Star map identification method and device, electronic equipment and storage medium

Abstract

This disclosure provides a star map recognition method, apparatus, electronic device, and storage medium. Belonging to the field of image processing technology, the method includes: acquiring an initial star map using an imaging device equipped with initial camera parameters and an initial distortion model; dividing the initial star map into N annular star map regions outward from the center point; identifying stars within each annular star map region to obtain N sets of stellar data, each set including the number and position of stars; correcting the initial camera parameters and initial distortion model based on the N sets of stellar data to obtain target camera parameters and a target distortion model; and processing the initial star map based on the target camera parameters and target distortion model to obtain a target star map. The technical solution provided by this disclosure, through progressive distortion correction, effectively improves the accuracy of star map recognition under complex distortion conditions.

Description

Technical Field

[0001] This disclosure belongs to the field of image processing technology, and in particular relates to a star map recognition method, apparatus, electronic device and storage medium. Background Technology

[0002] In the field of star map recognition, the mainstream algorithms currently include subgraph isomorphism algorithms and pattern recognition algorithms. The triangle algorithm is a representative of subgraph isomorphism algorithms, while the grid algorithm is a representative of pattern recognition algorithms. Although these algorithms have achieved some success in practical applications, they still have some significant drawbacks.

[0003] First, while the triangle algorithm is the most typical and mature star map recognition algorithm, its feature dimension is low, relying mainly on the angular distance information between stars. This dependence makes the algorithm prone to redundant and incorrect matching when there are many stars and triangles or when there is a lot of positional noise, thus significantly reducing the recognition success rate.

[0004] Secondly, the grid algorithm is known for its fast processing speed and high recognition rate. However, when selecting nearest neighbor stars, this algorithm is highly susceptible to position noise and false stars. Position noise may lead to incorrect selection of "calibration stars" or "starting edges" during the construction of grid patterns or circumferential patterns, thus affecting the accuracy of the entire recognition process. Summary of the Invention

[0005] This disclosure provides a solution to address the problem of low accuracy in star map recognition in related technologies.

[0006] In a first aspect, this disclosure provides a star map recognition method, the method comprising: An initial star map is acquired using an imaging device, which has initial camera parameters and an initial distortion model. The initial star map is divided into N annular star map regions from the center point outwards; Stars are identified within each ring-shaped star map region, resulting in N sets of star data, each set including the number of stars and their positions; Based on the N sets of stellar data, the initial camera parameters and the initial distortion model are corrected to obtain the target camera parameters and the target distortion model; Based on the target camera parameters and the target distortion model, the initial star map is processed to obtain the target star map.

[0007] Secondly, this disclosure provides a star map recognition device, the device comprising: An initial star map is acquired using an imaging device, which has initial camera parameters and an initial distortion model. The initial star map is divided into N annular star map regions from the center point outwards; Stars are identified within each ring-shaped star map region, resulting in N sets of star data, each set including the number of stars and their positions; Based on the N sets of stellar data, the initial camera parameters and the initial distortion model are corrected to obtain the target camera parameters and the target distortion model; Based on the target camera parameters and the target distortion model, the initial star map is processed to obtain the target star map.

[0008] Thirdly, this disclosure provides an electronic device, including: Processor; and Memory for storing the executable instructions of the processor; The processor is configured to execute the first aspect or any method in a possible implementation of the first aspect by executing the executable instructions.

[0009] Fourthly, embodiments of this disclosure provide a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements any of the methods in the first aspect or any possible implementations of the first aspect.

[0010] Fifthly, embodiments of this disclosure provide a computer program product including computer instructions that, when executed by a processor, implement any method in the first aspect or any possible implementation of the first aspect.

[0011] The technical solution provided in this disclosure acquires an initial star map using an imaging device equipped with initial camera parameters and an initial distortion model. The initial star map is then divided into N annular star map regions outward from its center point. Stars within each annular star map region are identified, yielding N sets of stellar data, each set including the number and position of stars. Based on the N sets of stellar data, the initial camera parameters and the initial distortion model are corrected to obtain target camera parameters and a target distortion model. Finally, based on the target camera parameters and the target distortion model, the initial star map is processed to obtain a target star map. The technical solutions provided in the various embodiments of this disclosure improve the accuracy of star map recognition under complex distortion conditions by sequentially performing distortion correction based on the N annular star map regions divided outward from the center point of the initial star map. Attached Figure Description

[0012] To more clearly illustrate the technical solutions in the embodiments or related technologies of this disclosure, the accompanying drawings used in the description of the embodiments or related technologies will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of this disclosure. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. In the drawings: Figure 1 This is a schematic flowchart illustrating a star map recognition method according to an embodiment of the present disclosure. Figure 2 This is a flowchart illustrating a distortion model determination method according to an embodiment of the present disclosure. Figure 3 This is a schematic diagram of the structure of a star map recognition device provided in an embodiment of the present disclosure; Figure 4 This is a schematic diagram of the structure of an electronic device provided in one embodiment of the present disclosure. Detailed Implementation

[0013] Embodiments of this disclosure are described in detail below, with examples of embodiments illustrated in the accompanying drawings. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this disclosure, and should not be construed as limiting this disclosure.

[0014] The terms "first" and "second," etc., used in the specification, claims, and drawings of this disclosure are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that embodiments of the present disclosure described herein can be implemented, for example, in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0015] The star map recognition method provided in this disclosure can run on a terminal device or a server. The terminal device can be a local terminal device, including wearable devices such as VR (Virtual Reality), AR (Augmented Reality), and MR (Mixed Reality). The server can be a standalone physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms.

[0016] In the field of star map recognition, the mainstream algorithms currently include subgraph isomorphism algorithms and pattern recognition algorithms. The triangle algorithm is a representative of subgraph isomorphism algorithms, while the grid algorithm is a representative of pattern recognition algorithms. Although these algorithms have achieved some success in practical applications, they still have some significant drawbacks.

[0017] First, while the triangle algorithm is the most typical and mature star map recognition algorithm, its feature dimension is low, relying mainly on the angular distance information between stars. This dependence makes the algorithm prone to redundant and incorrect matching when there are many stars and triangles or when there is a lot of positional noise, thus significantly reducing the recognition success rate.

[0018] Secondly, the grid algorithm is known for its fast processing speed and high recognition rate. However, when selecting nearest neighbor stars, this algorithm is highly susceptible to position noise and false stars. Position noise may lead to incorrect selection of "calibration stars" or "starting edges" during the construction of grid patterns or circumferential patterns, thus affecting the accuracy of the entire recognition process.

[0019] Figure 1 This is a schematic flowchart of a star map recognition method provided in an exemplary embodiment of the present disclosure. The method includes at least the following steps S101-S105: S101 acquires an initial star map using imaging equipment.

[0020] In some embodiments, the imaging device has initial camera parameters and an initial distortion model.

[0021] The imaging equipment mentioned here mainly refers to a spherical camera. The main function of this spherical camera is to capture star maps, and these star maps are large-scale star maps with wide coverage and a large number of stars.

[0022] S102, the initial star map is divided into N annular star map regions from the center point outwards.

[0023] In some embodiments, to improve the accuracy of subsequent processing, square star maps, elliptical star maps, etc., may also be used when dividing the star map regions. No restrictions are imposed here.

[0024] S103 identifies stars within each ring-shaped star map region, obtaining N sets of star data, each set including the number of stars and their positions.

[0025] In this embodiment, star identification primarily employs the brightness sequence ring diagram identification method. Specific identification methods can be adopted according to actual circumstances, and will not be limited or elaborated upon here.

[0026] S104, Based on the N sets of stellar data, the initial camera parameters and the initial distortion model are corrected to obtain the target camera parameters and the target distortion model.

[0027] In some embodiments, the distortion model includes optical distortion type and distortion order.

[0028] The different distortion models can refer to different types of optical distortion included in the lens optical distortion model; or, the same type of optical distortion included in the distortion model but different distortion orders.

[0029] In some embodiments, the optical distortion type refers to radial distortion model and tangential distortion model.

[0030] In this embodiment of the disclosure, high-precision identification and parameter calibration of star images acquired by a spherical camera are achieved based on stellar data, thus solving the interference problem of distorted star images from spherical cameras.

[0031] In some embodiments, based on the N sets of stellar data, the initial camera parameters and the initial distortion model are corrected to obtain the target camera parameters and the target distortion model, such as... Figure 2 As shown, steps S11-S13 are included: S11, based on N sets of stellar data, determines the parameters of the target camera.

[0032] In some embodiments, the target camera parameters include at least focal length and distortion coefficient.

[0033] S12, determine the target distortion order based on N sets of stellar data and the target camera parameters.

[0034] S13, Based on the distortion order, the distortion coefficients, and the initial distortion model, determine the target distortion model.

[0035] In this embodiment, when setting target camera parameters, different camera parameters can be obtained for star data in different annular star map regions. This allows for iterative correction from the inner star map region to the outer star map region, resulting in more accurate camera parameters.

[0036] In some embodiments, the method further includes: determining a first theoretical formula for the angle formed by the direction vectors of any two stars in the initial star map in the image coordinate system and the angle formed by the direction vectors of two stars that match the two stars in the celestial coordinate system.

[0037] Specifically, the first theoretical formula is:

[0038] Among them, the and Let be the direction vectors of the two stars that correspond to the two stars in the celestial coordinate system. and Let be the direction vectors of any two stars in the initial star map in the image coordinate system. The angle formed by the direction vectors of any two stars in the initial star map in the image coordinate system is the angle between them. It is the angle formed by the direction vectors of the two stars that match the two stars in the celestial coordinate system.

[0039] Furthermore, the matching refers to the fact that the star in the star chart and the star in the celestial coordinate system are the same star.

[0040] In this embodiment of the disclosure, the initial parameter vector of the spherical camera is determined.

[0041] The parameter vector can be represented as:

[0042] The The main point, the For focal length, the The initial distortion coefficients.

[0043] Specifically, the distortion coefficient can be a radial distortion coefficient or / and a tangential distortion coefficient.

[0044] In some embodiments, if the distortion model in the spherical camera is a radial distortion model, then the distortion coefficients contained in the initial parameter vector are radial distortion coefficients; if the distortion model in the spherical camera is a tangential distortion model, then the distortion coefficients contained in the initial parameter vector are tangential distortion coefficients.

[0045] In some embodiments, for step S12 above, determining the target camera parameters based on N sets of stellar data includes: Step 11: For the k-th annular star map region among the N annular star map regions, determine the k-th estimated parameter vector of the spherical camera, wherein the k-th estimated parameter vector is:

[0046] Step 12: Based on the (k-1)th estimated parameter vector and the kth estimated parameter vector, determine the kth error vector, wherein the kth error vector is: .

[0047] Wherein, k is a positive integer. When k=1, the (k-1)th estimated parameter vector is the initial parameter vector.

[0048] Step 13: Determine the k-th relationship formula between the k-th estimated parameter vector and the direction vectors of any two stars in the k-th ring star map region. The k-th relationship formula is: .

[0049] Step 14: Based on the k-th relation formula and the k-th error vector, linearize the first theoretical formula to obtain the second relation formula, which is: .

[0050] Step 15: Define a residual formula, wherein the residual formula is: ; Among them, the The deviation value indicates the difference between the direction vector of a star in the celestial coordinate system and the direction vector of a star that matches it in the image coordinate system. Specifically, the deviation value is the difference between the cosine of the direction vectors of the two stars that match the star in the celestial coordinate system and the cosine of the direction vectors of the corresponding two stars in the initial star map in the image coordinate system.

[0051] Step 16: Based on the number of stars n in the k-th ring star map region and the second relationship formula, process the residual formula, and express the processing result in matrix form as the k-th matrix formula, which is: ; in, ; A ; Where i = 1…n-1, j = i+1…n, R is the residual vector, and A is the Jacobian matrix, whose elements are derived from… The partial derivatives with respect to each parameter constitute the partial derivatives.

[0052] In this embodiment of the disclosure, the process of processing the residual formula and expressing the processing result in matrix form as the k-th matrix formula refers to writing out specific residual formulas for n stars using the second relation formula based on the basic form of the residual formula, and forming the corresponding matrix.

[0053] Step 17: Process the formula of the k-th matrix using the least squares method to obtain the target error vector: ; Where P is the weight set for each measurement value in each iteration, and the measurement value refers to the stellar data corresponding to each star in the k-th ring star map region. .

[0054] Step 18: Based on the target error vector and the k-th estimation parameter vector, obtain the (k+1)-th estimation parameter vector, where the (k+1)-th estimation parameter vector is: .

[0055] Step 19: Based on the (k+1)th estimated parameter vector obtained in step 18, repeat steps 11-18 using an iterative method until k=N, thereby obtaining the target estimated parameter vector, and determining the target camera parameters based on the target estimated parameter vector.

[0056] In some embodiments, the weight determination process for each measurement value in each iteration for step 17 above is as follows: Step 21: Determine the theoretical formula for the weight matrix, which is: ; Wherein, P is the weight matrix for each measurement in each iteration, Q is the inverse of the weight matrix, and E is the identity matrix.

[0057] Step 22: Based on the k-th relational formula, determine the estimated value of the unit weight variance:

[0058] Wherein, L is the number of measurements used in each iteration for least squares estimation.

[0059] Step 23: Determine the variance estimate for each measurement:

[0060] in, The Weight coefficient matrix The diagonal element, the These are the diagonal elements of the weight coefficient matrix P.

[0061] Step 24: Based on the variance estimate, the unit weight variance estimate, and the statistic, determine the weight of each measurement in each iteration;

[0062] Among them, the F is the weight of the i-th measurement within the k-th annular star map region, where i=n, ​​and F is a preset threshold.

[0063] In this embodiment of the disclosure, the weight of the pointer can be gradually reduced with each iteration, even approaching zero, which can eliminate the negative impact of star pair errors caused by misidentification.

[0064] In some embodiments, the statistical determination process for step 24 described above is as follows: Step 31: Based on the variance estimate and the unit weight variance estimate, determine the normality formula: .

[0065] Step 32: Based on the normality formula, determine the statistic: .

[0066] In some embodiments, for step 22 above, L is the number of measurements used in each iteration using least squares estimation, and the process for determining the number of measurements is as follows: Based on the number of rows and columns of the Jacobian matrix in the formula for the k-th matrix, determine the number of measurements to be estimated using least squares in each iteration:

[0067] Among them, the The total number of measurements or the number of rows, the The number of basic measurements or the number of columns.

[0068] In some embodiments, for step 22 described above, the U and The determination process is as follows: Step 41: Based on the target error vector and the weight calculation formula, determine U, the calculation formula is as follows:

[0069]

[0070] in, These are elements of U, representing the residuals from each measurement; Step 42: Determine the weight coefficient matrix of U, calculated using the following formula: .

[0071] In some embodiments, for step S12 above, determining the target distortion order based on N sets of stellar data and the target camera parameters includes: Step 51: For the k-th ring-shaped star map region among the N ring-shaped star map regions, determine the target step size for the k-th ring-shaped star map region. ;

[0072] Among them, the As the reference step size, the To control the attenuation factor of the residual's sensitivity to step size, the The residual threshold is... The average projection residual of all matching stars within the k-th ring star map region, the The calculation formula is as follows:

[0073] Among them, the Let the coordinates of the observed pixels be within the k-th ring-shaped star map region. The projection points of the matching stars; Step 52: Based on the target step size and order formula, determine the kth distortion order of the kth ring star map region; G

[0074] Wherein, G is the k-th distortion order, and the For the (k-1)th distortion order, the This is the maximum order value.

[0075] In some embodiments, the order is a positive integer, such as 2nd order, 4th order, etc.

[0076] Step 53: Based on the kth distortion order obtained in Step 2, repeat Step 1-2 using the iterative method until k=N, thereby obtaining the target distortion order.

[0077] In this embodiment, a residual-driven exponential expansion strategy is employed. By quantifying the projection error level of the current region, the expansion process is controlled. The step size of the region expansion is determined after each round of model parameter calculation. Furthermore, to achieve high efficiency and robustness in distortion correction, polynomial-order expansion is dynamically combined with the region expansion step size, forming a collaborative optimization framework. This mechanism maximizes processing efficiency while ensuring accuracy through the coordinated adjustment of model complexity and spatial expansion.

[0078] In this embodiment of the disclosure, each iteration is for a different annular star map region. Based on the initial distortion order, the order can be used to characterize more complex distortion morphologies, enabling the distortion model to capture the nonlinear distortion features of the edge field of view when processing the star map.

[0079] In some embodiments, to better understand the technical content of this solution, an example is given below, with specific steps as follows: Step 61: Obtain an initial star map using an imaging device, and divide the initial star map into N ring-shaped star map regions from the center point outwards.

[0080] Step 62: Obtain the number of stars and the position of each star in each ring star map region.

[0081] Step 63: Based on the number of stars and the position of each star in the first ring star map region, determine the camera parameters corresponding to the first ring star map region according to steps 11-18 (the camera parameters refer to the second estimated parameter vector in steps 11-18, which refers to the parameter vector corresponding to the first ring star map region and is the parameter vector used for calculation of the second ring star map region).

[0082] Step 64: While determining the camera parameters corresponding to the first ring star map region according to Step 63, based on the number of stars and the position of each star in the first ring star map region, determine the distortion order corresponding to the first ring star map region according to Steps 51-52.

[0083] Step 65: Based on the distortion order and camera parameters obtained in Steps 63-64 for the first ring star map region, start calculating the distortion order and camera parameters for the second ring star map region, and so on until the distortion order and camera parameters for the Nth ring star map region are calculated.

[0084] In this embodiment, the progressive correction strategy significantly improves the accuracy and robustness of distortion correction, avoiding the recognition failure problem caused by inaccurate distortion models in global recognition, which is common in traditional methods. Furthermore, this method can adapt to regions with varying degrees of distortion, providing reliable assurance for full-field star map recognition.

[0085] S105, Based on the target camera parameters and the target distortion model, the initial star map is processed to obtain the target star map.

[0086] Specifically, after the target camera parameters and the target distortion model of the spherical camera and other equipment were determined, in subsequent use, after the spherical camera takes a star map, star map identification can be better performed based on the target camera parameters and the target distortion model, and reliable data support can be provided for subsequent missions. This effectively solves the problem of difficult star map identification caused by inaccurate distortion models and position noise of curved cameras in the early stage of deep space exploration.

[0087] The technical solution provided in this disclosure acquires an initial star map using an imaging device equipped with initial camera parameters and an initial distortion model. The initial star map is then divided into N annular star map regions outward from its center point. Stars within each annular star map region are identified, yielding N sets of stellar data, each set including the number and position of stars. Based on the N sets of stellar data, the initial camera parameters and the initial distortion model are corrected to obtain target camera parameters and a target distortion model. Finally, based on the target camera parameters and the target distortion model, the initial star map is processed to obtain a target star map. The technical solutions provided in the various embodiments of this disclosure improve the accuracy of star map recognition under complex distortion conditions by sequentially performing distortion correction based on the N annular star map regions divided outward from the center point of the initial star map.

[0088] Figure 3 A schematic diagram of the structure of a star map recognition device provided for an exemplary embodiment of this disclosure; The device includes: an acquisition unit 201, a division unit 202, an identification unit 203, a correction unit 204, and a determination unit 205; The acquisition unit 201 is used to acquire an initial star map through an imaging device, wherein the imaging device has initial camera parameters and an initial distortion model; The dividing unit 202 is used to divide the initial star map into N annular star map regions from the center point outwards. The identification unit 203 is used to identify stars in each ring star map region and obtain N sets of star data, each set of star data including the number of stars and the position of the stars; The correction unit 204 is used to correct the initial camera parameters and the initial distortion model based on the N sets of star data to obtain the target camera parameters and the target distortion model; The determining unit 205 is used to process the initial star map based on the target camera parameters and the target distortion model to obtain the target star map.

[0089] In some embodiments, the device is used to correct the initial camera parameters and the initial distortion model based on the N sets of stellar data to obtain target camera parameters and a target distortion model. Specifically, the device is used to: Based on N sets of stellar data, the parameters of the target camera are determined, including at least the focal length and distortion coefficient. The target distortion order is determined based on N sets of stellar data and the target camera parameters; The target distortion model is determined based on the distortion order, the distortion coefficients, and the initial distortion model.

[0090] In some embodiments, the device is further configured to: A first theoretical formula is used to determine the angle between the direction vectors of any two stars in the initial star map in the image coordinate system and the angle between the direction vectors of two stars that match the two stars in the celestial coordinate system, as well as the initial parameter vector of the spherical camera. The first theoretical formula is:

[0091] Among them, the and Let be the direction vectors of the two stars that correspond to the two stars in the celestial coordinate system. and Let be the direction vectors of any two stars in the initial star map in the image coordinate system. The angle formed by the direction vectors of any two stars in the initial star map in the image coordinate system is the angle between them. M is the angle formed by the direction vectors of the two stars that match the two stars in the celestial coordinate system, and M is the orthogonal moment; The initial parameter vector is:

[0092] Among them, the The main point, the For focal length, the The initial distortion coefficients; The device is used to determine target camera parameters based on N sets of stellar data. Specifically, the device is used for: Step 11: For the k-th annular star map region among the N annular star map regions, determine the k-th estimated parameter vector of the spherical camera, wherein the k-th estimated parameter vector is: ; Step 12: Based on the (k-1)th estimated parameter vector and the kth estimated parameter vector, determine the kth error vector, wherein the kth error vector is: ; Wherein, k is a positive integer, and when k=1, the (k-1)th estimated parameter vector is the initial parameter vector; Step 3: Determine the k-th relationship formula between the k-th estimated parameter vector and the direction vectors of any two stars in the k-th ring star map region. The k-th relationship formula is: ; Step 14: Based on the k-th relation formula and the k-th error vector, linearize the first theoretical formula to obtain the second relation formula, which is: ; Step 5: Define a residual formula, which is: ; Among them, the The deviation value indicates the difference between the direction vector of a star in the celestial coordinate system and the star's direction vector matched in the image coordinate system. Step 16: Based on the number of stars n in the k-th ring star map region and the second relationship formula, process the residual formula, and express the processing result in matrix form as the k-th matrix formula, which is: ; in, ; A ; Where i = 1…n-1, j = i+1…n, R is the residual vector, and A is the Jacobian matrix, whose elements are derived from… The partial derivatives with respect to each parameter constitute the partial derivatives. Step 17: Process the formula of the k-th matrix using the least squares method to obtain the target error vector, which is:

[0093] Wherein, P is the weight set of each measurement value in each iteration, and the measurement value refers to the stellar data corresponding to each star in the k-th ring star map region. ; Step 18: Based on the target error vector and the k-th estimation parameter vector, obtain the (k+1)-th estimation parameter vector, where the (k+1)-th estimation parameter vector is: ; Step 19: Based on the (k+1)th estimated parameter vector obtained in step 18, repeat steps 11-18 using an iterative method until k=N, thereby obtaining the target estimated parameter vector, and determining the target camera parameters based on the target estimated parameter vector.

[0094] In some embodiments, the device in step 17 is specifically used for: Step 21: Determine the theoretical formula for the weight matrix, wherein the theoretical formula for the weight matrix is: ; Wherein, P is the weight matrix for each measurement in each iteration, Q is the inverse of the weight matrix, and E is the identity matrix; Step 22: Based on the k-th relational formula, determine the estimated value of the unit weight variance, which is:

[0095] Wherein, L is the number of measurements estimated using least squares in each iteration, and U is the residual vector; Step 23: Determine the variance estimate for each measurement, wherein the variance estimate is:

[0096] in, The Weight coefficient matrix The diagonal element, the The diagonal elements of the weight coefficient matrix P, the Let be an element of U, representing the residual of each measurement; Step 24: Based on the variance estimate, the unit weight variance estimate, and the statistic, determine the weight of each measurement in each iteration. The weight of each measurement is as follows:

[0097] Among them, the F is the weight of the i-th measurement within the k-th annular star map region, where i=n, ​​and F is a preset threshold.

[0098] In some embodiments, the device in step 24 is specifically used for: Step 31: Based on the variance estimate and the unit weight variance estimate, determine the normality formula: ; Step 32: Based on the normality formula, determine the statistic: .

[0099] In some embodiments, the device in step 22 is specifically used for: Based on the number of rows and columns of the Jacobian matrix in the formula for the k-th matrix, determine the number of measurements to be estimated using least squares in each iteration:

[0100] Among them, the The total number of measurements or the number of rows, the The number of basic measurements or the number of columns.

[0101] In some embodiments, the U and in step 22 The determination process is as follows: Step 41: Based on the target error vector and the weight calculation formula, determine U, the calculation formula is as follows: ; ; in, These are elements of U, representing the residuals from each measurement; Step 42: Determine the weight coefficient matrix of U, calculated using the following formula: .

[0102] In some embodiments, the device is used to determine the target distortion order based on N sets of stellar data and the target camera parameters, and the device is specifically used for: Step 51: For the k-th ring-shaped star map region among the N ring-shaped star map regions, determine the target step size for the k-th ring-shaped star map region. ;

[0103] Among them, the As the reference step size, the To control the attenuation factor of the residual's sensitivity to step size, the The residual threshold is... The average projection residual of all matching stars within the k-th ring star map region, the The calculation formula is as follows:

[0104] Among them, the Let the coordinates of the observed pixels be within the k-th ring-shaped star map region. The projection points of the matching stars; Step 52: Based on the target step size and order formula, determine the kth distortion order of the kth ring star map region; G

[0105] Wherein, G is the k-th distortion order, and the For the (k-1)th distortion order, the The maximum order value; Step 53: Based on the kth distortion order obtained in Step 2, repeat Step 1-2 using the iterative method until k=N, thereby obtaining the target distortion order.

[0106] The technical solution provided in this disclosure acquires an initial star map using an imaging device equipped with initial camera parameters and an initial distortion model. The initial star map is then divided into N annular star map regions outward from its center point. Stars within each annular star map region are identified, yielding N sets of stellar data, each set including the number and position of stars. Based on the N sets of stellar data, the initial camera parameters and the initial distortion model are corrected to obtain target camera parameters and a target distortion model. Finally, based on the target camera parameters and the target distortion model, the initial star map is processed to obtain a target star map. The technical solutions provided in the various embodiments of this disclosure improve the accuracy of star map recognition under complex distortion conditions by sequentially performing distortion correction based on the N annular star map regions divided outward from the center point of the initial star map.

[0107] It should be understood that the device embodiments and method embodiments can correspond to each other, and similar descriptions can be referred to the method embodiments. To avoid repetition, they will not be repeated here. Specifically, the device can execute the above method embodiments, and the foregoing and other operations and / or functions of each module in the device correspond to the corresponding processes in the various methods in the above method embodiments, which will not be repeated here for the sake of brevity.

[0108] The apparatus of this disclosure embodiment has been described above from the perspective of functional modules in conjunction with the accompanying drawings. It should be understood that this functional module can be implemented in hardware, in software instructions, or in a combination of hardware and software modules. Specifically, the steps of the method embodiments in this disclosure can be completed by integrated logic circuits in the processor's hardware and / or by software instructions. The steps of the method disclosed in this disclosure embodiment can be directly embodied as being executed by a hardware decoding processor, or by a combination of hardware and software modules in the decoding processor. Optionally, the software module can reside in a mature storage medium in the art, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, etc. This storage medium is located in memory, and the processor reads information from the memory and, in conjunction with its hardware, completes the steps in the above method embodiments.

[0109] Figure 4 This is a schematic block diagram of an electronic device provided in an embodiment of this disclosure. The electronic device may include: The system includes a memory 301 for storing computer programs and a processor 302 for transferring program code to the processor 302. In other words, the processor 302 can retrieve and run the computer program from the memory 301 to implement the methods described in this embodiment.

[0110] For example, the processor 302 can be used to execute the above-described method embodiments according to instructions in the computer program.

[0111] In some embodiments of this disclosure, the processor 302 may include, but is not limited to: General-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.

[0112] In some embodiments of this disclosure, the memory 301 includes, but is not limited to: Volatile memory and / or non-volatile memory. Non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. Volatile memory can be random access memory (RAM), used as an external cache. By way of example, but not limitation, many forms of RAM are available, such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDR SDRAM), Enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), and Direct Rambus RAM (DR RAM).

[0113] In some embodiments of this disclosure, the computer program may be divided into one or more modules, which are stored in the memory 301 and executed by the processor 302 to perform the method provided in this disclosure. The one or more modules may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program in the electronic device.

[0114] like Figure 4 As shown, the electronic device may also include: Transceiver 303, which can be connected to processor 302 or memory 301.

[0115] The processor 302 can control the transceiver 303 to communicate with other devices; specifically, it can send information or data to other devices or receive information or data sent by other devices. The transceiver 303 may include a transmitter and a receiver. The transceiver 303 may further include antennas, and the number of antennas may be one or more.

[0116] It should be understood that the various components in the electronic device are connected through a bus system, which includes a data bus, a power bus, a control bus, and a status signal bus.

[0117] This disclosure also provides a computer storage medium storing a computer program thereon, which, when executed by a computer, enables the computer to perform the methods of the above-described method embodiments. Alternatively, embodiments of this disclosure also provide a computer program product containing instructions that, when executed by a computer, cause the computer to perform the methods of the above-described method embodiments.

[0118] When implemented using software, it can be implemented entirely or partially as a computer program product. This computer program product includes one or more computer instructions. When these computer program instructions are loaded and executed on a computer, all or part of the flow or function according to embodiments of this disclosure is generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium accessible to a computer or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., digital video disc (DVD)), or a semiconductor medium (e.g., solid-state disk (SSD)).

[0119] Those skilled in the art will recognize that the modules and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this disclosure.

[0120] In the embodiments provided in this disclosure, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or modules may be electrical, mechanical, or other forms.

[0121] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical modules; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. For example, the functional modules in the various embodiments of this disclosure may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0122] The above are merely specific embodiments of this disclosure, but the scope of protection of this disclosure is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this disclosure should be included within the scope of protection of this disclosure. Therefore, the scope of protection of this disclosure should be determined by the scope of the claims.

Claims

1. A star map recognition method, characterized in that, The method includes: An initial star map is acquired using an imaging device, which has initial camera parameters and an initial distortion model. The initial star map is divided into N annular star map regions from the center point outwards; Stars are identified within each ring-shaped star map region, resulting in N sets of star data, each set including the number of stars and their positions; Based on the N sets of stellar data, the initial camera parameters and the initial distortion model are corrected to obtain the target camera parameters and the target distortion model, specifically including: (1) Starting from the innermost first ring star map region, the first set of estimated parameter vectors is obtained by iterative optimization using the initial camera parameters and the star data of the first ring star map region. (2) For the kth ring star map region (k≥2), the estimated parameter vector obtained from the (k-1)th ring star map region is used as the initial parameter. Combined with the star data of the kth ring star map region, the kth set of estimated parameter vectors is obtained by least squares iterative optimization. (3) Continue in this manner until the correction of the Nth ring star map region is completed, and the target camera parameters are obtained; (4) During the ring-by-ring correction process, the order of the distortion model is dynamically adjusted according to the average projection residual of stars in the current ring star map region, and the target distortion order is finally obtained. The target distortion model is determined by combining the distortion coefficients of the target camera parameters. Based on the target camera parameters and the target distortion model, the initial star map is processed to obtain the target star map.

2. The method according to claim 1, characterized in that, Based on the N sets of stellar data, the initial camera parameters and the initial distortion model are corrected to obtain the target camera parameters and the target distortion model, including: Based on N sets of stellar data, the parameters of the target camera are determined, including at least the focal length and distortion coefficient. The target distortion order is determined based on N sets of stellar data and the target camera parameters; The target distortion model is determined based on the distortion order, the distortion coefficients, and the initial distortion model.

3. The method according to claim 2, characterized in that, The method further includes: A first theoretical formula is used to determine the angle between the direction vectors of any two stars in the initial star map in the image coordinate system and the angle between the direction vectors of two stars that match the two stars in the celestial coordinate system, as well as the initial parameter vector of the spherical camera. The first theoretical formula is: Among them, the and Let be the direction vectors of the two stars that correspond to the two stars in the celestial coordinate system. and Let be the direction vectors of any two stars in the initial star map in the image coordinate system. The angle formed by the direction vectors of any two stars in the initial star map in the image coordinate system is the angle between them. M is the angle formed by the direction vectors of the two stars that match the two stars in the celestial coordinate system, and M is an orthogonal matrix; The initial parameter vector is: Among them, the The main point, the For focal length, the ( The initial distortion coefficients; The determination of target camera parameters based on N sets of stellar data includes: Step 11: For the k-th annular star map region among the N annular star map regions, determine the k-th estimated parameter vector of the spherical camera, wherein the k-th estimated parameter vector is: ； Step 12: Based on the (k-1)th estimated parameter vector and the kth estimated parameter vector, determine the kth error vector, wherein the kth error vector is: ; Wherein, k is a positive integer, and when k=1, the (k-1)th estimated parameter vector is the initial parameter vector; Step 13: Determine the k-th relationship formula between the k-th estimated parameter vector and the direction vectors of any two stars in the k-th ring star map region. The k-th relationship formula is: ； Step 14: Based on the k-th relation formula and the k-th error vector, linearize the first theoretical formula to obtain the second relation formula, which is: ； Step 15: Define a residual formula, wherein the residual formula is: ； Among them, the The deviation value indicates the difference between the direction vector of a star in the celestial coordinate system and the star's direction vector matched in the image coordinate system. Step 16: Based on the number of stars n in the k-th ring star map region and the second relationship formula, process the residual formula, and express the processing result in matrix form as the k-th matrix formula, which is: ； in, ; A ； Where i = 1…n-1, j = i+1…n, R is the residual vector, and A is the Jacobian matrix, whose elements are derived from… The partial derivatives with respect to each parameter constitute the partial derivatives. Step 17: Process the formula of the k-th matrix using the least squares method to obtain the target error vector, which is: Wherein, P is the weight set of each measurement value in each iteration, and the measurement value refers to the stellar data corresponding to each star in the k-th ring star map region. ; Step 18: Based on the target error vector and the k-th estimation parameter vector, obtain the (k+1)-th estimation parameter vector, where the (k+1)-th estimation parameter vector is: ； Step 19: Based on the (k+1)th estimated parameter vector obtained in step 18, repeat steps 11-18 using an iterative method until k=N, thereby obtaining the target estimated parameter vector, and determining the target camera parameters based on the target estimated parameter vector.

4. The method according to claim 3, characterized in that, The process for determining the weight of each measurement value in each iteration of step 17 is as follows: Step 21: Determine the theoretical formula for the weight matrix, wherein the theoretical formula for the weight matrix is: ； Wherein, P is the weight matrix for each measurement in each iteration, Q is the inverse of the weight matrix, and E is the identity matrix; Step 22: Based on the k-th relational formula, determine the estimated value of the unit weight variance, which is: Wherein, L is the number of measurements estimated using least squares in each iteration, and U is the residual vector; Step 23: Determine the variance estimate for each measurement, wherein the variance estimate is: in, The Weight coefficient matrix The diagonal element, the The diagonal elements of the weight coefficient matrix P, the Let be an element of U, representing the residual of each measurement; Step 24: Based on the variance estimate, the unit weight variance estimate, and the statistic, determine the weight of each measurement in each iteration. The weight of each measurement is as follows: Among them, the F is the weight of the i-th measurement within the k-th annular star map region, where i=n, ​​and F is a preset threshold.

5. The method according to claim 4, characterized in that, The process for determining the statistic in step 24 is as follows: Step 31: Based on the variance estimate and the unit weight variance estimate, determine the normality formula: ； Step 32: Based on the normality formula, determine the statistic: 。 6. The method according to claim 4, characterized in that, The process of determining the number of measurements in step 22 is as follows: Based on the number of rows and columns of the Jacobian matrix in the formula for the k-th matrix, determine the number of measurements to be estimated using least squares in each iteration: Among them, the The total number of measurements or the number of rows, the The number of basic measurements or the number of columns.

7. The method according to claim 4, characterized in that, U and in step 22 The determination process is as follows: Step 41: Based on the target error vector and the weight calculation formula, determine U, the calculation formula is as follows: ； ； in, These are elements of U, representing the residuals from each measurement; Step 42: Determine the weight coefficient matrix of U, calculated using the following formula: 。 8. The method according to claim 2, characterized in that, Determining the target distortion order based on N sets of stellar data and the target camera parameters includes: Step 51: For the k-th ring-shaped star map region among the N ring-shaped star map regions, determine the target step size for the k-th ring-shaped star map region. ; Among them, the As the reference step size, the To control the attenuation factor of the residual's sensitivity to step size, the The residual threshold is... The average projection residual of all matching stars within the k-th ring star map region, the The calculation formula is as follows: Among them, the Let the coordinates of the observed pixels be within the k-th ring-shaped star map region. The projection points of the matching stars; Step 52: Based on the target step size and order formula, determine the kth distortion order of the kth ring star map region; G Wherein, G is the k-th distortion order, and the For the (k-1)th distortion order, the The maximum order value; Step 53: Based on the kth distortion order obtained in Step 2, repeat Step 1-2 using the iterative method until k=N, thereby obtaining the target distortion order.

9. A star map recognition device, characterized in that, The apparatus is used to perform the method according to any one of claims 1-8, comprising: An acquisition unit is used to acquire an initial star map through an imaging device, wherein the imaging device has initial camera parameters and an initial distortion model; A dividing unit is used to divide the initial star map into N annular star map regions from the center point outwards. The identification unit is used to identify stars in each ring star map region and obtain N sets of star data, each set of star data including the number of stars and their positions; The correction unit is used to correct the initial camera parameters and the initial distortion model based on the N sets of star data to obtain the target camera parameters and the target distortion model. The determining unit is used to process the initial star map based on the target camera parameters and the target distortion model to obtain the target star map.

10. An electronic device, characterized in that, include: processor; as well as Memory for storing the executable instructions of the processor; The processor is configured to execute the method of any one of claims 1-8 by executing the executable instructions.

11. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method described in any one of claims 1-8.

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