Visual attitude determination method and device for lander

Abstract

The invention provides a lander visual attitude determination method and device, and the method comprises the steps: determining homonymy feature points between adjacent landing images according to the obtained landing images of a lander, and carrying out the calculation to obtain a homography matrix and spatial points corresponding to the homonymy feature points; classifying the spatial points to obtain spatial points and other spatial points in the plane of the landing area; traversing and recombining the space points in the plane of the landing area and other space points to obtain a basic matrix; decomposing the homography matrix and the basic matrix to respectively obtain an initial pose solution and a candidate pose solution of the lander; and obtaining a target pose solution of the lander according to the initial pose solution and the candidate pose solution. According to the lander visual attitude determination method provided by the scheme, the problem that the visual positioning of a lander in a flat terrain is inaccurate and unstable is effectively solved.

Description

Technical Field

[0001] The present invention relates to the field of space image processing technology, and in particular to a lander visual attitude determination method and device. Background Art

[0002] In planetary exploration missions, the probe needs to complete a soft landing on the planetary surface. The powered descent phase is the key to soft landing and is implemented under fully automatic control. The accurate recovery of the attitude of the lander during descent can better assist in understanding the results of autonomous control and provide data support for the analysis of orbital control strategies for subsequent missions. In order to ensure the successful landing of the probe, the pre-selected landing area usually has a flat terrain. In the existing methods of restoring the attitude of the lander based on the landing image in the plane degradation scenario caused by the flat landing terrain, there is still the problem of inaccurate and unstable lander attitude information obtained. Summary of the invention

[0003] The embodiments of the present invention provide a lander visual attitude determination method and device, which effectively solves the problem of inaccurate and unstable visual positioning of the lander on flat terrain.

[0004] In a first aspect, an embodiment of the present invention provides a lander visual attitude determination method, comprising:

[0005] Determine the feature points with the same name between adjacent landing images according to the acquired landing images of the lander, and calculate the homography matrix and the spatial points corresponding to the feature points with the same name;

[0006] Classifying the spatial points to obtain spatial points located inside the landing area plane and other spatial points;

[0007] Traversing and reorganizing the spatial points located inside the landing area plane and the other spatial points to obtain a basic matrix;

[0008] Decomposing the homography matrix and the basic matrix to obtain an initial pose solution and a candidate pose solution of the lander respectively;

[0009] A target posture solution of the lander is obtained according to the initial posture solution and the candidate posture solution.

[0010] Optionally, determining feature points with the same name between adjacent landing images based on the acquired landing images of the lander, and calculating a homography matrix and spatial points corresponding to the feature points with the same name, comprises:

[0011] Extracting features from the landing image to obtain feature points;

[0012] Matching feature points of adjacent landing images with the same name to obtain feature points of adjacent landing images with the same name;

[0013] According to the feature points with the same name, the homography matrix and the camera pose are calculated based on the RANSAC algorithm;

[0014] According to the camera posture, the spatial coordinates of the spatial points corresponding to the feature points with the same name are determined.

[0015] Optionally, the classifying the spatial points to obtain the spatial points located inside the landing area plane and other spatial points includes:

[0016] Determining a landing area plane according to the homography matrix;

[0017] Calculating the distance from each of the spatial points to the landing area plane;

[0018] Determining whether the distance is within a preset distance range;

[0019] If the judgment result is yes, then the spatial point is the spatial point located inside the landing area plane;

[0020] If the judgment result is no, then the spatial point is the other spatial point.

[0021] Optionally, the traversing and reorganizing the spatial points located inside the landing area plane and the other spatial points to obtain a basic matrix includes:

[0022] Acquire a first pixel point set of feature points with the same name in the landing image corresponding to each of the spatial points located inside the landing area plane;

[0023] Obtain a second pixel point set in the landing image of a feature point with the same name corresponding to each of the other spatial points;

[0024] For each of the second pixel point sets, the following steps are performed: combining all of the first pixel point sets with the second pixel point set to obtain the basic matrix.

[0025] Optionally, decomposing the homography matrix and the basic matrix to obtain an initial pose solution and a candidate pose solution of the lander respectively includes:

[0026] Decomposing the homography matrix to obtain a first pose solution;

[0027] Based on the plane ipsilaterality and visibility constraints, the first posture solutions are screened to obtain the initial posture solutions; wherein the number of the first posture solutions is greater than the number of the initial posture solutions;

[0028] Decomposing the basic matrix to obtain a second pose solution;

[0029] Based on the plane ipsilaterality constraint, the second posture solutions are screened to obtain the candidate posture solutions; wherein the number of the second posture solutions is greater than the number of the candidate posture solutions.

[0030] Optionally, obtaining a target pose solution of the lander according to the initial pose solution and the candidate pose solution includes:

[0031] Performing a difference operation on the initial pose solution and the candidate pose solution to obtain a difference value;

[0032] Counting the number of the candidate pose solutions whose difference is less than a preset difference threshold;

[0033] Determining whether the quantity is greater than a preset quantity threshold;

[0034] If the judgment result is yes, a mean operation is performed on the candidate posture solutions whose difference is less than a preset difference threshold to obtain the target posture solution.

[0035] In a second aspect, an embodiment of the present invention further provides a lander visual attitude determination device, comprising:

[0036] A first solving module is used to determine the feature points with the same name between adjacent landing images according to the acquired landing images of the lander, and calculate the homography matrix and the spatial points corresponding to the feature points with the same name;

[0037] A classification module, used for classifying the spatial points to obtain spatial points located inside the landing area plane and other spatial points;

[0038] A second solving module is used for traversing and reorganizing the spatial points located inside the landing area plane and the other spatial points to obtain a basic matrix;

[0039] The pose solving module is used to decompose the homography matrix and the basic matrix to obtain the initial pose solution and the candidate pose solution of the lander respectively; and obtain the target pose solution of the lander according to the initial pose solution and the candidate pose solution.

[0040] In a third aspect, an embodiment of the present invention further provides a computing device, comprising a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the lander visual attitude determination method described in any one of the above items is implemented.

[0041] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed in a computer, the computer is caused to execute any of the lander visual attitude determination methods described above.

[0042] In a fifth aspect, an embodiment of the present invention further provides a computer program product, comprising computer instructions, which, when executed by a processor, implement the steps of the method described in any first aspect of this specification.

[0043] An embodiment of the present invention provides a lander visual attitude determination method and device, which utilizes the same-name feature points between adjacent landing images to calculate a homography matrix, uses the homography matrix as a non-degenerate mapping model, and solves an initial attitude solution; then, the spatial points corresponding to the same-name feature points are reorganized to obtain a basic matrix corresponding to different combinations, the basic matrix is ​​decomposed to obtain a candidate attitude solution, and then the initial attitude solution and the candidate attitude solution are compared to determine the final target attitude solution of the lander. In this way, the present invention can fully utilize the consistency between the homography matrix and the basic matrix in the case of plane degeneration, eliminate the ambiguity of the degenerate mapping model solution through mutual constraints, improve the accuracy of the lander attitude inversion, and increase the overall reliability of the lander attitude inversion system. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0045] Figure 1 is a flow chart of a lander visual attitude determination method provided by one embodiment of the present invention;

[0046] Figure 2 is a hardware architecture diagram of a computing device provided by an embodiment of the present invention;

[0047] Figure 3 It is a structural diagram of a lander visual attitude determination device provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0048] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0049] Currently, there are two methods to solve the attitude information of the lander in a plane-degraded scenario. The first one, the Geometric Robust Information Criterion (GRIC), can distinguish whether to use the fundamental matrix or the homography matrix, but for quasi-plane degradation problems, GRIC cannot correctly handle the degraded data and always tends to only obtain the homography matrix. The second one, proposed by Rebert et al. in 2019, uses the parallax bundle method to calculate the correct fundamental matrix using "plane induced parallax". This method uses the centroid of the polygon in the maximum overlapping area of ​​many parallax bundles as the core point of the image, but does not take into account the situation where all spatial points are located inside the plane, and the method of using the polygon centroid as the core point does not use the geometric prior properties of the homography matrix decomposition result as a constraint, which may result in incorrect results.

[0050] Therefore, in order to improve the accuracy of lander attitude inversion in planar degenerate scenarios, the present invention proposes a pose solution method based on homography matrix constraints for lander pose solution.

[0051] The following is the concept of the present invention: Figure 1 As shown, an embodiment of the present invention provides a lander visual attitude determination method, the method comprising:

[0052] Step 100, determining the feature points with the same name between adjacent landing images based on the acquired landing images of the lander, and calculating the homography matrix and the spatial points corresponding to the feature points with the same name;

[0053] Step 102, classifying the spatial points to obtain the spatial points located inside the landing area plane and other spatial points;

[0054] Step 104, traversing and reorganizing the spatial points located inside the landing area plane and other spatial points to obtain a basic matrix;

[0055] Step 106, decomposing the homography matrix and the basic matrix to obtain the initial pose solution and the candidate pose solution of the lander respectively;

[0056] Step 108, obtaining the target pose solution of the lander according to the initial pose solution and the candidate pose solutions.

[0057] In an embodiment of the present invention, the homography matrix is ​​first calculated using the feature points of the same name between adjacent landing images, and the homography matrix is ​​used as a non-degenerate mapping model to solve the initial pose solution; then, the spatial points corresponding to the feature points of the same name are reorganized to obtain the basic matrix corresponding to different combinations, and the basic matrix is ​​decomposed to obtain the candidate pose solution, and then the initial pose solution and the candidate pose solution are compared to determine the target pose solution of the final lander. In this way, the present invention can make full use of the consistency between the homography matrix and the basic matrix in the case of plane degeneration, eliminate the ambiguity of the degenerate mapping model solution through mutual constraints, improve the accuracy of the lander attitude inversion, and increase the overall reliability of the lander attitude inversion system.

[0058] Described below Figure 1 How the various steps are performed.

[0059] First, for step 100, based on the acquired landing image of the lander, the feature points with the same name between adjacent landing images are determined, and the homography matrix and the spatial points corresponding to the feature points with the same name are calculated, including:

[0060] Extract features from the landing image to obtain feature points;

[0061] Match the feature points of adjacent landing images with the same name to obtain the feature points of adjacent landing images with the same name;

[0062] According to the feature points with the same name, the homography matrix and camera pose are calculated based on the RANSAC algorithm;

[0063] According to the camera pose, the spatial coordinates of the spatial points corresponding to the feature points with the same name are determined.

[0064] It should be noted that multiple frames of landing images of the lander during the landing process are obtained. Each feature point with the same name corresponds to a spatial point.

[0065] In the embodiment of the present invention, feature point extraction algorithm and matching algorithm are used to obtain feature points of the same name in adjacent landing images, and then the RANSAC algorithm is used to calculate the homography matrix reflecting the geometric relationship between the binocular image and the spatial plane as a degenerate mapping model reflecting the posture of the spatial plane. At this time, the plane corresponding to the homography matrix is ​​the landing area plane. At the same time, the spatial coordinates of the spatial points in the space where the feature points of the same name are located can be obtained according to the camera posture solution, so as to further realize the positioning of the feature points of the same name.

[0066] With respect to step 102, the spatial points are classified to obtain the spatial points located inside the landing area plane and other spatial points, including:

[0067] According to the homography matrix, the landing area plane is determined;

[0068] Calculate the distance from each spatial point to the landing zone plane;

[0069] Determine whether the distance is within a preset distance range;

[0070] If the judgment result is yes, then the spatial point is a spatial point located inside the landing area plane;

[0071] If the judgment result is no, then the spatial point is another spatial point.

[0072] Specifically, the plane corresponding to the homography matrix is ​​the landing area plane. Therefore, by calculating the distance from each spatial point to the landing area plane, if the distance is within the preset distance range, it can be determined that the spatial point is within the landing area plane; if the distance is outside the preset distance range, it is determined that the spatial point is outside the landing area plane or is a mismatched point. At this time, the spatial points outside the landing area plane and the mismatched points are both referred to as other spatial points.

[0073] In the embodiment of the present invention, since the landing area is not necessarily very flat and may have undulations, by setting a preset distance range, which is equivalent to setting the thickness of the landing area plane, the spatial points of the undulating positions can be considered within the landing area plane, so that the landing area with certain undulations can be regarded as flat terrain, thereby obtaining more comprehensive information about the landing area, thereby providing richer data for the lander's visual positioning, which is beneficial to improving the accuracy of the lander's visual positioning.

[0074] For step 104, the spatial points located inside the landing area plane and other spatial points are traversed and reorganized to obtain a basic matrix, including:

[0075] Obtain the first pixel point set of the feature points with the same name in the landing image corresponding to each spatial point located inside the landing area plane;

[0076] Obtain the second pixel point set of the feature point with the same name corresponding to each other spatial point in the landing image;

[0077] For each second pixel point set, the following steps are performed: combining all first pixel point sets with the second pixel point set to obtain a basic matrix.

[0078] It should be noted that the number of fundamental matrices is the same as the number of other spatial points.

[0079] Specifically, for example, there are 6 feature points with the same name in the three frames of landing images A, B, and C, which are the Ⅰ, Ⅱ, Ⅲ, Ⅳ, Ⅴ, and Ⅵ feature points with the same name, and the spatial points corresponding to the Ⅰ, Ⅱ, Ⅲ, and Ⅳ feature points with the same name are located inside the landing area plane, and the spatial points corresponding to the Ⅴ and Ⅵ feature points with the same name are other spatial points. Obtain the pixel points of each feature point with the same name in the landing images A, B, and C, then the first pixel point set corresponding to the Ⅰ feature point with the same name in the landing image is the set of pixel points of the Ⅰ feature point with the same name in the landing images A, B, and C, and then the four first pixel point sets of the Ⅰ, Ⅱ, Ⅲ, and Ⅳ feature points with the same name are combined as a pixel point set to be combined with the second pixel point set of the Ⅴ feature point with the same name to obtain a basic matrix a; the pixel point set to be combined is combined with the second pixel point set of the Ⅵ feature point with the same name to obtain a basic matrix b.

[0080] In step 106, the homography matrix and the basic matrix are decomposed to obtain the initial pose solution and the candidate pose solution of the lander, respectively, including:

[0081] Decompose the homography matrix to obtain the first pose solution;

[0082] Based on the plane ipsilaterality and visibility constraints, the first pose solutions are screened to obtain initial pose solutions; wherein the number of the first pose solutions is greater than the number of the initial pose solutions;

[0083] Decompose the basic matrix to obtain the second pose solution;

[0084] Based on the plane homolaterality constraint, the second pose solutions are screened to obtain candidate pose solutions; wherein the number of the second pose solutions is greater than the number of the candidate pose solutions.

[0085] Specifically, by decomposing the homography matrix, a total of 8 sets of first-order pose solutions for position and attitude can be obtained, but not all of these 8 sets of solutions are valid solutions, and some of them do not meet the actual situation. First, the camera positions corresponding to the two landing images can only be located on the same side of the scene plane. This plane same-side constraint can eliminate 4 sets of meaningless solutions; secondly, all scene points can only be located in front of the camera. After this visibility constraint, only 2 sets of solutions are left that meet the actual situation, that is, the initial pose solution is obtained.

[0086] Specifically, by decomposing the basic matrix, four sets of second pose solutions are obtained, and then the only set of pose solutions that meets the plane same side constraint is used as the candidate pose solution of the lander. Therefore, the number of candidate pose solutions is the same as the number of basic matrices.

[0087] For step 108, the target posture solution of the lander is obtained according to the initial posture solution and the candidate posture solution, including:

[0088] Perform a difference operation on the initial pose solution and the candidate pose solution to obtain a difference value;

[0089] Counting the number of candidate pose solutions whose difference is less than a preset difference threshold;

[0090] Determine whether the quantity is greater than a preset quantity threshold;

[0091] If the judgment result is yes, the candidate pose solutions whose difference is less than the preset difference threshold are averaged to obtain the target pose solution.

[0092] Specifically, as described in the previous example, there are 2 initial pose solutions obtained by solving the homography matrix, and the number of candidate pose solutions is the same as the number of basic matrices, which is 2. Each candidate pose solution is subtracted from each initial pose solution. If the differences are all less than the preset difference threshold, then the two candidate pose solutions are considered to be consistent with the initial pose solution, and 2 is greater than the preset number threshold 1, then the two candidate pose solutions are averaged to obtain the target pose solution of the lander.

[0093] In an embodiment of the present invention, the target pose solution of the final lander is determined by comparing the initial pose solution and the candidate pose solutions, so that the consistency between the homography matrix and the basic matrix can be fully utilized in the case of plane degradation, and the ambiguity of the solution of the degenerate mapping model is eliminated through mutual constraints, thereby improving the accuracy of the lander attitude inversion and increasing the overall reliability of the lander attitude inversion system.

[0094] like Figure 2 , Figure 3 As shown, an embodiment of the present invention provides a lander visual attitude determination device. The device embodiment can be implemented by software, or by hardware or a combination of software and hardware. From the hardware level, Figure 2 As shown, it is a hardware architecture diagram of a computing device where a lander visual attitude determination device is provided in an embodiment of the present invention, except Figure 3 In addition to the processor, memory, network interface, and non-volatile memory shown in the figure, the computing device in which the device is located in the embodiment may also generally include other hardware, such as a forwarding chip responsible for processing messages, etc. Taking software implementation as an example, Figure 3 As shown, as a device in a logical sense, the CPU of the computing device in which it is located reads the corresponding computer program in the non-volatile memory into the memory and runs it. This embodiment provides a lander visual attitude determination device, including:

[0095] The first solving module 300 is used to determine the feature points with the same name between adjacent landing images according to the acquired landing images of the lander, and calculate the homography matrix and the spatial points corresponding to the feature points with the same name;

[0096] A classification module 302 is used to classify the spatial points to obtain the spatial points located inside the landing area plane and other spatial points;

[0097] The second solving module 304 is used to traverse and reorganize the spatial points located inside the landing area plane and other spatial points to obtain a basic matrix;

[0098] The pose solving module 306 is used to decompose the homography matrix and the basic matrix to obtain the initial pose solution and the candidate pose solution of the lander respectively; and obtain the target pose solution of the lander according to the initial pose solution and the candidate pose solution.

[0099] In some specific implementations, the first solution module 300 may be used to execute the above step 100 , the classification module 302 may be used to execute the above step 102 , the second solution module 304 may be used to execute the above step 104 , and the posture solution module 306 may be used to execute the above steps 106 and 108 .

[0100] In some specific implementations, the first solution module 300 is further configured to perform the following operations:

[0101] Extract features from the landing image to obtain feature points;

[0102] Match the feature points of adjacent landing images with the same name to obtain the feature points of adjacent landing images with the same name;

[0103] According to the feature points with the same name, the homography matrix and camera pose are calculated based on the RANSAC algorithm;

[0104] According to the camera pose, the spatial coordinates of the spatial points corresponding to the feature points with the same name are determined.

[0105] In some specific implementations, the classification module 302 is further configured to perform the following operations:

[0106] According to the homography matrix, the landing area plane is determined;

[0107] Calculate the distance from each spatial point to the landing zone plane;

[0108] Determine whether the distance is within a preset distance range;

[0109] If the judgment result is yes, then the spatial point is a spatial point located inside the landing area plane;

[0110] If the judgment result is no, then the spatial point is another spatial point.

[0111] In some specific implementations, the second solution module 304 is further configured to perform the following operations:

[0112] Obtain the first pixel point set of the feature points with the same name in the landing image corresponding to each spatial point located inside the landing area plane;

[0113] Obtain the second pixel point set of the feature point with the same name corresponding to each other spatial point in the landing image;

[0114] For each second pixel point set, the following steps are performed: combining all first pixel point sets with the second pixel point set to obtain a basic matrix.

[0115] In some specific implementations, the posture solving module 306 is further configured to perform the following operations:

[0116] Decompose the homography matrix to obtain the first pose solution;

[0117] Based on the plane ipsilaterality and visibility constraints, the first pose solutions are screened to obtain initial pose solutions; wherein the number of the first pose solutions is greater than the number of the initial pose solutions;

[0118] Decompose the basic matrix to obtain the second pose solution;

[0119] Based on the plane homolaterality constraint, the second pose solutions are screened to obtain candidate pose solutions; wherein the number of the second pose solutions is greater than the number of the candidate pose solutions.

[0120] In some specific implementations, the posture solving module 306 is further configured to perform the following operations:

[0121] Perform a difference operation on the initial pose solution and the candidate pose solution to obtain a difference value;

[0122] Counting the number of candidate pose solutions whose difference is less than a preset difference threshold;

[0123] Determine whether the quantity is greater than a preset quantity threshold;

[0124] If the judgment result is yes, the candidate pose solutions whose difference is less than the preset difference threshold are averaged to obtain the target pose solution.

[0125] It is to be understood that the structure illustrated in the embodiment of the present invention does not constitute a specific limitation on a visual attitude determination device for a lander. In other embodiments of the present invention, a visual attitude determination device for a lander may include more or fewer components than those illustrated, or combine certain components, or separate certain components, or arrange the components differently. The illustrated components may be implemented in hardware, software, or a combination of software and hardware.

[0126] The information interaction, execution process and other contents between the modules in the above-mentioned device are based on the same concept as the embodiment of the method of the present invention. For the specific contents, please refer to the description in the embodiment of the method of the present invention, and no further description is given here.

[0127] An embodiment of the present invention further provides a computing device, comprising a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, a lander visual attitude determination method in any embodiment of the present invention is implemented.

[0128] An embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the processor executes a lander visual attitude determination method in any embodiment of the present invention.

[0129] An embodiment of the present application also provides a computer program product, which includes a computer program. A processor of a computer device reads the computer program from a computer-readable storage medium, and the processor executes the computer program, so that the computer device executes a lander visual attitude determination method described in any one of the above embodiments.

[0130] Specifically, a system or device equipped with a storage medium can be provided, on which software program code that implements the functions of any of the above-mentioned embodiments is stored, and a computer (or CPU or MPU) of the system or device can be enabled to read and execute the program code stored in the storage medium.

[0131] In this case, the program code itself read from the storage medium can realize the function of any one of the above-mentioned embodiments, and thus the program code and the storage medium storing the program code constitute a part of the present invention.

[0132] The storage medium embodiments for providing the program code include a floppy disk, a hard disk, a magneto-optical disk, an optical disk (such as CD-ROM, CD-R, CD-RW, DVD-ROM, DVD-RAM, DVD-RW, DVD+RW), a magnetic tape, a non-volatile memory card, and a ROM. Alternatively, the program code can be downloaded from a server computer by a communication network.

[0133] In addition, it should be clear that the functions of any of the above embodiments can be implemented not only by executing the program code read by the computer, but also by enabling an operating system operating on the computer to complete part or all of the actual operations based on instructions from the program code.

[0134] In addition, it can be understood that the program code read from the storage medium is written to a memory provided in an expansion board inserted into the computer or to a memory provided in an expansion module connected to the computer, and then based on the instructions of the program code, a CPU installed on the expansion board or expansion module is enabled to perform part or all of the actual operations, thereby realizing the functions of any of the above-mentioned embodiments.

[0135] It should be noted that, in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the statement "comprise a ..." do not exclude the presence of other identical factors in the process, method, article or device including the elements.

[0136] A person of ordinary skill in the art can understand that all or part of the steps of implementing the above method embodiments can be completed by hardware related to program instructions, and the aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps of the above method embodiments; and the aforementioned storage medium includes: ROM, RAM, magnetic disk or optical disk, etc., various media that can store program codes.

[0137] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A lander visual attitude determination method, characterized in that: include: Determine the feature points with the same name between adjacent landing images according to the acquired landing images of the lander, and calculate the homography matrix and the spatial points corresponding to the feature points with the same name; Classifying the spatial points to obtain spatial points located inside the landing area plane and other spatial points; Traversing and reorganizing the spatial points located inside the landing area plane and the other spatial points to obtain a basic matrix; Decomposing the homography matrix and the basic matrix to obtain an initial pose solution and a candidate pose solution of the lander respectively; A target posture solution of the lander is obtained according to the initial posture solution and the candidate posture solution.

2. The method according to claim 1, characterized in that The step of determining the feature points with the same name between adjacent landing images based on the acquired landing images of the lander, and calculating the homography matrix and the spatial points corresponding to the feature points with the same name includes: Extracting features from the landing image to obtain feature points; Matching feature points of adjacent landing images with the same name to obtain feature points of adjacent landing images with the same name; According to the feature points with the same name, the homography matrix and the camera pose are calculated based on the RANSAC algorithm; According to the camera posture, the spatial coordinates of the spatial points corresponding to the feature points with the same name are determined.

3. The method according to claim 1, characterized in that The classifying of the spatial points to obtain the spatial points and other spatial points located inside the landing area plane includes: Determining a landing area plane according to the homography matrix; Calculating the distance from each of the spatial points to the landing area plane; Determining whether the distance is within a preset distance range; If the judgment result is yes, then the spatial point is the spatial point located inside the landing area plane; If the judgment result is no, then the spatial point is the other spatial point.

4. The method according to claim 1, characterized in that: The step of traversing and reorganizing the spatial points located inside the landing area plane and the other spatial points to obtain a basic matrix includes: Acquire a first pixel point set of feature points with the same name in the landing image corresponding to each of the spatial points located inside the landing area plane; Obtain a second pixel point set in the landing image of a feature point with the same name corresponding to each of the other spatial points; For each of the second pixel point sets, the following steps are performed: combining all of the first pixel point sets with the second pixel point set to obtain the basic matrix.

5. The method according to claim 1, characterized in that Decomposing the homography matrix and the basic matrix to obtain an initial pose solution and a candidate pose solution of the lander respectively includes: Decomposing the homography matrix to obtain a first pose solution; Based on the plane ipsilaterality and visibility constraints, the first posture solutions are screened to obtain the initial posture solutions; wherein the number of the first posture solutions is greater than the number of the initial posture solutions; Decomposing the basic matrix to obtain a second pose solution; Based on the plane ipsilaterality constraint, the second posture solutions are screened to obtain the candidate posture solutions; wherein the number of the second posture solutions is greater than the number of the candidate posture solutions.

6. The method according to any one of claims 1 to 5, characterized in that: The step of obtaining a target posture solution of the lander according to the initial posture solution and the candidate posture solution comprises: Performing a difference operation on the initial pose solution and the candidate pose solution to obtain a difference value; Counting the number of the candidate pose solutions whose difference is less than a preset difference threshold; Determining whether the quantity is greater than a preset quantity threshold; If the judgment result is yes, a mean operation is performed on the candidate posture solutions whose difference is less than a preset difference threshold to obtain the target posture solution.

7. A lander visual attitude determination device, characterized in that: include: A first solving module is used to determine the feature points with the same name between adjacent landing images according to the acquired landing images of the lander, and calculate the homography matrix and the spatial points corresponding to the feature points with the same name; A classification module, used for classifying the spatial points to obtain spatial points located inside the landing area plane and other spatial points; A second solving module is used for traversing and reorganizing the spatial points located inside the landing area plane and the other spatial points to obtain a basic matrix; A posture solving module, used for decomposing the homography matrix and the basic matrix to obtain an initial posture solution and a candidate posture solution of the lander respectively; And according to the initial posture solution and the candidate posture solution, a target posture solution of the lander is obtained.

8. A computing device, comprising a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the method according to any one of claims 1 to 6 is implemented.

9. A computer-readable storage medium having a computer program stored thereon, which, when executed in a computer, causes the computer to execute the method according to any one of claims 1 to 6.

10. A computer program product, characterized in that The method comprises computer instructions, which, when executed by a processor, implement the steps of the method according to any one of claims 1 to 6.

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