be executed on one computing device, or on multiple computing devices located at one site, or on multiple computing devices distributed across multiple sites and interconnected by a communication network.

[0152] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or system including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or system. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or system including the element.

[0153] The serial numbers of the above-mentioned embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.

[0154] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as a read-only memory / random access memory, a magnetic disk, or an optical disk), and includes a number of instructions for enabling a multimedia terminal device (which can be a mobile phone, a computer, a television receiver, or a network device, etc.) to execute the methods described in each embodiment of the present application.

[0155] In summary, the present application provides a method, device, medium and equipment for measuring the position and posture of a satellite target, the method comprising: binarizing the target image of the target satellite based on a grayscale threshold to obtain a binary image; using a recursive small area elimination technique to eliminate abnormal areas in the binary image to obtain a target area image; wherein the judgment condition for the recursive small area elimination is that the number of areas is equal to the number of reference marks; according to the edge coordinates of the target area in the target area image, the center coordinates of the reference mark and the center coordinates of the coplanar reference mark in the image are obtained; according to the center coordinates of the reference mark and the center coordinates of the coplanar reference mark in the image, the relative posture information of the target satellite is obtained; according to the relative position, the coordinate conversion relationship and the relative posture information of the target satellite, the relative position and posture information of the target satellite is obtained. This application first binarizes the target image through a grayscale threshold to highlight the area where the benchmark mark is located. Secondly, it uses recursive small area elimination technology to eliminate abnormal areas caused by noise in the binary image, reduce the influence of other non-target patterns, and set the elimination judgment conditions so that the number of retained areas is equal to the number of benchmark marks of the target. Then, the edge coordinates of the target area representing the benchmark mark are used to obtain the center of the benchmark mark itself and the center of the coplanar benchmark mark in the image, solve the relative attitude of the satellite, and combine the relative position and coordinate conversion relationship to finally obtain the relative attitude of the satellite, thereby improving the satellite's posture measurement accuracy.

[0156] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.

Claims

1. A method for measuring the position and posture of a satellite target, characterized in that: The following steps are involved: Binarizing the target image of the target satellite based on the grayscale threshold to obtain a binary image; Using a recursive small area elimination technique, the abnormal area in the binary image is eliminated to obtain a target area image; wherein the judgment condition for the recursive small area elimination is that the number of areas is equal to the number of reference marks; According to the edge coordinates of the target area in the target area image, obtaining the center coordinates of the reference mark and the center coordinates of the coplanar reference mark in the image; Obtaining relative attitude information of the target satellite according to the central coordinates of the reference mark and the central coordinates of the coplanar reference mark in the image; The relative position and posture information of the target satellite is obtained according to the relative position, the coordinate conversion relationship and the relative posture information of the target satellite.

2. The method for measuring the position and posture of a satellite target according to claim 1, characterized in that: The method further comprises: obtaining the center coordinates of the reference mark and the center coordinates of the coplanar reference mark in the image according to the edge coordinates of the target area in the target area image; Eliminate stray points on the contour of the target area to obtain contour information; Based on the Hu invariant moment principle, the most similar target regions whose number is equal to the number of the reference markers are obtained in the contour information.

3. The method for measuring the position and posture of a satellite target according to claim 1, characterized in that: The step of obtaining the center coordinates of the reference mark and the center coordinates of the coplanar reference mark in the image according to the edge coordinates of the target area in the target area image comprises: Obtaining the center coordinates of the reference mark according to the edge coordinates of the target area in the target area image; The center coordinates of the coplanar reference marks in the image are obtained according to the coordinate average of the center coordinates of the coplanar reference marks.

4. The method for measuring the position and posture of a satellite target according to claim 1, characterized in that: The step of obtaining the relative attitude information of the target satellite according to the central coordinates of the reference mark and the central coordinates of the coplanar reference mark in the image comprises: Obtaining a relative pitch angle and a relative yaw angle of the target satellite according to the central coordinates of the coplanar reference marks in the image and the central coordinates of the non-coplanar reference marks; The relative rolling angle is obtained according to the position of the line connecting the central coordinates of the reference marks relative to the physical coordinate axis of the image.

5. The method for measuring the position and posture of a satellite target according to claim 1, characterized in that: Before obtaining the relative position and posture information of the target satellite according to the relative position, the coordinate conversion relationship and the relative posture information of the target satellite, the method further includes: The relative position is obtained by solving the first pixel spacing, the second pixel spacing and the focal length of the camera; wherein the first pixel spacing is the pixel distance between two determined imaging points on the target image when the distance between the tracking satellite and the target satellite in the optical axis direction is the first distance, and the second pixel spacing is the pixel distance between two determined imaging points on the target image when the distance is the second distance.

6. The method for measuring the position and posture of a satellite target according to claim 1, characterized in that: Before binarizing the target image of the target satellite based on the grayscale threshold and obtaining the binarized image, the method further includes: Performing filtering processing on the target image to obtain a filtered target image; The step of binarizing the target image of the target satellite based on the grayscale threshold to obtain a binarized image includes: The filtered target image of the target satellite is binarized based on the grayscale threshold to obtain a binarized image.

7. The method for measuring the position and posture of a satellite target according to claim 1, characterized in that: The recursive small area elimination technology is a recursive small area elimination technology based on a gradual increase in the number of pixels.

8. A position and posture measurement device based on a satellite target, characterized in that: include: A binarization module, wherein the binarization module is used to binarize the target image of the target satellite based on a grayscale threshold to obtain a binarized image; A rejection module, the rejection module is used to use a recursive small area rejection technique to remove abnormal areas in the binary image to obtain a target area image; wherein the judgment condition for the recursive small area rejection is that the number of areas is equal to the number of reference marks; A center acquisition module, the center acquisition module is used to obtain the center coordinates of the reference mark and the center coordinates of the coplanar reference mark in the image according to the edge coordinates of the target area in the target area image; An attitude acquisition module, the attitude acquisition module is used to obtain the relative attitude information of the target satellite according to the central coordinates of the reference mark and the central coordinates of the coplanar reference mark in the image; The posture acquisition module is used to obtain the relative posture information of the target satellite according to the relative position, coordinate conversion relationship and relative posture information of the target satellite.

9. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is loaded and executed by the processor, the satellite target-based posture measurement method as described in any one of claims 1 to 7 is implemented.

10. An electronic device, characterized in that: comprising a processor and a memory, wherein: The memory is used to store computer programs; The processor is used to load and execute the computer program so that the electronic device performs the satellite target-based posture measurement method as described in any one of claims 1-7.

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