Image measurement error correction device and method under airborne vibration condition

Abstract

On one hand, the invention relates to an image measurement error correction method under an airborne vibration condition, and the method comprises the steps: calibrating an internal parameter matrix and an external parameter matrix of a measurement camera based on a coding point associated with the measurement camera in a static state; correcting a posture change amount of each measurement camera relative to the stationary state at a vibration moment based on photographing a coding point associated with the measurement camera by using a correction camera arranged at any position behind the measurement camera; and correcting a pose change matrix between the measurement cameras based on the pose change amount of each measurement camera at the vibration moment relative to the static state. Other aspects of the disclosure relate to a corresponding error correction system.

Description

Technical Field

[0001] The present application generally relates to airborne image measurement error correction in the field of civil aircraft flight testing, and specifically to a measurement error correction system suitable for ensuring measurement accuracy in an airborne vibration environment. The system can correct measurement errors introduced by relative movement within a binocular vision measurement system due to vibration. Background Art

[0002] The deformation and displacement of key aircraft components during flight testing, such as wing deformation, are crucial for flight testing. To achieve drag reduction, it's necessary to measure the actual shape and clearance changes between the fixed and movable wings during flight compared to when the aircraft is stationary on the ground. This is known as wing deformation, which can be used to identify unexpected deformation patterns and potential areas for drag reduction.

[0003] The measurement accuracy of vision measurement systems is easily affected by the onboard vibration environment. For example, in a binocular vision measurement system, small vibrations can cause changes in the extrinsic parameters between calibrated cameras, including but not limited to changes in the relative angle between the cameras, thus affecting measurement accuracy.

[0004] In binocular vision measurement systems, airborne vibrations can cause changes in the relative angles between the surrounding cameras. This can lead to relative changes within the binocular vision measurement system, i.e., changes in calibration parameters, which can introduce measurement errors. To correct for these vibration-induced errors, there is a need in the art for improved devices and methods for correcting image measurement errors under airborne vibration conditions. Summary of the Invention

[0005] One aspect of the present disclosure relates to a method for correcting image measurement errors under airborne vibration conditions, comprising: calibrating, in a stationary state, an intrinsic parameter matrix and an extrinsic parameter matrix of a measurement camera based on code points associated with the measurement camera; correcting a pose change of each measurement camera relative to the stationary state at the moment of vibration by photographing the code points associated with the measurement camera using a correction camera arranged at an arbitrary position behind the measurement camera; and correcting a pose change matrix between measurement cameras based on the pose change of each measurement camera relative to the stationary state at the moment of vibration.

[0006] According to some exemplary embodiments, the image measurement error correction method further includes using the change in the three-axis attitude angle between the inertial navigation systems rigidly connected to each measurement camera relative to the static state at the vibration moment to correct the rotation matrix between the measurement cameras; and optimizing the corrected position change matrix between the measurement cameras based on the corrected rotation matrix between the measurement cameras.

[0007] According to some exemplary embodiments, at least one or more of the following is true: the measurement camera includes at least two measurement cameras; each measurement camera is associated with greater than or equal to four encoding points; and the encoding points are rigidly connected to the associated test camera.

[0008] According to some exemplary embodiments, correcting the position change of each measuring camera relative to the static state at the vibration moment based on photographing the code points associated with the measuring camera using a correction camera arranged at an arbitrary position behind the measuring camera includes: photographing the code points associated with each measuring camera in the static state using the correction camera; determining the rotation and translation matrix of the correction camera in the static state in the world coordinate system based on photographing the code points associated with each measuring camera in the static state; photographing the code points associated with each measuring camera using the correction camera at the vibration moment; determining the rotation and translation matrix of the correction camera in the world coordinate system at the vibration moment based on photographing the code points associated with each measuring camera at the vibration moment; and determining the position change of each measuring camera based on the rotation and translation matrices of the correction camera in the world coordinate system at the static state and at the vibration moment.

[0009] According to some exemplary embodiments, optimizing the corrected measurement camera inter-pose change matrix based on the corrected measurement camera inter-rotation matrix includes: using Kalman filtering to predict and update the corrected measurement camera inter-rotation matrix to obtain a filtered measurement camera inter-rotation matrix; and using the filtered measurement camera inter-rotation matrix to optimize the corrected measurement camera inter-pose change matrix.

[0010] According to some exemplary embodiments, the image measurement error correction method further includes using a synchronous trigger timing device to synchronously trigger the measurement camera, the inertial navigation system, and the correction camera at the static state and the vibration moment, respectively.

[0011] Another aspect of the present disclosure relates to an error correction system for correcting image measurement errors under airborne vibration conditions, comprising at least two measurement cameras; code points associated with each of the at least two test cameras; and at least one correction camera arranged at any position behind the at least two measurement cameras, configured to capture the code points associated with the measurement cameras to correct the pose change of each measurement camera relative to a stationary state at the moment of vibration, wherein a pose change matrix between measurement cameras is corrected based on the pose change of each measurement camera relative to the stationary state at the moment of vibration.

[0012] According to some exemplary embodiments, the error correction system further includes at least two inertial guides rigidly connected to each measurement camera of the at least two test cameras; wherein the change in the three-axis attitude angle between the inertial guides rigidly connected to each measurement camera relative to the static state at the moment of vibration is used to correct the rotation matrix between the measurement cameras; and the corrected measurement camera pose change matrix is optimized based on the corrected measurement camera rotation matrix.

[0013] According to some exemplary embodiments, the error correction system further includes a synchronous trigger timing device for synchronously triggering the measurement camera, the inertial navigation system, and the correction camera at the static state and the vibration moment, respectively.

[0014] According to some exemplary embodiments, the optimization of the modified inter-measurement-camera pose change matrix based on the modified inter-measurement-camera rotation matrix is based on Kalman filtering.

[0015] Other aspects of the present disclosure also include corresponding devices, equipment, computer-readable media, etc. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 A system arrangement and a principle block diagram of an error correction system according to an exemplary aspect of the present disclosure are shown.

[0017] Figure 2 A flowchart illustrating an image processing method of an error correction system according to an exemplary aspect of the present disclosure is shown.

[0018] Figure 3 A block diagram of a data processing apparatus for an error correction system according to an exemplary aspect of the present disclosure is shown.

[0019] Figure 4 A data processing flow chart showing the image processing algorithm of the error correction system. DETAILED DESCRIPTION

[0020] According to some exemplary embodiments, the method for measuring aircraft wing deformation in the field of civil aircraft flight testing is mainly based on image vision. That is, multiple sets of binocular vision measurement systems are used to capture wing images. By post-processing the images, the deformation of the aircraft wing can be measured. This non-contact method does not affect the aerodynamic shape of the wing, poses no safety risks, and is suitable for dynamic measurement. However, the measurement accuracy of the binocular vision measurement system is easily affected by the onboard vibration environment. Small vibrations can cause changes in the external parameters between the calibrated cameras, thereby affecting the measurement accuracy, especially the relative angle between the two cameras. Small changes can lead to a significant decrease in accuracy. Studies have shown that for every 1° change in the attitude transformation between the two cameras, the displacement measurement difference caused can reach 100mm.

[0021] According to some exemplary embodiments, a method for correcting errors in a binocular vision measurement system used in a vibrating environment typically involves adding an observation camera to the crossbar of the binocular vision system (two cameras) to observe a fixed point. The position of the observation camera is calculated from the fixed point's position, thereby correcting for the overall vibration displacement of the binocular vision system. However, this method requires that the observation camera and the measurement camera be mounted on the same crossbar and assumes an absolutely rigid connection between the three cameras, with no relative displacement during measurement. In other words, the internal and external parameters of the cameras remain unchanged, resulting in only a rough correction of the overall displacement of the binocular measurement system.

[0022] According to some exemplary embodiments, helicopter rotor vibration error correction also employs an observation camera to calculate the amplitude of code points on the rotor, thereby correcting the error caused by vibration by correcting the overall vibration position. This method requires the camera to be placed on a stable, vibration-free platform to correct the overall vibration displacement of the target.

[0023] In order to correct the measurement errors introduced by the relative changes between the internal cameras of a binocular measurement system due to the airborne vibration environment, that is, the changes in calibration parameters, this paper designs an image measurement error correction device and method that can be used in airborne vibration environments to solve the problem of inaccurate measurements caused by the relative position movement between the cameras in the binocular vision measurement system.

[0024] Figure 1 The system layout and principle block diagram of an error correction system 100 according to an exemplary aspect of the present disclosure are shown. According to exemplary embodiments of the present disclosure, the error correction system 100 may include measurement camera #1 and measurement camera #2, as well as inertial navigation systems #1 and #2 corresponding to measurement camera #1 and measurement camera #2, respectively. According to some exemplary embodiments, in the error correction system 100, inertial navigation system #1 and measurement camera #1, and inertial navigation system #2 and measurement camera #2, respectively, may be rigidly connected via rigid structures, such that the real-time three-axis attitude change between the two inertial navigation system measurements represents the real-time attitude change between the two measurement cameras. According to some exemplary embodiments, measurement camera #1 and measurement camera #2 may be mounted on a beam, for example, at either end of the beam.

[0025] In the error correction system 100 according to the exemplary embodiment of the present disclosure, each measurement camera may be equipped with corresponding code points. The code points may generally include easily identifiable coded markers. By detecting and matching identical code points across multiple images, the difficulty of camera calibration can be effectively reduced, improving the speed and accuracy of surface 3D reconstruction. Commonly used marker points include a variety of types, such as dot-shaped code points, circular code points, checkerboard code points, scale code points (e.g., including cross-shaped code points), and various combinations of code points. Code points may contain coded information, and by decoding the code points, the corresponding coded information can be obtained for camera calibration.

[0026] According to an exemplary embodiment of the present disclosure, the number of encoding points on each measuring camera is preferably ≥4, and the encoding points and the measuring camera are preferably rigidly connected via a rigid bracket.

[0027] According to an exemplary embodiment of the present disclosure, the error correction system 100 may further include a correction camera. This correction camera may be installed on the aircraft at any location behind the measurement camera. According to an exemplary embodiment, the resection principle is used to determine the pose matrix of the correction camera in the world coordinate system, thereby further determining the pose change of the measurement camera.

[0028] According to an exemplary embodiment of the present disclosure, the error correction system 100 may further include a synchronous triggering and timing device for synchronously triggering and uniformly timing the entire system so as to align time and obtain multiple data at the same moment (for example, the initial still moment and the vibration moment, etc.) for data fusion.

[0029] Specifically, according to an exemplary embodiment of the present disclosure, the correction camera of the error correction system 100 may respectively capture images of the code points associated with each measurement camera in a vibrating state.

[0030] According to an exemplary embodiment of the present disclosure, a rotation and translation matrix of a correction camera coordinate system in a world coordinate system in a vibrating state may be determined accordingly based on an image of code points captured by a correction camera in a vibrating state.

[0031] According to an exemplary embodiment of the present disclosure, the pose change amount of each measurement camera when vibrating compared to an initial static state may be determined, and the pose change matrix between the measurement cameras may be corrected based on the change amount.

[0032] On the other hand, according to an exemplary embodiment of the present disclosure, the change in the three-axis attitude angle of the inertial navigation associated with each measuring camera relative to the initial static state during vibration is determined, and based on this, the change in the rotation matrix between the measuring cameras is determined, and the rotation matrix between the measuring cameras is corrected accordingly.

[0033] According to an exemplary embodiment of the present disclosure, the corrected rotation matrix between the measurement cameras may be predicted and updated (for example, using a Kalman filter) to obtain an optimal value of the current rotation matrix.

[0034] According to an exemplary embodiment of the present disclosure, based on the optimal value of the current rotation matrix and the corrected pose change matrix between the measurement cameras, an optimized pose transformation matrix between the measurement cameras is obtained, thereby obtaining an optimized corrected rotation and translation matrix for each measurement camera.

[0035] According to an exemplary embodiment of the present disclosure, the binocular vision measurement result, that is, the corrected parameter measurement result, can be output by using the optimized and corrected rotation and translation matrix.

[0036] although Figure 1 A binocular vision measurement system in combination with two measurement cameras is described in the specification. However, those skilled in the art will appreciate that the technical solutions and concepts of the present disclosure are not limited to binocular vision measurement systems and two measurement cameras, but can be appropriately modified to be applicable to other vision measurement systems and more or fewer measurement cameras.

[0037] Figure 2 A flowchart of an image processing method 200 of an error correction system according to an exemplary aspect of the present disclosure is shown.

[0038] At block 202, according to an exemplary embodiment, the image processing method 200 of the error correction system may include calibrating an intrinsic parameter matrix and an extrinsic parameter matrix of a measurement camera when the system is in a static state. The calibration of the intrinsic parameter matrix and the extrinsic parameter matrix of the measurement camera may be performed using conventional calibration methods, for example.

[0039] At block 204 , according to an exemplary embodiment, the image processing method 200 of the error correction system may include capturing, by the correction camera, code points (e.g., ≥4) of each measurement camera to determine two-dimensional image coordinates of corresponding code points of the corresponding measurement camera at an initial static state and at a vibration moment, respectively.

[0040] At block 206 , according to an exemplary embodiment, the image processing method 200 of the error correction system may include correcting a rotation and translation matrix of a camera coordinate system of the measurement camera based on an intrinsic parameter matrix and two-dimensional image coordinates of the measurement camera.

[0041] At block 208 , according to an exemplary embodiment, the image processing method 200 of the error correction system may include correcting a pose transformation matrix between the measurement cameras relative to an initial static state at the moment of vibration based on the corrected rotation and translation matrices of the measurement cameras.

[0042] At block 210 , according to an exemplary embodiment, the image processing method 200 of the error correction system may include determining a change in three-axis attitude angles between inertial navigation measurements at the moment of vibration relative to an initial rest state.

[0043] At block 212 , according to an exemplary embodiment, the image processing method 200 of the error correction system may include correcting a rotation matrix between measurement cameras based on the three-axis pose angle.

[0044] At block 214 , according to an exemplary embodiment, the image processing method 200 of the error correction system may include optimizing a rotation matrix between measurement cameras based on a Kalman filter.

[0045] At block 216 , according to an exemplary embodiment, the image processing method 200 of the error correction system may include performing an optimization based on the inter-camera pose transformation matrix.

[0046] Figure 3 FIG. 3 is a block diagram of a data processing apparatus 300 for an error correction system according to an exemplary aspect of the present disclosure. The data processing apparatus 300 may be used to process data from, for example, the above combination. Figure 1 The error correction system 100 described herein obtains various data and performs error correction. According to some exemplary embodiments, the data processing device 300 may be included in Figure 1 According to some other exemplary embodiments, the data processing device 300 may not be included in the error correction system 100 (not shown). Figure 1 In the error correction system 100, Figure 1 The data collected by the error correction system 100 is provided or transmitted to the data processing device 300 for processing and result output. In other words, the data processing device 300 can be Figure 1 It may be a part of the error correction system 100 (not shown), or it may be an independent data processing device.

[0047] According to an exemplary embodiment, the data processing device 300 for the error correction system can be a dedicated data processing device or implemented by, for example, a general-purpose computer or data processor. In the case of being implemented by a general-purpose computer or data processor, the various modules in the data processing device 300 can be implemented by software and / or firmware.

[0048] According to an exemplary embodiment, the data processing apparatus 300 for the error correction system may implement the above-mentioned combination, for example. Figure 2 The image processing method 200 of the error correction system is described.

[0049] According to an exemplary embodiment, the data processing device 300 for the error correction system may include, but is not limited to, an inertial navigation correction module 302 and an image correction module 304 .

[0050] According to an exemplary embodiment, the inertial navigation correction module 302 can be used to measure the relative three-axis attitude angle change of two cameras in the binocular vision measurement system. Figure 2 When describing the image processing method 200 of the error correction system, the inertial navigation correction module 302 can be used to measure the three-axis attitude angle required in step 210, including the three-axis attitude angle between the inertial navigation in the initial static state and the three-axis attitude angle at the vibration moment, and their changes, etc.

[0051] According to an exemplary embodiment, the image correction module 304 can obtain the relative posture and position changes of the two cameras in the binocular vision measurement system through an image processing algorithm, obtain the optimal value of the rotation matrix calculated based on the inertial navigation measurement attitude angle and the rotation matrix calculated based on the attitude angle measured by the image correction module through a Kalman filter algorithm, thereby improving the measurement accuracy of the rotation matrix, and then obtain the relative position change relationship of the two measurement cameras through an image processing algorithm, thereby correcting the external parameters of the binocular measurement system to improve the measurement accuracy of the binocular vision measurement system. For example, when the above combination is implemented Figure 2 When describing the image processing method 200 of the error correction system, the image correction module 304 may implement some or all of the functions of one or more of blocks 204 - 216 .

[0052] Due to the above combination Figure 1 The error correction system 100 described uses a synchronous trigger timing device to synchronize the triggering and unified timing of the entire system so as to align the time and obtain multiple data at the same time (for example, the initial static time and the vibration time, etc.) for data fusion, thereby ensuring Figure 3 The data processing device 300 for the error correction system can acquire multiple data at the same time (ie, time-aligned) for data processing and data fusion.

[0053] According to some exemplary embodiments, it is assumed that the moment when the aircraft is stationary is the initial time 0, which can be used as the reference state for the entire measurement process (i.e., the initial stationary state). At this time, the intrinsic parameter matrix K and extrinsic parameter matrix K of the two measurement cameras should be obtained by calibration.

[0054] At the initial time 0, the coordinates of the coded points #P1-#P4 on the measuring camera #1 in the world coordinate system {Ow1} can be known through mapping. And measure the coordinates of the coded points #P5-#P8 on camera #2 in the world coordinate system {Ow2}

[0055] The correction camera captures the image of the code points #P1-#P4 on the measurement camera #1 and identifies the two-dimensional image coordinates of the code points. The two-dimensional coordinates are

[0056] Combined with the corrected camera intrinsic parameters K obtained by the correction camera calibration, the rotation and translation matrix [R l0 t l0 ], the formula is as follows:

[0057] p nl =K[R l0 t l0 ]P nl

[0058] And obtain the rotation and translation matrix [R r0 t r0 ], the formula is as follows:

[0059] p nr =K[R r0 t r0 ]P nr .

[0060] When the aircraft vibrates, in order to correct the pose transformation matrix between the two measurement cameras, it is necessary to solve the pose change of each measurement camera at that moment and the initial static state. Assume that the pose change of measurement camera #1 at that moment and the initial static state is [ΔR′ l1 Δt′ l1 ], the change in the position of the measuring camera #2 at this moment and the initial static state is [ΔR′ r1 Δt′ r1 ]. Since the position change of the coding point group before and after vibration is equal to the pose change of the measurement camera before and after vibration, the pose transformation matrix between the measurement cameras after correction is:

[0061]

[0062] Among them, [ΔR′ l1 Δt′ l1 ] and [ΔR′ r1 Δt′ r1 ] is solved by the following formula:

[0063] [ΔR′ l1 Δ′ t1 ]=[R l0 t l0 ][R l1 t l1 ] -1

[0064] [ΔR′ r1 Δt′ r1 ]=[R r0t r0 ][R r1 t r1 ] -1

[0065] [R l1 t l1 ] and [R r1 t r1 The solution method of ] is the same as that of the static initial state, which is to construct a set of equations based on the two-dimensional coordinates of the coding points in the image of the target taken by the analytical correction camera and the three-dimensional coordinates of the coding points in the world coordinate system.

[0066] In the system, inertial navigation system #1 and measurement camera #1, as well as inertial navigation system #2 and measurement camera #2, are rigidly connected via rigid structures. The real-time three-axis attitude change between the two inertial navigation systems represents the real-time attitude change between the two measurement cameras. The change in the three-axis attitude angle measured by the inertial navigation system relative to the initial state at the moment of vibration is (Δα1, Δβ1, Δλ1). Therefore, the change in the rotation matrix between the measurement cameras is:

[0067]

[0068] If the initial rotation matrix of the two cameras is Then the corrected rotation matrix is

[0069]

[0070] The rotation matrix between the corrected measurement camera coordinate systems is predicted and updated through Kalman filtering to obtain the optimal value of the current rotation matrix.

[0071] Based on this, the optimized and corrected inter-camera pose transformation matrix can be obtained.

[0072] Finally, based on the optimized and corrected inter-camera pose transformation matrix, the optimized and corrected binocular vision measurement results can be output.

[0073] Figure 4 FIG. 4 is a data processing flow chart of an image processing algorithm 400 of an error correction system. According to some exemplary embodiments, Figure 4 In the image processing algorithm 400 of the error correction system, in a static state, the intrinsic parameter matrices and extrinsic parameter matrices of the two measurement cameras are calibrated (block 402). The calibration of the intrinsic parameter matrices and extrinsic parameter matrices of the measurement cameras can be completed using conventional calibration methods, for example.

[0074] According to an exemplary embodiment, the three-dimensional coordinates of the code points of each measurement camera #1 and measurement camera #2 in the world coordinate system are determined. This can be performed using a surveying method. According to some embodiments, the three-dimensional coordinates of the code points of each measurement camera in the world coordinate system can be measured using a total station or other surveying methods.

[0075] For example, for the code points #P1-#P4 of the measurement camera #1, determine their coordinates in the world coordinate system {Ow1} (Block 404-1) On the other hand, for the code points #P5-#P8 of the measurement camera #2, determine their coordinates in the world coordinate system {Ow2} (Block 404-2).

[0076] According to an exemplary embodiment, the correction camera captures images of the code points #P1-#P4 on the measurement camera #1 in the initial static state and at the vibration moment, and identifies the two-dimensional image coordinates of the code points. The two-dimensional coordinates are (Block 406-1).

[0077] According to an exemplary embodiment, the correction camera captures images of the code points #P1-#P4 on the measurement camera #1 in the initial static state and at the vibration moment, and identifies the two-dimensional image coordinates of the code points. The two-dimensional coordinates are (Block 406-2).

[0078] According to an exemplary embodiment, the rotation and translation matrices [R l0 t l0 ] and [R r0 t r0 ] and the rotation and translation matrices [R l1 t l1 ] and [R r1 t r1 ] (Blocks 408-1 and 408-2). Solving the above rotation and translation matrix can be achieved, for example, by analyzing a set of equations constructed by the two-dimensional coordinates of the code points in the image of the target captured by the correction camera and the three-dimensional coordinates of the code points in the world coordinate system.

[0079] According to an exemplary embodiment, based on the rotation and translation matrices [R l0 t l0 ] and [R r0 t r0], and the rotation and translation matrices [R l1 t l1 ] and [R r1 t r1 ], to determine the position change [ΔR′] of each measurement camera at the moment of vibration compared to the initial state of rest l1 Δt′ l1 ] and [ΔR′ r1 Δt′ r1 ](boxes 410-1 and 410-2).

[0080] According to an exemplary embodiment, the position change amount of the coding point group before and after vibration (ie, the vibration moment relative to the initial static state) is equal to the position change amount [ΔR′] before and after the measurement camera is vibrated. l1 Δt′ l1 ], [ΔR′ r1 Δt′ r1 ] to correct the pose transformation matrix between the measurement cameras (box 412).

[0081] On the other hand, according to an exemplary embodiment, the change in the three-axis attitude angle between the inertial navigation measurements at the time of vibration relative to the initial static state is determined (block 420). Figure 1 In the error correction system 100 described above, a rigid connection is achieved between the inertial navigation system #1 and the measurement camera #1, and between the inertial navigation system #2 and the measurement camera #2 through a rigid structure. Therefore, the real-time three-axis attitude data between the inertial navigation measurements represents the real-time attitude between the measurement cameras.

[0082] According to an exemplary embodiment, based on the change in the three-axis attitude angle between the inertial navigation measurements at the vibration moment relative to the initial static state, the change in the rotation matrix between the measurement cameras is determined (block 422 ).

[0083] According to an exemplary embodiment, the rotation matrix between the measurement cameras is modified based on the amount of change in the rotation matrix between the measurement cameras (block 424 ).

[0084] According to an exemplary embodiment, the rotation matrix between the corrected measurement camera coordinate systems is predicted and updated by Kalman filtering to obtain an optimal value of the current rotation matrix (block 414 ).

[0085] According to an exemplary embodiment, an optimized revised measurement inter-camera pose transformation matrix is obtained based on the revised measurement inter-camera pose transformation matrix and the optimized measurement inter-camera rotation matrix (block 416 ).

[0086] According to an exemplary embodiment, finally, an optimized and corrected binocular vision measurement result is output based on the optimized and corrected inter-camera pose transformation matrix (block 418 ).

[0087] The advantages of the present invention mainly include:

[0088] The system designed by the present invention can complete the synchronous acquisition of measurement images, correction images and inertial navigation data;

[0089] The present invention improves the accuracy of correction data by fusing inertial navigation data and image data;

[0090] The system and method designed by the present invention solve the problem of reduced measurement accuracy of the binocular vision measurement system caused by airborne vibration by correcting the calibration parameters of the binocular vision measurement system.

[0091] The present disclosure provides a system, device, and method that can correct measurement errors introduced by relative changes within a binocular measurement system due to an airborne vibration environment, namely, changes in calibration parameters. By combining inertial navigation and image data, the system uses Kalman filtering to improve the accuracy of rotation matrix parameters for measurement results obtained by two independent measurement methods. By installing rigidly connected encoding points on the camera, the relative position change of the encoding points after vibration is solved instead of the position change of the measurement camera after vibration, thereby correcting the relative position relationship between the measurement cameras. By installing a correction camera at any position on the aircraft, the principle of rear intersection can be used to solve the position matrix of the correction camera in the world coordinate system, thereby further calculating the position change of the measurement camera.

[0092] The above description is merely an exemplary embodiment of the present invention. However, the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present invention should be included in the scope of protection of the present invention.

[0093] The various illustrative logical blocks, modules, and circuits described in conjunction with the present disclosure may be implemented or executed with a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic device (PLD), discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. A general-purpose processor may be a microprocessor, but in the alternative, the processor may be any commercially available processor, controller, microcontroller, or state machine. A processor may also be implemented as a combination of computing devices, for example, a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration.

[0094] The steps of the method or algorithm described in conjunction with the present disclosure can be implemented directly in hardware, in a software module executed by a processor, or in a combination of the two. The software module can reside in any form of storage medium known in the art. Some examples of usable storage media include random access memory (RAM), read-only memory (ROM), flash memory, EPROM memory, EEPROM memory, registers, hard disk, removable disk, CD-ROM, etc. The software module can include a single instruction or many instructions and can be distributed over several different code segments, distributed between different programs and distributed across multiple storage media. A storage medium can be coupled to a processor so that the processor can read and write information from / to the storage medium. Alternatively, a storage medium can be integrated into the processor.

[0095] The methods disclosed herein include one or more steps or actions for achieving the described method. These method steps and / or actions may be interchangeable with one another without departing from the scope of the claims. In other words, unless a particular order of steps or actions is specified, the order and / or use of the specific steps and / or actions may be modified without departing from the scope of the claims.

[0096] The processor can execute software stored on a machine-readable medium. The processor can be implemented with one or more general and / or special-purpose processors. Examples include microprocessors, microcontrollers, DSP processors, and other circuit systems that can execute software. Software should be broadly interpreted to mean instructions, data, or any combination thereof, whether referred to as software, firmware, middleware, microcode, hardware description language, or other. As an example, the machine-readable medium may include RAM (random access memory), flash memory, ROM (read-only memory), PROM (programmable read-only memory), EPROM (erasable programmable read-only memory), EEPROM (electrically erasable programmable read-only memory), registers, magnetic disks, optical disks, hard drives, or any other suitable storage medium, or any combination thereof. The machine-readable medium may be implemented in a computer program product. The computer program product may include packaging materials.

[0097] In a hardware implementation, the machine-readable medium may be a portion of the processing system that is separate from the processor. However, as will be readily appreciated by those skilled in the art, the machine-readable medium or any portion thereof may be external to the processing system. As an example, the machine-readable medium may include a transmission line, a carrier wave modulated by data, and / or a computer product separate from the wireless node, all of which may be accessed by the processor via a bus interface. Alternatively or in addition, the machine-readable medium or any portion thereof may be integrated into the processor, as may be the case with a cache and / or general register file.

[0098] The processing system can be configured as a general-purpose processing system having one or more microprocessors providing processor functionality and external memory providing at least a portion of the machine-readable medium, all linked together with other supporting circuitry via an external bus architecture. Alternatively, the processing system can be implemented as an ASIC (application-specific integrated circuit) with a processor, bus interface, user interface (in the case of an access terminal), supporting circuitry, and at least a portion of the machine-readable medium integrated into a single chip, or as one or more FPGAs (field programmable gate arrays), PLDs (programmable logic devices), controllers, state machines, gating logic, discrete hardware components, or any other suitable circuitry, or any combination of circuits capable of performing the various functionalities described throughout this disclosure. Depending on the specific application and the overall design constraints imposed on the overall system, those skilled in the art will recognize how to best implement the functionality described with respect to the processing system.

[0099] The machine-readable medium may include several software modules. These software modules include instructions that, when executed by a device (such as a processor), cause a processing system to perform various functions. These software modules may include a transmitting module and a receiving module. Each software module may reside in a single storage device or be distributed across multiple storage devices. As an example, when a triggering event occurs, a software module may be loaded from a hard drive into RAM. During execution of the software module, the processor may load some instructions into a cache to increase access speed. One or more cache lines may then be loaded into a general register file for execution by the processor. When describing the functionality of a software module below, it will be understood that such functionality is implemented by the processor when the processor executes instructions from the software module.

[0100] If implemented in software, each function may be stored as one or more instructions or codes on or transmitted by a computer-readable medium. Computer-readable media include both computer storage media and communication media, including any media that facilitates the transfer of a computer program from one place to another. Storage media can be any available medium that can be accessed by a computer. By way of example and not limitation, such computer-readable media may include RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and can be accessed by a computer. Any connection is also properly referred to as a computer-readable medium. For example, if the software is transmitted from a website, server, or other remote source using a coaxial cable, fiber optic cable, twisted pair, digital subscriber line (DSL), or wireless technology (such as infrared (IR), radio, and microwave), then the coaxial cable, fiber optic cable, twisted pair, DSL, or wireless technology (such as infrared, radio, and microwave) is included in the definition of medium. As used herein, disk and disc include compact disc (CD), laser disc, optical disc, digital versatile disc (DVD), floppy disk, and Disks, where disks often reproduce data magnetically, and discs reproduce data optically with lasers. Thus, in some aspects, computer-readable media may include non-transitory computer-readable media (e.g., tangible media). Additionally, for other aspects, computer-readable media may include transient computer-readable media (e.g., signals). Combinations of the above should also be included within the scope of computer-readable media.

[0101] Thus, some aspects may include a computer program product for performing the operations presented herein. For example, such a computer program product may include a computer-readable medium having stored (and / or encoded) thereon instructions, which are executable by one or more processors to perform the operations described herein. In some aspects, the computer program product may include packaging materials.

[0102] It will be understood that the claims are not limited to the precise configuration and components illustrated above. Various changes, substitutions and variations may be made in the arrangement, operation and details of the methods and apparatus described above without departing from the scope of the claims.

Claims

1. A method for correcting image measurement errors under airborne vibration conditions, comprising: In a stationary state, calibrating an intrinsic parameter matrix and an extrinsic parameter matrix of the measurement camera based on code points associated with the measurement camera; Correcting the position change of each measuring camera relative to the static state at the moment of vibration by photographing the code points associated with the measuring camera using a correction camera arranged at an arbitrary position behind the measuring camera; as well as The inter-measurement camera pose change matrix is corrected based on the pose change of each of the measurement cameras relative to the static state at the time of vibration.

2. The image measurement error correction method according to claim 1, further comprising: Correcting the rotation matrix between measurement cameras using a change in the three-axis attitude angle between the inertial navigation systems rigidly connected to each measurement camera relative to the static state at the vibration moment; as well as The corrected inter-camera pose change matrix is optimized based on the corrected inter-camera rotation matrix.

3. The image measurement error correction method according to claim 1, wherein: At least one or more of the following is true: The measuring camera includes at least two measuring cameras; There are four or more code points associated with each measurement camera; and The encoding point is rigidly connected to the associated test camera.

4. The image measurement error correction method according to claim 1, wherein: Correcting the position change of each of the measuring cameras relative to the static state at the vibration moment based on photographing the code points associated with the measuring cameras using a correction camera arranged at an arbitrary position behind the measuring cameras includes: photographing the code points associated with each measurement camera using the correction camera in the stationary state; determining a rotation and translation matrix of the correction camera in the static state in a world coordinate system based on capturing the code points associated with each measurement camera in the static state; photographing the code point associated with each measurement camera at the vibration moment using the correction camera; Determining a rotation and translation matrix of the correction camera in the world coordinate system at the target moment based on capturing the code points associated with each measurement camera at the vibration moment; and The position change of each measuring camera is determined based on the rotation and translation matrices of the correction camera in the world coordinate system at the static state and the vibration moment.

5. The image measurement error correction method according to claim 2, wherein: Optimizing the corrected inter-camera pose change matrix based on the corrected inter-camera rotation matrix includes: Predicting and updating the corrected inter-camera rotation matrix using a Kalman filter to obtain a filtered inter-camera rotation matrix; and The filtered inter-camera rotation matrix is used to optimize the corrected inter-camera pose change matrix.

6. The image measurement error correction method according to claim 2, further comprising: A synchronous trigger timing device is used to synchronously trigger the measurement camera, the inertial navigation system and the correction camera in the static state and the vibration moment respectively.

7. An error correction system for correcting image measurement errors under airborne vibration conditions, comprising: at least two measurement cameras; a code point associated with each measurement camera of the at least two test cameras; At least one correction camera is arranged at any position behind the at least two measuring cameras, and is used to capture the code points associated with the measuring cameras to correct the position change of each measuring camera relative to the static state at the moment of vibration, wherein The inter-measurement camera pose change matrix is corrected based on the pose change of each of the measurement cameras relative to the static state at the vibration moment.

8. The error correction system of claim 7, further comprising: at least two inertial navigation systems rigidly connected to each measurement camera of the at least two test cameras; in The change in the three-axis attitude angle between the inertial navigation systems rigidly connected to each measuring camera relative to the static state at the vibration moment is used to correct the rotation matrix between the measuring cameras; and The modified inter-measurement-camera pose change matrix is optimized based on the modified inter-measurement-camera rotation matrix.

9. The error correction system of claim 8, further comprising: The synchronous triggering timing device is used to synchronously trigger the measuring camera, the inertial navigation system and the correction camera in the static state and the vibration moment respectively.

10. The error correction system of claim 8, further comprising: A data processing device is used to correct the inter-camera pose change matrix, wherein the corrected inter-camera pose change matrix is optimized by the data processing device based on Kalman filtering based on the optimization of the corrected inter-camera rotation matrix.

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