A method and device for acquiring complete sequence images of a target spacecraft

Abstract

The application discloses a complete sequence image acquisition method and device of a target spacecraft. The method comprises the following steps: acquiring the space characteristics of the target spacecraft and the imaging characteristics of an observation camera, and performing parameterization processing on the space characteristics and the imaging characteristics respectively to obtain a target parameterization model and a camera parameterization model; optimizing the above models based on a unit area pixel value to determine a viewpoint planning optimization target; combining the space light environment parameters of an on-orbit spacecraft, camera imaging constraints and safety constraints to construct a target constraint; constructing an optimization model based on the model, the optimization target and the constraint, and solving the optimization model to obtain a best viewpoint pose of the camera; adjusting the camera trajectory and the attitude to collect images, and updating the target parameterization model according to the images; iteratively executing the above steps to integrate multiple images to obtain a complete sequence image. The application improves the sequence image quality, provides high-quality data support for three-dimensional reconstruction, adapts to complex space environments, and meets the task requirements of space monitoring, resource detection and the like.

Description

Technical Field

[0001] This application relates to the field of image acquisition technology, and in particular to a method and apparatus for acquiring complete sequence images of a target spacecraft. Background Technology

[0002] With the continuous advancement of space missions such as deep space exploration, on-orbit servicing, and space situational awareness, the demand for complete sequential images of target spacecraft is becoming increasingly urgent. These sequential images are the core data support for realizing three-dimensional reconstruction of spacecraft, fault diagnosis, and orbital attitude analysis, directly affecting the accuracy and reliability of mission decisions. However, the space environment is extremely complex, with numerous challenges such as solar interference, dynamic orbital changes, and radiation disturbances, making it difficult for observation cameras to stably acquire high-quality images, which greatly hinders the integrity and accuracy of sequential images.

[0003] Current spacecraft image acquisition methods mostly rely on fixed viewpoint pose planning or single-parameter modeling. However, the viewpoint planning of these methods lacks optimization criteria based on core imaging quality indicators, resulting in problems such as missing details, insufficient clarity, and poor sequence coherence in the acquired images, making it difficult to meet the needs of high-precision 3D reconstruction and on-orbit monitoring.

[0004] Therefore, how to quickly acquire high-quality complete sequence images of target spacecraft through accurate modeling, optimized viewpoint planning, and dynamic adaptation of constraints is a technical problem that urgently needs to be solved. Summary of the Invention

[0005] In view of this, the method and apparatus for acquiring complete sequence images of a target spacecraft provided in this application can improve the quality of the sequence images, provide high-quality data support for 3D reconstruction, adapt to complex space environments, and meet the needs of missions such as space monitoring and resource exploration. The method and apparatus for acquiring complete sequence images of a target spacecraft provided in this application are implemented as follows:

[0006] This application provides a method for acquiring complete sequence images of a target spacecraft, including:

[0007] Step 101: Obtain the space characteristics of the target spacecraft and the imaging characteristics of the observation camera; perform parameterization processing on the space characteristics to obtain the target parameterization model; and perform parameterization processing on the imaging characteristics to obtain the camera parameterization model.

[0008] Step 102: Optimize the target parameterization model and the camera parameterization model based on the pixel value per unit area to obtain the viewpoint planning optimization target;

[0009] Step 103: Obtain the first constraint parameters, and construct target constraints based on the first constraint parameters and the viewpoint planning optimization objective;

[0010] Step 104: Construct an optimization model based on the target parameterization model, the camera parameterization model, the viewpoint planning optimization objective, and the target constraints;

[0011] Step 105: Perform pose acquisition processing on the optimized mathematical model to obtain the optimal viewpoint pose of the observation camera;

[0012] Step 106: After adjusting the movement trajectory and attitude of the observation camera based on the optimal viewpoint pose, an image of the target spacecraft is acquired to obtain the first image;

[0013] Step 107: Update the target parameterization model based on the first image to obtain the processed target parameterization model;

[0014] Step 108: Iteratively execute steps 102 to 107 to obtain multiple first images. Integrate the multiple first images to obtain a complete sequence of images of the target spacecraft.

[0015] In some embodiments, the step of performing pose acquisition processing on the optimized mathematical model to obtain the optimal viewpoint pose of the observation camera includes:

[0016] The initial pose of the observation camera and the set of patches in the target parameterization model are initialized to obtain the initial calculation parameters;

[0017] Based on the initial calculation parameters, the visible faces in the target parameterized model under the current viewpoint are summed to obtain the total evaluation value under the current viewpoint.

[0018] Gradient calculation is performed on the observation camera to obtain the gradient value;

[0019] Determine the effective step size based on the aforementioned objective constraints;

[0020] The observation camera is updated based on the gradient value and the effective step size to obtain the pose information of the next viewpoint.

[0021] Determine whether the pose information of the next viewpoint satisfies the target constraint. If the pose information of the next viewpoint does not satisfy the target constraint, reacquire the effective step size and / or recalculate the gradient value.

[0022] Alternatively, the optimal viewpoint pose of the observation camera can be obtained if the pose information of the next viewpoint satisfies the target constraint.

[0023] In some embodiments, the spatial characteristics include the three-dimensional geometric features, pose information, and attitude evolution characteristics of the target spacecraft. The parameterization of the spatial characteristics to obtain a target parameterized model includes:

[0024] The three-dimensional geometric features, pose information, and attitude evolution characteristics of the target spacecraft are discretized to obtain multiple bounded planes;

[0025] The target pose information of each bounded plane is obtained based on the geometric center position and normal vector of each bounded plane;

[0026] The target pose information, three-dimensional geometric features, and pose evolution characteristics are integrated and processed to obtain a set of facets;

[0027] A target parameterized model is constructed based on the set of patches.

[0028] In some embodiments, the optimization of the target parameterization model and the camera parameterization model based on pixel values ​​per unit area to obtain the viewpoint planning optimization target includes:

[0029] Calculate the ratio of each patch in the target parameterization model to the projected area of ​​the observation camera to obtain the pixel value per unit area;

[0030] The total evaluation value is obtained by summing the pixel values ​​per unit area corresponding to all visible faces in the target parameterization model described below from the current viewpoint.

[0031] The overall evaluation value is optimized based on the target parameterization model and the camera parameterization model to obtain the viewpoint planning optimization target.

[0032] In some embodiments, the first constraint parameters include the motion state of the spacecraft in orbit, space illumination environment parameters, camera imaging constraints, and safety constraints. The step of obtaining the first constraint parameters and constructing target constraints based on the first constraint parameters and the viewpoint planning optimization objective includes:

[0033] Obtain the camera imaging constraint from the first constraint parameters, and construct depth constraint and observation angle constraint based on the camera imaging constraint and viewpoint planning optimization target. The depth constraint is used to limit the imaging distance range from the target spacecraft to the observation camera, and the observation angle constraint is used to limit the incident angle of the observation camera.

[0034] Obtain the safety constraints and space illumination environment parameters from the first constraint parameters. Based on the safety constraints, space illumination environment parameters, and the viewpoint planning optimization target, construct a safe area constraint. The safe area constraint is used to control the observation camera to avoid direct sunlight and / or Earth-Moon reflected light, and to limit the safe distance range between the observation camera and the target spacecraft.

[0035] Obtain the motion state of the spacecraft in orbit from the first constraint parameters, and construct a step size constraint based on the motion state. The step size constraint is used to limit the maximum spatial distance between adjacent viewpoints based on the orbit and velocity of the target spacecraft.

[0036] The depth of field constraint, observation angle constraint, safe area constraint, and step size constraint are integrated to obtain the target constraint.

[0037] In some embodiments, updating the target parameterized model based on the first image to obtain a processed target parameterized model includes:

[0038] The first image is subjected to feature extraction and 3D reconstruction to obtain a target point cloud model;

[0039] The target point cloud model and the target parameterized model are compared and fused to obtain correction information;

[0040] Based on the correction information, the set of faces in the target parameterized model is adjusted to obtain the adjusted set of faces;

[0041] The adjusted patch set is integrated to obtain the processed target parameterized model.

[0042] This application provides an embodiment of a device for acquiring complete sequence images of a target spacecraft, comprising:

[0043] The acquisition module is used to acquire the space characteristics of the target spacecraft and the imaging characteristics of the observation camera, perform parameterization processing on the space characteristics to obtain a target parameterization model, and perform parameterization processing on the imaging characteristics to obtain a camera parameterization model.

[0044] The processing module is used to optimize the target parameterization model and the camera parameterization model based on the pixel value per unit area to obtain the viewpoint planning optimization target;

[0045] The acquisition module is further configured to acquire the first constraint parameter and construct the target constraint based on the first constraint parameter and the viewpoint planning optimization objective;

[0046] A construction module is used to construct an optimization model based on the target parameterization model, the camera parameterization model, the viewpoint planning optimization objective, and the target constraints;

[0047] The processing module is also used to perform pose acquisition processing on the optimized mathematical model to obtain the optimal viewpoint pose of the observation camera;

[0048] The processing module is also used to adjust the movement trajectory and attitude of the observation camera based on the optimal viewpoint pose and then acquire an image of the target spacecraft to obtain a first image;

[0049] The processing module is further configured to update the target parameterized model based on the first image to obtain the processed target parameterized model.

[0050] The processing module is also used to iteratively execute multiple first images, integrate the multiple first images, and obtain a complete sequence of images of the target spacecraft.

[0051] The computer device provided in this application includes a memory and a processor. The memory stores a computer program that can run on the processor. When the processor executes the program, it implements the method described in this application.

[0052] The computer-readable storage medium provided in this application embodiment stores a computer program thereon, which, when executed by a processor, implements the method described in this application embodiment.

[0053] This application provides a method and apparatus for acquiring a complete sequence of images of a target spacecraft. The method involves acquiring the spacecraft's spatial characteristics and the imaging characteristics of the observation camera, then parametrically processing these characteristics to obtain a target parametric model and a camera parametric model. The models are optimized based on pixel values ​​per unit area to determine the viewpoint planning optimization target. Target constraints are constructed by combining the spacecraft's motion state in orbit, space illumination environment parameters, camera imaging constraints, and safety constraints. An optimization model is built based on the model, optimization target, and constraints to solve for the optimal viewpoint pose of the camera. The camera trajectory and attitude are adjusted to acquire images, and the target parametric model is updated based on the images. The above steps are iteratively executed, and multiple images are integrated to obtain a complete sequence of images. This improves the quality of the sequence of images, provides high-quality data support for 3D reconstruction, adapts to complex space environments, meets the needs of space monitoring, resource exploration, and other missions, and solves the technical problems mentioned in the background art. Attached Figure Description

[0054] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the description of the embodiments of this application or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0055] Figure 1 A schematic diagram illustrating the implementation process of a method for acquiring complete sequence images of a target spacecraft, provided in an embodiment of this application;

[0056] Figure 2 A schematic diagram illustrating the implementation process of obtaining the optimal viewpoint pose, provided in an embodiment of this application;

[0057] Figure 3 This is a schematic diagram of the structure of a device for acquiring complete sequence images of a target spacecraft, provided in an embodiment of this application. Detailed Implementation

[0058] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0059] The following description of some technologies involved in the embodiments of this application is provided to aid understanding and should be considered merely exemplary. Therefore, those skilled in the art should recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of this application. Similarly, for clarity and brevity, some descriptions of well-known functions and structures are omitted in the following description.

[0060] Figure 1 This is a schematic flowchart illustrating the implementation of a method for acquiring complete sequence images of a target spacecraft according to an embodiment of this application, including steps 101 to 108. Wherein, Figure 1 This is merely one execution order shown in the embodiments of this application and does not represent the only execution order for a method of acquiring complete sequence images of a target spacecraft. Where the final result can be achieved, Figure 1 The steps shown can be performed in parallel or in reverse order.

[0061] Step 101: Obtain the spatial characteristics of the target spacecraft and the imaging characteristics of the observation camera. Perform parameterization processing on the spatial characteristics to obtain the target parameterization model and perform parameterization processing on the imaging characteristics to obtain the camera parameterization model.

[0062] In this embodiment of the application, the spatial characteristics of the target spacecraft specifically include three-dimensional geometric features (such as surface protrusions, depressions, and edge contours) and pose information.

[0063] First, the surface of the target spacecraft is decomposed into multiple bounded planes (i.e., patches). The number of patches is determined based on the geometric complexity of the spacecraft to ensure that its surface features can be completely depicted. For each patch, the geometric center position in three-dimensional space and the unit normal vector pointing outward of the spacecraft are defined to obtain the pose information of each patch. Finally, the pose information of all patches, the three-dimensional geometric features of the spacecraft, the pose information and attitude evolution characteristics are integrated to form a target parameterized model composed of a set of patches, which can accurately reflect the spacecraft's spatial morphology and motion state.

[0064] The imaging characteristics of an observation camera include geometric characteristics (such as lens size), imaging principles (such as optical imaging mechanisms), and motion control characteristics (such as attitude adjustment range). The parameterization process is as follows: First, the above characteristics are parameterized to construct the camera's pose model and projection model. The pose model is used to characterize the camera's spatial position and orientation information. The projection model is constructed based on the pinhole imaging principle, defining the mapping relationship between three-dimensional spatial points (i.e., the surface of the target spacecraft) and the pixels on the camera's imaging plane. The mapping relationship incorporates core optical parameters such as the camera's focal length and sensor size to ensure projection accuracy. Finally, the pose model and the improved projection model are integrated to obtain a parameterized camera model that can accurately describe the camera's imaging characteristics.

[0065] Step 102: Optimize the target parameterization model and the camera parameterization model based on the pixel value per unit area to obtain the viewpoint planning optimization target.

[0066] In this embodiment, firstly, based on the actual area of ​​each facet in the target parameterized model and the projection relationship of the camera parameterized model, the projected area of ​​each facet on the camera imaging plane is calculated, and then the ratio of the two, i.e., the pixel value per unit area, is obtained; then, visible faces that can be observed by the camera from the current viewpoint are selected, and the pixel values ​​per unit area of ​​all visible faces are summed to obtain the overall imaging quality evaluation value of the current viewpoint; finally, maximizing this overall evaluation value is used as the viewpoint planning optimization objective.

[0067] Step 103: Obtain the first constraint parameters, and construct the target constraints based on the first constraint parameters and the viewpoint planning optimization objective.

[0068] In this embodiment, the depth of field constraint is as follows: based on the camera's optical parameters (such as focal length and aperture size), the range of clear imaging distances from the target spacecraft to the camera sensor plane is determined to ensure that when the image is captured within this range, the circle of confusion size is less than the allowable threshold, thus avoiding image blurring.

[0069] Observation angle constraint: Combine the camera's field of view threshold to set a reasonable range for the camera's incident angle. That is, the angle between the ray from the camera's optical center to the surface of the patch and the normal to the patch must be less than this threshold to avoid imaging distortion (such as radial distortion and tangential distortion) caused by an excessively large incident angle.

[0070] Safety zone constraints: By acquiring spatial position data of the target spacecraft relative to the sun and moon, the effective observation angle area of ​​the camera is calculated to ensure that the camera avoids direct sunlight or reflected light from the Earth and Moon, preventing overexposure and glare; at the same time, a safe distance range (R1, R2) between the camera and the target spacecraft is set, where R1 is the lower bound to ensure the acquisition of image details, and R2 is the upper bound to avoid collision risks and information interference. The range can be dynamically adjusted according to the size of the spacecraft and the imaging capabilities of the camera.

[0071] Step size constraint: Referring to the spatial domain-based method in keyframe extraction, the maximum spatial distance between adjacent viewpoints is calculated based on the orbital radius and speed of the target spacecraft, and the positional difference between the new viewpoint and the current viewpoint is limited to not exceeding the maximum spatial distance.

[0072] Finally, the above-mentioned depth of field constraints, observation angle constraints, safe zone constraints, and step size constraints are integrated to obtain the target constraints for the optimization of the viewpoint planning.

[0073] Step 104: Construct an optimization model based on the target parameterization model, camera parameterization model, viewpoint planning optimization objective, and target constraints.

[0074] In this embodiment, a multi-objective optimization mathematical model is constructed based on the target parameterization model and camera parameterization model, the determined viewpoint planning optimization objective, and the target constraints. The core optimization objective is to maximize the sum of pixel values ​​per unit area of ​​visible images, while depth-of-field constraints, observation angle constraints, safe area constraints, and step size constraints are used as constraints, forming a solvable mathematical optimization framework.

[0075] Step 105: Perform pose acquisition processing on the optimized mathematical model to obtain the optimal viewpoint pose of the observation camera.

[0076] In this embodiment of the application, the initial pose of the observation camera (including the initial spatial position and orientation) and the set of patches in the target parameterization model are initialized to determine the initial calculation parameters.

[0077] Based on the initial calculation parameters, the pixel value per unit area of ​​all visible faces under the current viewpoint is calculated one by one, and then summed to obtain the overall image quality evaluation value of the current viewpoint.

[0078] The gradient of the objective function (i.e., the overall evaluation value) with respect to the camera pose is calculated using the chain rule. This gradient value includes the position component and the normal vector component, reflecting the trend of the overall evaluation value as the camera pose changes.

[0079] By combining the target constraints, the effective step size that meets the constraints is calculated to avoid the pose update exceeding the constraint range due to excessively large step sizes.

[0080] Pose update: The camera pose is updated according to the calculated gradient value and effective step size to obtain the pose information of the next viewpoint.

[0081] Verify whether the updated center point pose information satisfies all target constraints. If not, adjust the effective step size or recalculate the gradient value, and perform the pose update again. If satisfied, determine whether the objective function has converged or reached the preset maximum number of iterations. If it has not converged and the maximum number of iterations has not been reached, return to the step of calculating the current total evaluation value and continue iterating. If the convergence condition is met or the maximum number of iterations has been reached, stop iterating, and the current camera pose is the optimal viewpoint pose.

[0082] Step 106: After adjusting the movement trajectory and attitude of the observation camera based on the optimal viewpoint pose, an image of the target spacecraft is acquired to obtain the first image.

[0083] In this embodiment, the camera's movement trajectory and attitude are adjusted by the camera's motion control system according to the optimal viewpoint pose, ensuring that the camera accurately reaches the optimal viewpoint position and is adjusted to the optimal orientation; then the camera is controlled to start image acquisition to obtain a single frame image of the target spacecraft, which is the first image, which has high definition and rich details.

[0084] Step 107: Update the target parameterized model based on the first image to obtain the processed target parameterized model.

[0085] In this embodiment, a feature extraction algorithm (such as a feature point detection algorithm based on deep learning) is first used to extract target feature information from the image. Then, a three-dimensional reconstruction algorithm (such as an algorithm based on motion recovery structure) is used to generate a target point cloud model of the target spacecraft in the current state. The target point cloud model is compared and fused with the target parameterized model to supplement the missing three-dimensional geometric feature information (such as surface details not covered by the initial modeling) in the target parameterized model and correct the orbit and attitude evolution deviations. Based on the fused information, the number of facets, the geometric center position and the unit normal vector in the target parameterized model are adjusted to obtain the processed target parameterized model.

[0086] Step 108: Iteratively execute steps 102 to 107 to obtain multiple first images. Integrate and process the multiple first images to obtain a complete sequence of images of the target spacecraft.

[0087] In this embodiment, the processed target parameterized model is used as new input, and steps 102 to 107 are executed iteratively: the viewpoint planning optimization target is re-determined based on the updated target parameterized model, the target constraints are adjusted, a new optimization model is constructed, the new optimal viewpoint pose is solved, new images are acquired, and the target parameterized model is updated. Through multiple iterations, multiple first images with continuous features and high consistency are acquired; finally, all acquired images are integrated and processed (e.g., sorted according to acquisition time order, and redundant images are removed) to obtain a complete sequence of images of the target spacecraft. This sequence of images can be directly used for subsequent tasks such as 3D reconstruction and target tracking.

[0088] This application's embodiments address the problems of traditional methods relying on experience to plan camera pose and unstable imaging quality by constructing a closed-loop process of parametric modeling, target optimization, constraint adaptation, and iterative updates, ensuring the integrity, coherence, and high definition of sequential images. By dynamically updating the target parametric model to adapt to the orbital and attitude evolution characteristics of the target spacecraft, it effectively copes with the complexity of the space environment and improves the method's adaptability to complex space mission scenarios. Integrating the target and camera parametric models and multi-dimensional constraints provides high-quality data support for subsequent tasks such as 3D reconstruction and target tracking, meeting the high-precision requirements of space monitoring, resource exploration, and other missions.

[0089] In the above Figure 1 Based on the above, this application also provides a schematic diagram of the implementation process for obtaining the optimal viewpoint pose, as shown below. Figure 2 As shown, steps 201 to 207 are included:

[0090] Step 201: Initialize the initial pose of the observation camera and the set of patches in the target parameterization model to obtain the initial calculation parameters.

[0091] In this embodiment, the initial pose of the observation camera and the patch set in the target parameterized model are first initialized to obtain initial calculation parameters. The initial pose of the observation camera includes initial spatial position coordinates and initial orientation. The initial position can be determined based on the initial pose information of the target spacecraft, the mission-preset observation starting point, or the initial positioning data provided by the ground telemetry and control system. The initial orientation is set to point towards the geometric center of the target spacecraft. The initialization of the patch set in the target parameterized model is based on the patch attributes after finite element mesh generation, clarifying the geometric center position, unit normal vector, and corresponding three-dimensional geometric features, orbital and attitude evolution correlation information of each patch. Through the above initialization, the basic parameter set required for subsequent calculations is formed.

[0092] Step 202: Based on the initial calculation parameters, sum the visible faces in the target parameterized model under the current viewpoint to obtain the total evaluation value under the current viewpoint.

[0093] In this embodiment, based on the initialization calculation parameters, the overall evaluation value of all visible faces in the target parameterized model at the current viewpoint is calculated. Specifically, firstly, visible faces that can be effectively observed under the current camera pose (i.e., faces that are not occluded by other faces and are within the camera's field of view) are selected; for each visible face, the pixel value per unit area is calculated based on its actual area and its projected area on the camera's imaging plane. This value is calculated based on the projection relationship and optical parameters such as focal length in the camera parameterized model, reflecting the image sharpness and detail richness of the face at the current viewpoint; then, the pixel values ​​per unit area of ​​all visible faces are summed to obtain the overall image quality evaluation value for the current viewpoint.

[0094] Step 203: Perform gradient calculation on the observation camera to obtain the gradient value.

[0095] In this embodiment, the chain rule is used to calculate the gradient of the overall evaluation value relative to the observed camera attitude, resulting in a gradient value that includes position and normal components. The core of gradient calculation is to quantify the trend of the overall evaluation value as the camera attitude changes. The position component reflects the influence of camera spatial position movement on the overall evaluation value, while the normal component reflects the influence of camera orientation adjustment on the overall evaluation value. During the calculation, the pose information of the facets in the target parameterized model and the projection characteristics of the camera parameterized model are combined. Partial derivatives of the overall evaluation value with respect to the camera position coordinates and orientation normal vector are derived through vector operations, ultimately integrating these to obtain the complete gradient value.

[0096] Step 204: Determine the effective step size based on the target constraint.

[0097] In this embodiment, the effective step size for camera attitude updates is determined based on target constraints. First, the maximum step size threshold calculated based on the target spacecraft's orbital radius and velocity is analyzed to ensure the update step size does not exceed this threshold. Simultaneously, the safe distance range in the safe region constraint is considered to ensure that the distance between the camera and the target spacecraft's surface remains within this range after the camera position corresponding to the step size moves. Furthermore, the clear imaging distance range of the depth-of-field constraint and the incident angle threshold of the observation angle constraint are referenced to verify that the attitude adjustment corresponding to the step size will not cause the camera to exceed its effective imaging range or induce imaging distortion. Considering all the above constraints, an effective step size that meets all requirements is determined through numerical calculation, avoiding excessively large step sizes that violate constraints or excessively small step sizes that affect iteration efficiency.

[0098] Step 205: Update the observation camera based on the gradient value and effective step size to obtain the pose information of the next viewpoint.

[0099] In this embodiment, the attitude of the observation camera is updated based on the calculated gradient value and the determined effective step size to obtain the pose information of the next viewpoint. During the update process, the current position coordinates and orientation normal vector of the camera are adjusted according to the optimization direction indicated by the gradient value: the position adjustment amount is determined by the product of the position component of the gradient and the effective step size, and the orientation adjustment amount is determined by the product of the normal vector component of the gradient and the effective step size. After adjustment, the new camera pose needs to be re-represented as spatial position coordinates and unit normal vector to form the pose information of the next viewpoint.

[0100] Step 206: Determine whether the pose information of the next viewpoint meets the target constraints. If the pose information of the next viewpoint does not meet the target constraints, reacquire the effective step size and / or recalculate the gradient value.

[0101] In this embodiment, the updated next-viewpoint pose information undergoes constraint satisfaction verification to determine whether it meets all target constraints. The verification includes: whether the distance between the next-viewpoint position and the target spacecraft surface is within the safe zone constraint and avoids direct sunlight or Earth-Moon reflected light range; whether the camera incident angle meets the threshold requirement of the observation angle constraint and has no significant risk of imaging distortion; whether the distance between the camera and the previous viewpoint does not exceed the maximum step size threshold of the step size constraint; and whether the camera position is within the clear imaging distance range defined by the depth-of-field constraint.

[0102] If the verification result shows that the pose information of the next viewpoint does not meet any of the target constraints, then the effective step size is re-acquired (e.g., the step size is reduced to a certain proportion of the original step size) and / or the gradient value is recalculated (the weight factor of the gradient calculation is adjusted in combination with the constraint violation terms), and the camera pose update processing step is returned to perform pose update and verification again.

[0103] Step 207, or, if the pose information of the next viewpoint satisfies the target constraints, obtain the optimal viewpoint pose of the observation camera.

[0104] In this embodiment, if the verification result shows that the next viewpoint pose information satisfies all target constraints, it is further determined whether the objective function has converged (i.e., the difference between the current total evaluation value and the previous viewpoint total evaluation value is less than a preset convergence threshold) or whether the preset maximum number of iterations has been reached. If the convergence condition is met or the maximum number of iterations is reached, the current next viewpoint pose is the optimal viewpoint pose for the observation camera; if not, the updated pose is used as the new current viewpoint, and the calculation step for the current viewpoint total evaluation value is returned to continue iterative optimization until the optimal viewpoint pose is obtained.

[0105] This application employs an iterative solution logic of initialization-gradient calculation-step size adaptation-constraint verification, combined with the Jacobi method to accurately calculate gradient values, ensuring the efficiency and accuracy of solving for the optimal viewpoint pose and avoiding getting trapped in local optima. A constraint verification mechanism dynamically adjusts the effective step size and gradient values, ensuring that the acquired viewpoint pose strictly meets constraints such as depth of field and safe distance, mitigating collision risks and imaging distortion while guaranteeing image quality. Pose optimization is based on the overall evaluation value of visible images, avoiding the generation of invalid or redundant viewpoints, improving image acquisition efficiency, and ensuring that images acquired from each viewpoint effectively support sequence integrity and detail richness.

[0106] In some embodiments, the spatial characteristics include the three-dimensional geometric features, pose information, and attitude evolution characteristics of the target spacecraft. The spatial characteristics are parameterized to obtain a parameterized model of the target, including: discretizing the three-dimensional geometric features, pose information, and attitude evolution characteristics of the target spacecraft to obtain multiple bounded planes.

[0107] Specifically, the spatial characteristics of the target spacecraft clearly include three-dimensional geometric features (such as surface protrusions, depressions, edge contours, and overall shape structure), pose information (such as orbital period and orbital plane parameters), and attitude evolution characteristics (such as attitude angle variation patterns and attitude adjustment frequency). First, based on the target spacecraft's design drawings, initial on-orbit observation data, or a pre-set three-dimensional model, the mesh generation accuracy is determined according to the spacecraft's geometric complexity. For large spacecraft with complex surface structures and numerous protrusions and depressions, fine mesh generation is used to capture more details; for small spacecraft with regular shapes, coarse mesh generation is used to improve computational efficiency. Subsequently, using a finite element mesh generation algorithm, the spacecraft's three-dimensional surface is decomposed into multiple non-overlapping, continuously connected bounded planes (i.e., patches). Each patch is a local region mapping of the spacecraft's surface, and its shape and size are determined by the mesh generation accuracy and the local three-dimensional geometric features of the spacecraft, ensuring that the set of all patches can completely cover the spacecraft surface while accurately correlating the spatial distribution patterns of pose information and attitude evolution characteristics.

[0108] Furthermore, the target pose information of each bounded plane is obtained based on the geometric center position and normal vector of each bounded plane.

[0109] Specifically, for each bounded plane obtained by discretization, its geometric center position and normal vector are determined, thereby obtaining the target pose information.

[0110] By calculating the centroid coordinates of each bounded plane, its geometric center position is obtained. These coordinates are based on a three-dimensional rectangular coordinate system (such as a celestial coordinate system or an orbital coordinate system) to ensure that the position of the patch in space is accurately reflected.

[0111] Define a normal vector for each bounded plane, with its direction strictly pointing outwards from the spacecraft. To ensure the mathematical rigor of the model, normalize all normal vectors to avoid affecting subsequent imaging calculations and optimizations due to inconsistent normal vector scales.

[0112] The geometric center position of each bounded plane is associated with the normalized normal vector to form the target pose information of the bounded plane, which includes both spatial position information and orientation information.

[0113] Furthermore, the target pose information, three-dimensional geometric features, pose information, and attitude evolution characteristics are integrated and processed to obtain a set of patches.

[0114] Specifically, the target pose information of each bounded plane is deeply integrated with the three-dimensional geometric features, pose information, and attitude evolution characteristics of the target spacecraft to form a set of facets.

[0115] Associating 3D geometric features: Associating the local geometric attributes such as the shape and size of each patch with the overall 3D geometric features of the spacecraft (such as the distribution of components such as the fuselage, solar panels, and antennas) to clarify the physical structural region of the spacecraft corresponding to each patch.

[0116] Associated pose information: Mapping information such as the operating cycle to the spatial coordinates of each patch, so that the geometric center position of the patch can be dynamically updated according to the orbital operation law, reflecting the orbital motion characteristics of the spacecraft.

[0117] Associated attitude evolution characteristics: By combining the spacecraft's attitude angle changes and attitude adjustment rules, the attitude evolution information is associated with the normal vector of the surface patch, so that the orientation of the normal vector can be dynamically adjusted with attitude changes, reflecting the attitude evolution characteristics of the spacecraft.

[0118] Forming a patch set: All bounded planes (patches) that are associated with 3D geometric features, pose information, and attitude evolution characteristics are summarized to obtain a patch set, in which each element fully contains the pose information of a single patch and the corresponding spatial characteristic associated data.

[0119] Furthermore, a target parameterized model is constructed based on the set of patches.

[0120] Specifically, the spatial connection relationship of each facet in the facet set is clearly defined to ensure that adjacent faces transition continuously in geometric position and normal vector orientation, avoiding model breaks or overlaps; secondly, the facet set is bound to the overall spatial characteristics of the spacecraft, so that the model can reflect the shape and structure of the spacecraft through the geometric distribution of the faces, and reflect the orbital operation and attitude evolution laws through the dynamic correlation of facet pose information; the final target parameterized model is formed.

[0121] This application's embodiments employ discretization to transform the target spacecraft into a bounded set of planes. Compared to traditional vertex sampling methods, this approach more accurately characterizes the spacecraft's three-dimensional geometric features (such as protrusions and depressions), pose information, and attitude evolution characteristics, thus improving the model's descriptive accuracy. Integrating the target pose information and spatial characteristics of each facet makes the target parametric model both structured and flexible, allowing the number of facets to be adjusted according to the spacecraft's size and complexity, adapting to the modeling needs of different types of spacecraft. All facet normal vectors are normalized to ensure the model's mathematical rigor, providing a precise digital foundation for subsequent projection calculations, viewpoint optimization, and other steps, reducing errors in subsequent processing.

[0122] In some embodiments, the target parameterized model and the camera parameterized model are optimized based on the pixel value per unit area to obtain the viewpoint planning optimization target, including: calculating the ratio of each patch in the target parameterized model to the projected area on the observation camera to obtain the pixel value per unit area.

[0123] Specifically, the actual area of ​​each facet in the target parametric model is obtained. This area is determined based on the finite element mesh generation results. Each facet, as a bounded plane on the spacecraft surface, has its actual area directly determined by the mesh generation accuracy and the facet's geometry, and is explicitly stored in the parametric model and can be directly accessed.

[0124] Calculate the projected area of ​​each patch on the imaging plane of the observation camera. The calculation of the projected area relies on the projection model in the camera's parametric model (built based on the pinhole imaging principle), combined with the camera's intrinsic parameters (such as focal length and sensor size) and extrinsic parameters (such as the spatial position and orientation of the current pose): through the mapping relationship of the camera's projection model, the three-dimensional geometric center position and boundary coordinates of the patch are converted into two-dimensional pixel coordinates on the imaging plane, thereby calculating the area of ​​the projected region of the patch on the imaging plane. In this process, the Euclidean distance between the camera and the patch, as well as the angle between the camera normal vector and the patch normal vector, must be considered. These two factors affect the size of the projected area through the projection model. The closer the distance and the smaller the angle, the larger the projected area and the higher the pixel value per unit area.

[0125] Finally, calculate the pixel value per unit area for each patch. The actual area of ​​the patch is then compared to the calculated projected area to obtain the pixel value per unit area for each patch.

[0126] Furthermore, the pixel values ​​per unit area corresponding to all visible faces in the parameterized model of the target at the current viewpoint are summed to obtain the total evaluation value.

[0127] Specifically, firstly, visible faces from the current viewpoint are selected. The selection criteria combine the face distribution from the target parametric model and the field-of-view constraints from the camera parametric model: on one hand, it determines whether a face is occluded by other faces (based on the face's spatial position and normal vector orientation, faces not blocked by other faces are considered unoccluded); on the other hand, it determines whether the face is within the camera's field of view (based on the camera pose and field-of-view parameters, spatial geometric calculations determine whether the face is within the camera's observable range). Faces that simultaneously satisfy the conditions of being unoccluded and within the field of view are considered visible faces from the current viewpoint.

[0128] Secondly, the pixel values ​​per unit area of ​​all visible images are summed. The pixel values ​​per unit area of ​​each visible image are extracted one by one and accumulated to obtain the overall image quality evaluation value for the current viewpoint. This overall evaluation value directly reflects the richness of image detail that the camera can acquire from the current viewpoint. The higher the overall evaluation value, the more visible images can present a clear image effect from the current viewpoint, and the better the image quality.

[0129] Furthermore, the overall evaluation value is optimized based on the target parameterization model and the camera parameterization model to obtain the viewpoint planning optimization target.

[0130] Specifically, first, the core optimization direction is clarified. Combining the patch characteristics of the target parametric model and the imaging constraints of the camera parametric model, the core optimization is determined to maximize the pixel value per unit area. The patch characteristics of the target parametric model include the geometric distribution of the patches, the orientation of the normal vector, and the spatial density, while the imaging constraints of the camera parametric model include the field of view range, focal length limit, and sensor resolution. These factors together determine the optimization space of the overall evaluation value.

[0131] Secondly, the parameters of the two models are optimized and adapted. For the target parameterized model, the geometric complexity and spatial distribution of the patches are considered. For regions with dense 3D geometric features, many protrusions or depressions, it is necessary to ensure that the optimization direction maximizes the pixel value per unit area of ​​the patch in that region; for cases where the patch normal vectors have diverse orientations, the optimization direction needs to adapt to the observation requirements of patches with different orientations. For the camera parameterized model, combined with the imaging rules of its projection model, it is ensured that the optimization direction conforms to the optical characteristics of the camera, avoiding imaging distortion or exceeding the effective imaging range due to optimization.

[0132] Finally, the final viewpoint planning optimization goal is determined. Through the above optimization and adaptation, maximizing the sum of pixel values ​​per unit area of ​​all visible images is defined as the viewpoint planning optimization goal.

[0133] This application embodiment quantifies the imaging quality of a single patch by using pixel value per unit area as the core evaluation index. Compared to traditional methods without clear optimization indicators, this makes the viewpoint planning objective clearer and more quantifiable. By summing and optimizing the visible patch values, it ensures that viewpoint planning always focuses on maximizing imaging detail, improving the sharpness and detail richness of the sequence of images, and avoiding detail loss due to improper viewpoint selection. The parameterized model relating the target and the camera is optimized to adapt the optimized target to the camera's imaging characteristics and the target's geometric distribution, ensuring the feasibility and effectiveness of viewpoint planning and providing clear guidance for subsequent optimal viewpoint pose determination.

[0134] In some embodiments, the first constraint parameters include space illumination environment parameters of the spacecraft in orbit, camera imaging constraints, and safety constraints. Obtaining the first constraint parameters and constructing target constraints based on the first constraint parameters and the viewpoint planning optimization objective includes: obtaining the camera imaging constraints in the first constraint parameters, and constructing depth constraints and observation angle constraints based on the camera imaging constraints and the viewpoint planning optimization objective. The depth constraints are used to limit the imaging distance range from the target spacecraft to the observation camera, and the observation angle constraints are used to limit the incident angle of the observation camera.

[0135] Specifically, the camera imaging constraints are obtained: the camera imaging constraints in the first constraint parameters include the camera's optical parameters (such as focal length, aperture size, and sensor size), field of view threshold, resolution requirements, etc.

[0136] Depth-of-field constraints are constructed based on the optical parameters in the camera imaging constraints and the viewpoint planning optimization objective (maximizing the sum of pixel values ​​per unit area of ​​visible surfaces). The core of the depth-of-field constraint is to limit the range of sharp imaging distances from the target spacecraft to the observation camera, ensuring that the circle of confusion size is less than an allowable threshold (i.e., the threshold of blur acceptable to the human eye or subsequent image processing algorithms) during surface imaging. Specifically, the effective depth-of-field range of the camera is calculated based on its focal length, aperture size, and sensor resolution. Using the formula relating the circle of confusion diameter to lens parameters, the minimum and maximum sharp imaging distances from the target spacecraft to the camera sensor plane are derived; this range constitutes the specific content of the depth-of-field constraint.

[0137] Constructing observation angle constraints: Combining the field-of-view threshold and viewpoint planning optimization objectives in the camera imaging constraints, observation angle constraints are constructed to limit the incident angle of the observation camera. The incident angle is defined as the angle between the ray from the camera's optical center to the surface of the target spacecraft patch and the normal to that patch. Based on the camera's field-of-view threshold (determined by the lens focal length and sensor size), a reasonable threshold for the incident angle is set (usually less than half the field of view to avoid image edge distortion). Through spatial geometric calculations, it is ensured that the incident angle of the camera on any patch is less than this threshold to reduce the impact of radial and tangential distortion on image quality, while ensuring that the patch can be effectively observed within the camera's field of view, avoiding incomplete imaging or loss of detail due to excessively large angles, and ensuring the accuracy of pixel value calculation per unit area.

[0138] Furthermore, the safety constraints and space illumination environment parameters in the first constraint parameters are obtained. Based on the safety constraints, space illumination environment parameters, and viewpoint planning optimization objectives, a safe area constraint is constructed. The safe area constraint is used to control the observation camera to avoid direct sunlight and / or Earth-Moon reflected light, and to limit the safe distance range between the observation camera and the target spacecraft.

[0139] Specifically, the safety constraints in the first constraint parameters include anti-collision requirements and information interference protection thresholds, while the space illumination environment parameters include the spatial position coordinates of the Sun and the Earth-Moon, illumination intensity, etc. These parameters are obtained through real-time acquisition by on-orbit observation equipment or ephemeris data provided by the ground telemetry and control system.

[0140] Constructing safe region constraints: Based on safety constraints, spatial lighting environment parameters, and viewpoint planning optimization objectives, safe region constraints are constructed, which include two core aspects:

[0141] Avoiding direct sunlight: By acquiring real-time spatial position data of the target spacecraft, the sun, and the moon, three-dimensional coordinate transformation and vector calculations are performed to calculate the effective observation angle area of ​​the camera. This ensures that the camera's observation direction does not directly face the direct path of sunlight or reflected light from the Earth and Moon, preventing overexposure of the camera sensor and glare that could affect image clarity and the effectiveness of pixel values ​​per unit area.

[0142] Define a safe distance range: Set a safe distance range between the camera and the target spacecraft. The safe distance range needs to be dynamically adjusted based on the size of the target spacecraft, the camera's imaging capabilities, and the space environment to ensure maximum image quality while maintaining safety.

[0143] Furthermore, the motion state of the spacecraft in orbit is obtained from the first constraint parameters, and a step size constraint is constructed based on the motion state. The step size constraint is used to limit the maximum spatial distance between adjacent viewpoints based on the target spacecraft's orbit and velocity.

[0144] Specifically, the motion state of the spacecraft in orbit is obtained: the motion state of the spacecraft in orbit in the first constraint parameter includes the target spacecraft's orbital radius, orbital speed, orbital plane parameters, attitude angle variation law, etc. These parameters are obtained through real-time monitoring by orbital measurement equipment or derivation from a preset orbital model.

[0145] Step size constraints are constructed: Based on the aforementioned motion state parameters and combined with the spatial domain-based sampling method in keyframe extraction, step size constraints are constructed to limit the maximum spatial distance between adjacent viewpoints. The maximum step size threshold for camera movement is calculated based on the target spacecraft's orbital radius and velocity. By analyzing the spacecraft's positional change during camera movement, the distance between adjacent viewpoints is ensured to remain within the specified limits, ensuring that the feature overlap of the target spacecraft in adjacent images meets the requirements for subsequent registration and 3D reconstruction. For example, when the spacecraft's velocity is high and its orbital radius is large, the maximum step size threshold is appropriately increased to accommodate its rapid displacement and avoid viewpoint redundancy due to excessively small step sizes. When the spacecraft's surface features are dense, the maximum step size threshold is appropriately decreased to ensure continuous feature capture. Finally, the maximum step size threshold is determined as the core threshold of the step size constraint using a mathematical formula, limiting the positional difference between the new viewpoint and the current viewpoint to no more than this threshold.

[0146] Furthermore, the depth of field constraint, observation angle constraint, safe area constraint, and step size constraint are integrated to obtain the target constraint.

[0147] Specifically, the logical relationships and priorities among the sub-constraints are analyzed first. Safety area constraints (collision prevention, direct sunlight avoidance) have the highest priority, ensuring the safety of the acquisition process; depth of field constraints and observation angle constraints have core priorities, ensuring image quality to match the viewpoint planning optimization goal; step size constraints have efficiency priority, balancing acquisition efficiency and data consistency. Secondly, a constraint fusion algorithm is used to eliminate potential conflicts between sub-constraints (such as the adaptation and adjustment of step size thresholds and safety distance intervals), ensuring that all constraints can be satisfied simultaneously. Finally, the integrated constraints are transformed into mathematical expressions, forming target constraints that can be embedded in the optimization model, providing a clear constraint basis for subsequently solving the optimal viewpoint pose of the observation camera.

[0148] This application's embodiments comprehensively integrate camera imaging constraints, safety constraints, space lighting environment, and spacecraft motion state to construct multi-dimensional target constraints, solving the problems of poor imaging quality and high safety risks caused by traditional single constraints. Depth of field and observation angle constraints ensure imaging clarity, safety area constraints avoid lighting interference and collision risks, step size constraints ensure data consistency, and multiple constraints collaboratively adapt to viewpoint planning and optimization targets, achieving a balance between quality, safety, and efficiency. The constraint construction process combines keyframe selection strategies with space environment characteristics to dynamically adapt to the target spacecraft's motion and environmental changes, improving the method's robustness in complex space scenarios.

[0149] In some embodiments, updating the target parameterized model based on the first image to obtain the processed target parameterized model includes: performing feature extraction and three-dimensional reconstruction processing on the first image to obtain a target point cloud model.

[0150] Specifically, a deep learning-based feature point detection algorithm (such as a convolutional neural network feature extraction model) is used to process the first image and extract key feature information of the target spacecraft surface, including three-dimensional geometric features (such as the contours of edges, corners, protrusions and depressions) and texture features (such as the surface material texture distribution). This feature information serves as the core basis for subsequent three-dimensional reconstruction, ensuring that the reconstruction results closely match the actual shape and surface characteristics of the spacecraft.

[0151] By combining the camera parametric model (including camera intrinsic parameters such as focal length and sensor size, and extrinsic parameters such as pose information when the first image is acquired), the motion recovery structure algorithm is used to perform 3D reconstruction, mapping feature points in the 2D image to 3D space, calculating the 3D coordinates of the feature points, and then generating a target point cloud model of the target spacecraft in its current state.

[0152] Furthermore, the target point cloud model and the target parameterized model are compared and fused to obtain correction information.

[0153] Specifically, a unified three-dimensional coordinate system is established based on the geometric center of the target spacecraft, and the target point cloud model is registered and aligned with the original target parametric model. This involves assessing the consistency of the spacecraft's overall geometry (e.g., deviations in protrusion height and depression depth), the positional matching degree of surface features (e.g., spatial coordinate differences of key components), and the fit between pose information and attitude evolution characteristics (e.g., deviations between the attitude reflected in the current point cloud and the preset attitude of the original model). The differences between the two are quantified through spatial distance calculation and feature point matching.

[0154] A weighted fusion strategy is adopted to integrate the effective information of the two types of models. For 3D geometric features (such as surface details not covered by the initial modeling) that are clearly captured in the target point cloud model but are missing or have large deviations in the original parametric model, the point cloud model data is used as the core basis. After the reconstructed point cloud is registered and aligned with the old point cloud, the old point cloud is replaced and the surface patches are updated before the next round of iterative calculation.

[0155] Furthermore, the set of faces in the target parameterized model is adjusted based on the correction information to obtain the adjusted set of faces.

[0156] Specifically, for the geometric center deviation of the facets clearly stated in the correction information, the coordinate correction amount of each facet is calculated, and the geometric center position of the original facet is updated to the spatial coordinates consistent with the target point cloud model, ensuring that the spatial position of the facet can accurately correspond to the actual surface area of ​​the spacecraft.

[0157] The direction is adjusted based on the normal vector in the correction information, and the normal vector of each patch is recalculated to ensure that the normal vector still points to the outside of the spacecraft; after adjustment, all normal vectors are normalized.

[0158] Furthermore, the adjusted set of patches is integrated to obtain the processed target parameterized model.

[0159] The adjusted and bound patch set is then integrated to form the processed target parameterized model. This model inherits the structured advantages of the original model while incorporating real-time information reflected in the first image.

[0160] This application's embodiments generate a target point cloud model through feature extraction and 3D reconstruction, realizing the conversion from 2D images to 3D information. This provides accurate real-time data support for model updates and solves the problem that traditional fixed models cannot adapt to changes in target state. A comparison and fusion strategy is employed to correct deviations in the original model, supplement missing 3D geometric features, and adjust the pose and quantity of the patch set. This allows the parameterized target model to reflect the actual state of the spacecraft in real time, improving the accuracy of subsequent iterations. The dynamically updated model provides accurate basis for subsequent viewpoint planning and image acquisition, ensuring that the sequence of images continuously adapts to changes in target state, guaranteeing the timeliness and integrity of the sequence of images, and further improving the accuracy of subsequent tasks such as 3D reconstruction.

[0161] While this application provides the method operation steps as described in the embodiments or flowcharts, more or fewer operation steps may be included based on conventional or non-inventive labor. The order of steps listed in this embodiment is merely one possible execution order among many and does not represent the only execution order. In actual device or client product execution, the methods shown in this embodiment or the accompanying drawings can be executed sequentially or in parallel (e.g., in a parallel processor or multi-threaded processing environment).

[0162] like Figure 3 As shown in the illustration, this application also provides a complete sequence image acquisition device 300 for a target spacecraft. The device includes:

[0163] The acquisition module 301 is used to acquire the spatial characteristics of the target spacecraft and the imaging characteristics of the observation camera, perform parameterization processing on the spatial characteristics to obtain the target parameterization model, and perform parameterization processing on the imaging characteristics to obtain the camera parameterization model.

[0164] The processing module 302 is used to optimize the target parameterized model and the camera parameterized model based on the pixel value per unit area to obtain the viewpoint planning optimization target.

[0165] The acquisition module 301 is also used to acquire the first constraint parameters and construct target constraints based on the first constraint parameters and the viewpoint planning optimization objective.

[0166] Module 303 is used to construct an optimization model based on the target parameterization model, camera parameterization model, viewpoint planning optimization target, and target constraints.

[0167] The processing module 302 is also used to perform pose acquisition processing on the optimized mathematical model to obtain the optimal viewpoint pose of the observation camera.

[0168] The processing module 302 is also used to adjust the movement trajectory and attitude of the observation camera based on the optimal viewpoint pose before acquiring images of the target spacecraft to obtain the first image.

[0169] The processing module 302 is also used to update the target parameterized model according to the first image to obtain the processed target parameterized model.

[0170] The processing module 302 is also used to iteratively execute multiple first images, integrate and process the multiple first images, and obtain a complete sequence of images of the target spacecraft.

[0171] In some embodiments, the processing module 302 is further configured to initialize the initial pose of the observation camera and the set of patches in the target parameterization model to obtain initial calculation parameters.

[0172] The processing module 302 is also used to sum the visible faces in the target parameterized model under the current viewpoint based on the initial calculation parameters to obtain the total evaluation value under the current viewpoint.

[0173] The processing module 302 is also used to perform gradient calculation on the observation camera to obtain gradient values.

[0174] The acquisition module 301 is also used to determine the effective step size based on the target constraint.

[0175] The processing module 302 is also used to update the observation camera based on the gradient value and the effective step size to obtain the pose information of the next viewpoint.

[0176] The processing module 302 is also used to determine whether the pose information of the next viewpoint meets the target constraints. If the pose information of the next viewpoint does not meet the target constraints, the effective step size is re-acquired and / or the gradient value is recalculated.

[0177] The processing module 302 is also used to obtain the optimal viewpoint pose of the observation camera, provided that the pose information of the next viewpoint satisfies the target constraints.

[0178] In some embodiments, the processing module 302 is further configured to discretize the three-dimensional geometric features, pose information and attitude evolution characteristics of the target spacecraft to obtain multiple bounded planes.

[0179] The acquisition module 301 is also used to obtain the target pose information of each bounded plane based on the geometric center position and normal vector of each bounded plane.

[0180] The processing module 302 is also used to integrate and process the target pose information, three-dimensional geometric features and attitude evolution characteristics to obtain a set of patches.

[0181] Module 303 is also used to construct a target parameterized model based on a set of patches.

[0182] In some embodiments, the processing module 302 is further configured to calculate the ratio of each patch in the target parameterized model to the projected area on the observation camera, to obtain a pixel value per unit area.

[0183] The processing module 302 is also used to sum the pixel values ​​per unit area of ​​all visible faces in the target parameterization model of the current viewpoint to obtain the total evaluation value.

[0184] The processing module 302 is also used to optimize the total evaluation value based on the target parameterization model and the camera parameterization model to obtain the viewpoint planning optimization target.

[0185] In some embodiments, the acquisition module 301 is further configured to acquire the camera imaging constraint in the first constraint parameters, and construct the depth constraint and observation angle constraint based on the camera imaging constraint and the viewpoint planning optimization target. The depth constraint is used to limit the imaging distance range from the target spacecraft to the observation camera, and the observation angle constraint is used to limit the incident angle of the observation camera.

[0186] The acquisition module 301 is also used to acquire the safety constraints and space lighting environment parameters in the first constraint parameters, and to construct a safe area constraint based on the safety constraints, space lighting environment parameters and viewpoint planning optimization target. The safe area constraint is used to control the observation camera to avoid direct sunlight and / or Earth-Moon reflected light, and to limit the safe distance range between the observation camera and the target spacecraft.

[0187] The acquisition module 301 is also used to acquire the motion state of the on-orbit spacecraft in the first constraint parameters, and to construct a step size constraint based on the motion state. The step size constraint is used to limit the maximum spatial distance between adjacent viewpoints based on the target spacecraft's orbit and speed.

[0188] The processing module 302 is also used to integrate the depth of field constraints, observation angle constraints, safe area constraints and step size constraints to obtain the target constraints.

[0189] In some embodiments, the processing module 302 is further configured to perform feature extraction and three-dimensional reconstruction processing on the first image to obtain a target point cloud model.

[0190] The processing module 302 is also used to compare and fuse the target point cloud model and the target parameterized model to obtain correction information.

[0191] The processing module 302 is also used to adjust the set of faces in the target parameterized model based on the correction information to obtain the adjusted set of faces.

[0192] The processing module 302 is also used to integrate the adjusted patch set to obtain the processed target parameterized model.

[0193] Some modules in the apparatus described in this application can be described in the general context of computer-executable instructions that are executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, classes, etc., that perform a specific task or implement a specific abstract data type. This application can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0194] The apparatus or module described in the above embodiments can be implemented by a computer chip or physical entity, or by a product with a certain function. For ease of description, the above apparatus is described by dividing it into various modules according to their functions. When implementing the embodiments of this application, the functions of each module can be implemented in one or more software and / or hardware. Of course, a module that implements a certain function can also be implemented by combining multiple sub-modules or sub-units.

[0195] The methods, apparatus, or modules described in this application can be implemented in a computer-readable program code manner. The controller can be implemented in any suitable manner, such as a microprocessor or processor and a computer-readable medium storing computer-readable program code (e.g., software or firmware) executable by the (micro)processor, logic gates, switches, application-specific integrated circuits (ASICs), programmable logic controllers, and embedded microcontrollers. Examples of controllers include, but are not limited to, the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20, and Silicon Labs C8051F320. A memory controller can also be implemented as part of the control logic of a memory. Those skilled in the art will also recognize that, in addition to implementing the controller in purely computer-readable program code manner, the same functionality can be achieved by logically programming the method steps to make the controller take the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, and embedded microcontrollers. Therefore, such a controller can be considered a hardware component, and the means included within it for implementing various functions can also be considered as structures within the hardware component. Alternatively, the device used to implement various functions can be viewed as either a software module that implements the method or a structure within a hardware component.

[0196] This application also provides an apparatus, the apparatus comprising: a processor; a memory for storing processor-executable instructions; wherein, when the processor executes the executable instructions, it implements the method described in this application.

[0197] This application also provides a non-volatile computer-readable storage medium storing a computer program or instructions thereon, which, when executed, enables the method described in this application embodiment to be implemented.

[0198] Furthermore, in the various embodiments of the present invention, each functional module can be integrated into a processing module, or each module can exist independently, or two or more modules can be integrated into a single module.

[0199] The aforementioned storage media include, but are not limited to, Random Access Memory (RAM), Read-Only Memory (ROM), Cache, Hard Disk Drive (HDD), or Memory Card. The memory can be used to store computer program instructions.

[0200] As can be seen from the above description of the embodiments, those skilled in the art can clearly understand that this application can be implemented by means of software plus necessary hardware. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product, or it can be embodied in the process of data migration. The computer software product can be stored in a storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, mobile terminal, server, or network device, etc.) to execute the methods described in various embodiments or some parts of the embodiments of this application.

[0201] The various embodiments described in this specification are presented in a progressive manner. Similar or identical parts between embodiments can be referred to interchangeably. Each embodiment focuses on its differences from other embodiments. All or part of this application can be used in numerous general-purpose or special-purpose computer system environments or configurations. Examples include: personal computers, server computers, handheld or portable devices, tablet devices, mobile communication terminals, multiprocessor systems, microprocessor-based systems, programmable electronic devices, network PCs, minicomputers, mainframe computers, and distributed computing environments including any of the above systems or devices, etc.

[0202] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit this application. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of this application.

Claims

1. A method for acquiring complete sequence images of a target spacecraft, characterized in that, include: Step 101: Obtain the space characteristics of the target spacecraft and the imaging characteristics of the observation camera; perform parameterization processing on the space characteristics to obtain the target parameterization model; and perform parameterization processing on the imaging characteristics to obtain the camera parameterization model. Step 102: Optimize the target parameterization model and the camera parameterization model based on the pixel value per unit area to obtain the viewpoint planning optimization target; Step 103: Obtain the first constraint parameters, and construct target constraints based on the first constraint parameters and the viewpoint planning optimization objective; Step 104: Construct an optimization model based on the target parameterization model, the camera parameterization model, the viewpoint planning optimization objective, and the target constraints; Step 105: Perform pose acquisition processing on the optimized model to obtain the optimal viewpoint pose of the observation camera; Step 106: After adjusting the movement trajectory and attitude of the observation camera based on the optimal viewpoint pose, an image of the target spacecraft is acquired to obtain the first image; Step 107: Update the target parameterization model based on the first image to obtain the processed target parameterization model; Step 108: Iteratively execute steps 102 to 107 to obtain multiple first images. Integrate the multiple first images to obtain a complete sequence of images of the target spacecraft. The optimization process based on pixel values ​​per unit area of ​​the target parameterization model and the camera parameterization model yields the viewpoint planning optimization objective, including: Calculate the ratio of each patch in the target parameterization model to the projected area of ​​the observation camera to obtain the pixel value per unit area; The total evaluation value is obtained by summing the pixel values ​​per unit area corresponding to all visible faces in the target parameterization model described below from the current viewpoint. The overall evaluation value is optimized based on the target parameterization model and the camera parameterization model to obtain the viewpoint planning optimization target; The first constraint parameters include the motion state of the spacecraft in orbit, space illumination environment parameters, camera imaging constraints, and safety constraints. The process of obtaining the first constraint parameters and constructing target constraints based on the first constraint parameters and the viewpoint planning optimization objective includes: Obtain the camera imaging constraint from the first constraint parameters, and construct depth constraint and observation angle constraint based on the camera imaging constraint and viewpoint planning optimization target. The depth constraint is used to limit the imaging distance range from the target spacecraft to the observation camera, and the observation angle constraint is used to limit the incident angle of the observation camera. Obtain the safety constraints and space illumination environment parameters from the first constraint parameters. Based on the safety constraints, space illumination environment parameters, and the viewpoint planning optimization target, construct a safe area constraint. The safe area constraint is used to control the observation camera to avoid direct sunlight and / or Earth-Moon reflected light, and to limit the safe distance range between the observation camera and the target spacecraft. Obtain the motion state of the spacecraft in orbit from the first constraint parameters, and construct a step size constraint based on the motion state. The step size constraint is used to limit the maximum spatial distance between adjacent viewpoints based on the orbit and velocity of the target spacecraft. The depth of field constraint, observation angle constraint, safe area constraint, and step size constraint are integrated to obtain the target constraint.

2. The method according to claim 1, characterized in that, The step of performing pose acquisition processing on the optimized model to obtain the optimal viewpoint pose of the observation camera includes: The initial pose of the observation camera and the set of patches in the target parameterization model are initialized to obtain the initial calculation parameters; Based on the initial calculation parameters, the visible faces in the target parameterized model under the current viewpoint are summed to obtain the total evaluation value under the current viewpoint. Gradient calculation is performed on the observation camera to obtain the gradient value; Determine the effective step size based on the aforementioned objective constraints; The observation camera is updated based on the gradient value and the effective step size to obtain the pose information of the next viewpoint. Determine whether the pose information of the next viewpoint satisfies the target constraint. If the pose information of the next viewpoint does not satisfy the target constraint, reacquire the effective step size and / or recalculate the gradient value. Alternatively, the optimal viewpoint pose of the observation camera can be obtained if the pose information of the next viewpoint satisfies the target constraint.

3. The method according to claim 1, characterized in that, The spatial characteristics include the three-dimensional geometric features, pose information, and attitude evolution characteristics of the target spacecraft. The parameterization of these spatial characteristics to obtain a target parameterized model includes: The three-dimensional geometric features, pose information, and attitude evolution characteristics of the target spacecraft are discretized to obtain multiple bounded planes; The target pose information of each bounded plane is obtained based on the geometric center position and normal vector of each bounded plane; The target pose information, three-dimensional geometric features, and pose evolution characteristics are integrated and processed to obtain a set of facets; A target parameterized model is constructed based on the set of patches.

4. The method according to claim 1, characterized in that, The step of updating the target parameterized model based on the first image to obtain the processed target parameterized model includes: The first image is subjected to feature extraction and 3D reconstruction to obtain a target point cloud model; The target point cloud model and the target parameterized model are compared and fused to obtain correction information; Based on the correction information, the set of faces in the target parameterized model is adjusted to obtain the adjusted set of faces; The adjusted patch set is integrated to obtain the processed target parameterized model.

5. A device for acquiring complete sequence images of a target spacecraft, characterized in that, include: The acquisition module is used to acquire the space characteristics of the target spacecraft and the imaging characteristics of the observation camera, perform parameterization processing on the space characteristics to obtain a target parameterization model, and perform parameterization processing on the imaging characteristics to obtain a camera parameterization model. The processing module is used to optimize the target parameterization model and the camera parameterization model based on the pixel value per unit area to obtain the viewpoint planning optimization target; The acquisition module is further configured to acquire the first constraint parameter and construct the target constraint based on the first constraint parameter and the viewpoint planning optimization objective; A construction module is used to construct an optimization model based on the target parameterization model, the camera parameterization model, the viewpoint planning optimization objective, and the target constraints; The processing module is also used to perform pose acquisition processing on the optimization model to obtain the optimal viewpoint pose of the observation camera; The processing module is also used to adjust the movement trajectory and attitude of the observation camera based on the optimal viewpoint pose and then acquire an image of the target spacecraft to obtain a first image; The processing module is further configured to update the target parameterized model based on the first image to obtain the processed target parameterized model. The processing module is also used to iteratively execute multiple first images, integrate the multiple first images, and obtain a complete sequence of images of the target spacecraft. The processing module is further configured to optimize the target parameterized model and the camera parameterized model based on the pixel value per unit area to obtain a viewpoint planning optimization target, wherein: Calculate the ratio of each patch in the target parameterization model to the projected area of ​​the observation camera to obtain the pixel value per unit area; The total evaluation value is obtained by summing the pixel values ​​per unit area corresponding to all visible faces in the target parameterization model described below from the current viewpoint. The overall evaluation value is optimized based on the target parameterization model and the camera parameterization model to obtain the viewpoint planning optimization target; The first constraint parameters include the motion state of the spacecraft in orbit, space illumination environment parameters, camera imaging constraints, and safety constraints. The acquisition module is further configured to acquire the first constraint parameters and construct target constraints based on the first constraint parameters and the viewpoint planning optimization objective, wherein: Obtain the camera imaging constraint from the first constraint parameters, and construct depth constraint and observation angle constraint based on the camera imaging constraint and viewpoint planning optimization target. The depth constraint is used to limit the imaging distance range from the target spacecraft to the observation camera, and the observation angle constraint is used to limit the incident angle of the observation camera. Obtain the safety constraints and space illumination environment parameters from the first constraint parameters. Based on the safety constraints, space illumination environment parameters, and the viewpoint planning optimization target, construct a safe area constraint. The safe area constraint is used to control the observation camera to avoid direct sunlight and / or Earth-Moon reflected light, and to limit the safe distance range between the observation camera and the target spacecraft. Obtain the motion state of the spacecraft in orbit from the first constraint parameters, and construct a step size constraint based on the motion state. The step size constraint is used to limit the maximum spatial distance between adjacent viewpoints based on the orbit and velocity of the target spacecraft. The depth of field constraint, observation angle constraint, safe area constraint, and step size constraint are integrated to obtain the target constraint.

6. A computer device comprising a memory and a processor, the memory storing a computer program executable on the processor, characterized in that, When the processor executes the program, it implements the steps of the method according to any one of claims 1 to 4.

7. 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 as described in any one of claims 1 to 4.

Images