Image navigation and image acquisition fusion method and device
The method addresses information loss in spacecraft image transmission by determining the target object's position and segmenting the image to transmit only the target area, ensuring accurate and efficient data reduction.
Patent Information
- Application Number
- CN202411153201.5
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-08-21
- Publication Date
- 2024-12-17
AI Technical Summary
Existing aerospace image transmission methods for spacecraft navigation result in information loss due to limited wireless channel capacity, necessitating lossy compression techniques that degrade image quality.
An image navigation and acquisition fusion method that determines the target object's position in a spacecraft image using feature points, segments the image to exclude non-target areas, and transmits only the target object's image, preserving information while reducing data size.
Effectively reduces data transmission size without losing target object information by segmenting and transmitting only the relevant image areas, enhancing navigation accuracy and efficiency.
Smart Images
Figure CN119152391A_ABST
Abstract
Description
Technical Field
[0001] The present disclosure relates to the field of image processing technology, and in particular to a method and device for fusion of image navigation and image acquisition. Background Art
[0002] Image navigation refers to the process of collecting images used for spacecraft navigation through the spacecraft's image acquisition equipment, and transmitting the images to a ground receiving station so that the ground receiving station can generate instructions for automatic navigation of the spacecraft based on the images.
[0003] In this process, in order to meet the requirements of navigation accuracy and search range, the images used for spacecraft navigation are all large-size, high-resolution and high-sensitivity images. Therefore, the data of the images used for navigation is large, but only a small area contains key target information. The existing aerospace equipment transmits data by wireless transmission. In order to adapt to the high maneuverability and long-distance transmission of drones, the communication capacity of its wireless channel is limited. This makes it impossible to transmit the images used for navigation to the ground receiving station in full. The traditional method is to use lossy methods such as image compression, resolution reduction, frame loss or sensitivity reduction to reduce the image data and then transmit it. This method will cause information loss in the target image received by the ground receiving station. Summary of the invention
[0004] In view of this, the present disclosure proposes an image navigation and image acquisition fusion method and device to solve the problem of information loss in images transmitted from a spacecraft to a ground receiving station in the related art.
[0005] The first aspect of the present disclosure provides a method for fusing image navigation and image acquisition, the method comprising:
[0006] Acquire a first image within a target period, wherein the first image includes images of the target object and other objects;
[0007] extracting a plurality of feature points from the first image;
[0008] Determine a target position of the target object in the first image based on the multiple feature points;
[0009] performing image navigation on the spacecraft through the target position;
[0010] While performing image navigation on the spacecraft through the target position, segmenting the first image based on the target position to obtain a second image containing the target object;
[0011] The second image is sent to a ground receiving station.
[0012] The disclosed embodiment determines the target position of the target object in the first image through multiple feature points, and segments the first image according to the target position to obtain a second image containing the target object, thereby achieving the purpose of effectively reducing the data size of image transmission without losing the image information of the target object.
[0013] In an embodiment of the present disclosure, determining a target position of the target object in the first image based on the multiple feature points includes:
[0014] According to the region information and line information in the first image, the plurality of feature points are divided into a plurality of sets; the feature points in each set correspond to any region or line in the first image;
[0015] For any one of the multiple sets, determining the similarity between the feature points in the set and the feature points of the target object stored in the database;
[0016] A set whose similarity is greater than a preset threshold is taken as a target set, and positions of a plurality of feature points corresponding to the target set in the first image are determined as the target positions.
[0017] The disclosed embodiment divides multiple feature points into multiple sets through area information and line information in the first image, and determines the similarity between the feature points in each set and the feature points of the target object stored in the database, thereby determining the positions of the multiple feature points in the first image corresponding to the target set whose similarity is greater than a preset threshold as the target position, which can quickly and accurately determine the position of the target object in the first image, and helps to improve the efficiency of generating the subsequent second image.
[0018] In an embodiment of the present disclosure, segmenting the first image based on the target position to obtain a second image containing the target object includes:
[0019] Dividing the first image into a first area and a second area based on the target position; the first area is an area containing the target set;
[0020] The first region is segmented from the first image to obtain the second image.
[0021] The disclosed embodiment divides a first image into a first area and a second area by a target position, and segments the first area from the first image to obtain a second image, thereby ensuring that the segmented second image only contains the target object and does not contain other objects, thereby achieving the purpose of effectively reducing the data size of image transmission without losing the image information of the target object.
[0022] In the embodiment of the present disclosure, before extracting a plurality of feature points from the first image, the method further includes:
[0023] Determining a camera matrix and distortion coefficients of the image acquisition device;
[0024] Distortion correction is performed on the first image using a distortion correction function, the camera matrix and the distortion coefficient.
[0025] In an embodiment of the present disclosure, performing image navigation on the spacecraft through the target position includes:
[0026] Calculating the position information and attitude information of the spacecraft based on the target position;
[0027] The spacecraft is controlled to fly toward the target object by using the position information and the attitude information.
[0028] In an embodiment of the present disclosure, calculating the position information and attitude information of the spacecraft based on the target position includes:
[0029] Matching the feature points of the target position in the first image with a target model; the target model refers to a three-dimensional model of the target object at different angles;
[0030] Calculating first posture information and first position information of the target object according to the feature point matching result and the known posture information of the target model;
[0031] determining posture change information based on the first posture information and the second posture information, and determining position change information based on the first position information and the second position information; the second posture information and the second position information refer to the posture information and the position information of the target object in the image received in the previous cycle;
[0032] The attitude information of the spacecraft is determined based on the attitude change information, and the position information of the spacecraft is determined based on the position change information.
[0033] The embodiment of the second aspect of the present disclosure provides an image navigation and image acquisition fusion device, including:
[0034] An image acquisition module, used to acquire a first image within a target period, wherein the first image includes images of the target object and other objects;
[0035] A feature point extraction module, used to extract a plurality of feature points from the first image;
[0036] a target position determination module, configured to determine a target position of the target object in the first image based on the multiple feature points;
[0037] An image navigation module, used for performing image navigation on the spacecraft through the target position;
[0038] An image segmentation module, configured to segment the first image based on the target position to obtain a second image containing the target object while performing image navigation on the spacecraft through the target position;
[0039] An image sending module is used to send the second image to a ground receiving station.
[0040] An embodiment of the third aspect of the present disclosure provides an electronic device, which includes a memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the image navigation and image acquisition fusion method described in the first aspect by executing the computer instructions.
[0041] An embodiment of the fourth aspect of the present disclosure provides a computer-readable storage medium, on which computer instructions are stored, and the computer instructions are used to enable a computer to execute the image navigation and image acquisition fusion method described in the first aspect.
[0042] Additional aspects and advantages of the present disclosure will be given in part in the following description and in part will become apparent from the following description or be learned through practice of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS
[0043] By reading the detailed description of the preferred embodiment below, various other advantages and benefits will become clear to those of ordinary skill in the art. The accompanying drawings are only used for the purpose of illustrating the preferred embodiment and are not considered to be limitations of the present disclosure. In addition, the same reference symbols are used to represent the same components throughout the accompanying drawings.
[0044] In the attached picture:
[0045] Figure 1 A schematic diagram of a flow chart of an image navigation and image acquisition fusion method provided by an embodiment of the present disclosure is shown;
[0046] Figure 2 A schematic diagram of the process of image navigation and key target image acquisition provided by an embodiment of the present disclosure is shown;
[0047] Figure 3 A schematic diagram of the structure of an image navigation and image acquisition fusion method and device provided by an embodiment of the present disclosure is shown;
[0048] Figure 4 A schematic diagram of the structure of an electronic device provided by an embodiment of the present disclosure is shown;
[0049] Figure 5 A schematic diagram of a storage medium provided by an embodiment of the present disclosure is shown. DETAILED DESCRIPTION
[0050] The exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments described herein. On the contrary, these embodiments are provided in order to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.
[0051] It should be noted that, unless otherwise specified, the technical terms or scientific terms used in the present disclosure should have the common meanings understood by those skilled in the art to which the present disclosure belongs.
[0052] The following describes the technical scenarios involved in the embodiments of the present disclosure.
[0053] Traditional key target image acquisition methods are limited by the transmission capacity of the wireless channels of aerospace equipment. They can only reduce the size of the entire image containing the key target and then transmit it down through compression, resolution reduction, frame rate reduction, and resolution reduction, which will inevitably lead to the loss of target image information received on the ground.
[0054] To this end, the disclosed embodiment proposes a method for fusing image navigation and key target image acquisition, which introduces navigation information into the key target image acquisition method, and determines the position of the key target in the front-end image based on the navigation information. The key target image acquisition only processes the area containing the key target in the entire image, and directly discards the image portion that does not contain the key target, thereby effectively reducing the size of data to be transmitted without losing the target image information.
[0055] According to an embodiment of the present disclosure, an embodiment of an image navigation and image acquisition fusion method is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.
[0056] In this embodiment, a method for fusion of image navigation and image acquisition is provided, which can be used for image processing equipment of spacecraft. Figure 1 is a flow chart of the image navigation and image acquisition fusion method according to an embodiment of the present disclosure, such as Figure 1 As shown, the process includes the following steps:
[0057] Step S101, capturing a first image within a target period.
[0058] Specifically, the first image is an image of the target object and other objects. The target period can be understood as the current period or the current time. The first image can be acquired by an image acquisition device of the spacecraft, and the image acquisition device can be selected according to actual conditions, and is not specifically limited here.
[0059] Step S102: extracting a plurality of feature points from the first image.
[0060] Specifically, the feature points extracted from the first image include but are not limited to brightness prominent points, color prominent points and texture features in the image. The feature point extraction method is set according to actual conditions and is not specifically limited here, as long as the purpose of feature point extraction can be achieved.
[0061] In some specific embodiments, before the above step S102, the method further includes steps a1 and a2:
[0062] Step a1, determining the camera matrix and distortion coefficient of the image acquisition device.
[0063] Step a2: performing distortion correction on the first image by using a distortion correction function, the camera matrix and the distortion coefficient.
[0064] In the above steps a1 and a2, first, the image acquisition device needs to be calibrated to obtain the camera parameters of the image acquisition device, namely, the intrinsic parameter matrix and the distortion coefficient. OpenCV can be used for calibration. Then, the calibrated intrinsic parameter matrix and the distortion coefficient are used to correct the first image. During the correction process, the distorted image can be loaded and the cv2.undistort function can be applied for correction.
[0065] Step 103: determine the target position of the target object in the first image based on the multiple feature points.
[0066] Specifically, the target object can be understood as a specific landmark on the surface of Mars, such as a rock, mountain, or crater, that is identified through images by the spacecraft's image acquisition equipment.
[0067] In some specific embodiments, the above step S103 includes steps S1031 to S1033:
[0068] Step S1031: Divide the plurality of feature points into a plurality of sets according to the region information and line information in the first image.
[0069] Specifically, the feature points in each set correspond to any point, area or line in the first image, such as an isolated point, a continuous curve or a continuous area.
[0070] Step S1032: for any one of the multiple sets, determine the similarity between the feature points in the set and the feature points of the target object stored in the database.
[0071] Step S1033: taking a set with a similarity greater than a preset threshold as a target set, and determining positions of a plurality of feature points corresponding to the target set in the first image as the target positions.
[0072] In the above steps S1031 to S1033, the multiple feature points in the multiple sets in the first image can be compared with the feature points of the target object stored in the database for similarity, so as to determine the set corresponding to the target object from the multiple sets according to the similarity comparison results, and the positions of the multiple feature points of the set in the first image are used as the target positions. For example: when the first image contains feature points of the first set, the second set and the third set, the three sets are compared with the feature points of the target object (rock area) stored in the database, wherein the similarity between the feature points of the first set and the feature points of the target object is greater than a preset threshold, then the positions of the multiple feature points of the first set in the first image are determined to be the target positions.
[0073] Step S104: performing image navigation on the spacecraft through the target position.
[0074] In some specific embodiments, the above step S104 includes step S1041-step S1042:
[0075] Step S1041, calculating the position information and attitude information of the spacecraft based on the target position.
[0076] In some specific embodiments, the above step S1041 includes steps a1 to a4:
[0077] Step a1: Match the feature points of the target position in the first image with the target model.
[0078] Specifically, the target model refers to the three-dimensional model of the target object at different angles. In the matching process, it is necessary to first detect multiple feature points of the target position in the first image and multiple feature points of the target model, which can be achieved using the SIFT algorithm; then match the multiple feature points of the target position with the multiple feature points of the target model, which can be matched using a brute force matcher.
[0079] Step a2: Calculate first posture information and first position information of the target object according to the feature point matching result and the known posture information of the target model.
[0080] Specifically, the rotation matrix (i.e., the first posture information) and position (i.e., the first position information) of the target object can be solved by the cv2.solvePnP function in OpenCV. Among them, the cv2.solvePnP function can calculate the posture and position of the target object through the image coordinate points and their corresponding three-dimensional space points. Specifically as follows: First, obtain the image points from the feature point matching results and pair them with the three-dimensional points of the target model; then, use the cv2.solvePnP function in OpenCV to calculate the rotation matrix and translation vector; finally, use the calculated rotation matrix and translation vector to determine the position of the target object in the image.
[0081] Step a3: determining posture change information based on the first posture information and the second posture information, and determining position change information based on the first position information and the second position information.
[0082] Specifically, the second posture information and the second position information refer to the posture information and position information of the target object in the image received in the previous cycle. The previous cycle here refers to the previous cycle of the target cycle, and the length of the cycle can be set according to actual conditions and is not specifically limited here.
[0083] More specifically, the position change information can be calculated in the following way:
[0084] Delta T_{target}=T_{target,t2}-T_{target,t1}
[0085] Among them, Delta T_{target} represents the position change information, T_{target,t2} refers to the position information of the target object in the image received in the previous cycle, and T_{target,t1} refers to the first position information received in the target cycle.
[0086] More specifically, the posture change information can be calculated by multiplying the rotation matrices.
[0087] Step a4: determining the attitude information of the spacecraft based on the attitude change information, and determining the position information of the spacecraft based on the position change information.
[0088] Step S1042: Control the spacecraft to fly toward the target object using the position information and the attitude information.
[0089] Specifically, assuming that the spacecraft moves at a uniform speed between two image captures, the motion trajectory of the spacecraft can be estimated by the target position change Delta T_{target}. The attitude change of the spacecraft can be estimated by the change of the target attitude. Assuming that the attitude change of the spacecraft is consistent with the attitude change of the target, the attitude change matrix of the spacecraft can be inferred. On this basis, the movement instruction of the spacecraft can be generated by the motion trajectory of the spacecraft, the attitude change matrix and the position information of the target object.
[0090] Step S105 , while performing image navigation on the spacecraft through the target position, segmenting the first image based on the target position to obtain a second image containing the target object.
[0091] Specifically, the second image refers to an image containing only the target object, and the first image refers to an image containing the target object and other objects. The other objects here can be understood as objects that do not play a role in image navigation, for example: an image of the surface of Mars, in which specific landmarks on the surface of Mars, such as rocks, mountains or craters, can be used as target objects, and exploration and other work can be carried out through these target objects.
[0092] In some specific embodiments, the above step S105 includes step S1051-step S1052:
[0093] Step S1051: divide the first image into a first area and a second area based on the target position.
[0094] Specifically, the first area can be understood as an area containing multiple feature points within the target set, for example; the area where a specific landmark on the surface of Mars is located in the first image; the second area can be understood as other areas in the first image except the first area.
[0095] Step S1052: segment the first area from the first image to obtain the second image.
[0096] Specifically, the specific implementation method of image segmentation is not specifically limited in the embodiment of the present disclosure. It can be achieved by segmenting the first area from the first image. For example, the first image contains the first area and the second area, and the second image obtained after segmentation only contains the first area.
[0097] Step S106: sending the second image to a ground receiving station.
[0098] Specifically, the second images are sent to a ground receiving station, so that the ground receiving station can perform scientific research such as exploration through these second images that only contain the target objects.
[0099] The disclosed embodiment also provides an image navigation method and a fusion method for key target image acquisition, for example Figure 2 As shown:
[0100] Image navigation includes steps b1 to b6:
[0101] Step b1, image digitization: converting the image into a digital signal (generally completed by an image camera);
[0102] Step b2, preprocessing: such as distortion correction, etc.;
[0103] Step b3, feature extraction: extracting feature points from the image, such as contour information;
[0104] Step b4, image recognition: identifying the target in the image;
[0105] Step b5, position calculation: according to the pre-designed target features, the position and rotation matrix of the target in the image are calculated, and the position and attitude of the spacecraft are calculated according to the positions and rotation matrices calculated twice before and after;
[0106] Step b6, flight control: control the spacecraft to fly toward the target based on the position and attitude of the spacecraft calculated by the position solution.
[0107] The key target image acquisition includes the above steps b1 to b4, and the following steps:
[0108] Step c1, remove the invalid image: remove objects other than the target from the image, and only keep the image of the target area;
[0109] Step c2, image processing;
[0110] Step c3, image downloading; sending the processed image to a ground receiving station so that the ground receiving station performs image navigation based on the second image.
[0111] Corresponding to the implementation of the above image navigation and image acquisition fusion method, the embodiment of the present disclosure also provides an image navigation and image acquisition fusion device for performing the above Figure 1 The image navigation and image acquisition fusion method described in the embodiment. Figure 3 As shown, the image navigation and image acquisition fusion device includes:
[0112] An image acquisition module, used to acquire a first image within a target period, wherein the first image includes images of the target object and other objects;
[0113] A feature point extraction module, used to extract a plurality of feature points from the first image;
[0114] a target position determination module, configured to determine a target position of the target object in the first image based on the multiple feature points;
[0115] An image navigation module, used for performing image navigation on the spacecraft through the target position;
[0116] An image segmentation module, configured to segment the first image based on the target position to obtain a second image containing the target object while performing image navigation on the spacecraft through the target position;
[0117] An image sending module is used to send the second image to a ground receiving station.
[0118] Optionally, the target position determination module is also used to: divide the multiple feature points into multiple sets according to the area information and line information in the first image; the feature points in each set correspond to any area or line in the first image; for any one of the multiple sets, determine the similarity between the feature points in the set and the feature points of the target object stored in the database; take the set with a similarity greater than a preset threshold as the target set, and determine the positions of the multiple feature points corresponding to the target set in the first image as the target positions.
[0119] Optionally, the image segmentation module is further used to: divide the first image into a first area and a second area based on the target position; the first area is a area containing the target set; and segment the first area from the first image to obtain the second image.
[0120] Optionally, the device also includes: an image correction module, used to determine the camera matrix and distortion coefficient of the image acquisition device before extracting multiple feature points from the first image; and perform distortion correction on the first image through a distortion correction function, the camera matrix and the distortion coefficient.
[0121] Optionally, the image navigation module is further used to: calculate the position information and attitude information of the spacecraft based on the target position; and control the spacecraft to fly toward the target object through the position information and the attitude information.
[0122] Optionally, the image navigation module is also used to: match feature points of the target position in the first image with a target model; the target model refers to a three-dimensional model of the target object at different angles; calculate first posture information and first position information of the target object based on feature point matching results and known posture information of the target model; determine posture change information based on the first posture information and second posture information, and determine position change information based on the first position information and second position information; the second posture information and second position information refer to the posture information and position information of the target object in the image received in the previous cycle; determine the posture information of the spacecraft based on the posture change information, and determine the position information of the spacecraft based on the position change information.
[0123] The image navigation and image acquisition fusion device provided in the above-mentioned embodiment of the present disclosure and the image navigation and image acquisition fusion method provided in the embodiment of the present disclosure are based on the same inventive concept and have the same beneficial effects as the methods adopted, run or implemented by the application programs stored therein.
[0124] The present disclosure also provides an electronic device to perform the above-mentioned image navigation and image acquisition fusion method. Figure 4 , which shows a schematic diagram of an electronic device provided by some embodiments of the present disclosure. Figure 4 As shown, the electronic device 4 includes: a processor 400, a memory 401, a bus 402 and a communication interface 403, wherein the processor 400, the communication interface 403 and the memory 401 are connected via the bus 402; the memory 401 stores a computer program that can be run on the processor 400, and when the processor 400 runs the computer program, the image navigation and image acquisition fusion method provided in any of the aforementioned embodiments of the present disclosure is executed.
[0125] The memory 401 may include a high-speed random access memory (RAM), and may also include a non-volatile memory, such as at least one disk memory. The communication connection between the system network element and at least one other network element is realized through at least one communication interface 403 (which may be wired or wireless), and the Internet, wide area network, local area network, metropolitan area network, etc. may be used.
[0126] The bus 402 may be an ISA bus, a PCI bus, or an EISA bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. The memory 401 is used to store programs, and the processor 400 executes the programs after receiving the execution instruction. The image navigation and image acquisition fusion method disclosed in any of the above embodiments may be applied to the processor 400, or implemented by the processor 400.
[0127] The processor 400 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by the hardware integrated logic circuit in the processor 400 or the instruction in the form of software. The above processor 400 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The disclosed methods, steps and logic block diagrams in the embodiments of the present disclosure can be implemented or executed. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc. The steps of the method disclosed in conjunction with the embodiments of the present disclosure can be directly embodied as a hardware decoding processor to be executed, or a combination of hardware and software modules in the decoding processor can be executed. The software module can be located in a mature storage medium in the field such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory 401, and the processor 400 reads the information in the memory 401 and completes the steps of the above method in combination with its hardware.
[0128] The electronic device provided by the embodiment of the present disclosure and the image navigation and image acquisition fusion method provided by the embodiment of the present disclosure are based on the same inventive concept and have the same beneficial effects as the methods adopted, run or implemented by them.
[0129] The present disclosure also provides a computer-readable storage medium corresponding to the image navigation and image acquisition fusion method provided in the above embodiment. Figure 5 The computer-readable storage medium shown is a CD 30 on which a computer program (ie, a program product) is stored. When the computer program is run by a processor, the image navigation and image acquisition fusion method provided in any of the aforementioned embodiments will be executed.
[0130] It should be noted that examples of the computer-readable storage medium may also include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other optical or magnetic storage media, which are not listed here one by one.
[0131] The computer-readable storage medium provided by the above-mentioned embodiments of the present disclosure and the image navigation and image acquisition fusion method provided by the embodiments of the present disclosure are based on the same inventive concept and have the same beneficial effects as the methods adopted, run or implemented by the application programs stored therein.
[0132] It should be noted that:
[0133] In the description provided herein, a large number of specific details are described. However, it is understood that the embodiments of the present disclosure can be practiced without these specific details. In some instances, well-known structures and technologies are not shown in detail so as not to obscure the understanding of this description.
[0134] Similarly, it should be understood that in order to streamline the present disclosure and aid in understanding one or more of the various inventive aspects, in the above description of the exemplary embodiments of the present disclosure, the various features of the present disclosure are sometimes grouped together into a single embodiment, figure, or description thereof. However, the disclosed method should not be interpreted as reflecting the following schematic diagram: the claimed disclosure requires more features than the features explicitly recited in each claim. More specifically, as reflected in the claims below, the inventive aspects lie in less than all the features of the single embodiment disclosed above. Therefore, the claims that follow the specific embodiment are hereby expressly incorporated into the specific embodiment, with each claim itself serving as a separate embodiment of the present disclosure.
[0135] In addition, those skilled in the art will appreciate that, although some embodiments described herein include certain features included in other embodiments but not other features, the combination of features of different embodiments is meant to be within the scope of this disclosure and form different embodiments. For example, in the claims below, any one of the claimed embodiments may be used in any combination.
[0136] The above is only a preferred specific implementation of the present disclosure, but the protection scope of the present disclosure is not limited thereto. Any changes or substitutions that can be easily thought of by any technician familiar with the technical field within the technical scope disclosed in the present disclosure should be included in the protection scope of the present disclosure. Therefore, the protection scope of the present disclosure should be based on the protection scope of the claims.
Claims
1. A method for fusion of image navigation and image acquisition, characterized in that: The method is applied to a spacecraft, and the method comprises: Acquire a first image within a target period, wherein the first image includes images of the target object and other objects; extracting a plurality of feature points from the first image; Determine a target position of the target object in the first image based on the multiple feature points; performing image navigation on the spacecraft through the target position; While performing image navigation on the spacecraft through the target position, segmenting the first image based on the target position to obtain a second image containing the target object; The second image is sent to a ground receiving station.
2. The method according to claim 1, characterized in that Determining a target position of the target object in the first image based on the multiple feature points includes: According to the region information and line information in the first image, the plurality of feature points are divided into a plurality of sets; the feature points in each set correspond to any region or line in the first image; For any one of the multiple sets, determining the similarity between the feature points in the set and the feature points of the target object stored in the database; A set whose similarity is greater than a preset threshold is taken as a target set, and positions of a plurality of feature points corresponding to the target set in the first image are determined as the target positions.
3. The method according to claim 2, characterized in that Segmenting the first image based on the target position to obtain a second image containing the target object includes: Dividing the first image into a first area and a second area based on the target position; the first area is an area containing the target set; The first region is segmented from the first image to obtain the second image.
4. The method according to claim 1 or 2, characterized in that: Before extracting a plurality of feature points from the first image, the method further includes: Determining a camera matrix and distortion coefficients of the image acquisition device; Distortion correction is performed on the first image using a distortion correction function, the camera matrix and the distortion coefficient.
5. The method according to claim 1 or 2, characterized in that: Performing image navigation on the spacecraft through the target position includes: Calculating the position information and attitude information of the spacecraft based on the target position; The spacecraft is controlled to fly toward the target object by using the position information and the attitude information.
6. The method according to claim 5, characterized in that Calculating the position information and attitude information of the spacecraft based on the target position includes: Matching the feature points of the target position in the first image with a target model; the target model refers to a three-dimensional model of the target object at different angles; Calculating first posture information and first position information of the target object according to the feature point matching result and the known posture information of the target model; determining posture change information based on the first posture information and the second posture information, and determining position change information based on the first position information and the second position information; the second posture information and the second position information refer to the posture information and the position information of the target object in the image received in the previous cycle; The attitude information of the spacecraft is determined based on the attitude change information, and the position information of the spacecraft is determined based on the position change information.
7. An image navigation and image acquisition fusion device, characterized in that: The device comprises: An image acquisition module, used to acquire a first image within a target period, wherein the first image includes images of the target object and other objects; A feature point extraction module, used to extract a plurality of feature points from the first image; a target position determination module, configured to determine a target position of the target object in the first image based on the multiple feature points; An image navigation module, used for performing image navigation on the spacecraft through the target position; An image segmentation module, configured to segment the first image based on the target position to obtain a second image containing the target object while performing image navigation on the spacecraft through the target position; An image sending module is used to send the second image to a ground receiving station.
8. A computer device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the method according to any one of claims 1 to 6 by executing the computer instructions.
9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute the method according to any one of claims 1 to 6.