Monocular camera imaging consistency detection device and method based on relative pixel coordinates

Abstract

The invention belongs to the field of monocular camera imaging consistency detection, and particularly relates to a monocular camera imaging consistency detection device and method based on relative pixel coordinates, and the device comprises a strapdown fixing tool, a detection target plate and imaging consistency resolving software. The strapdown fixing tool comprises a monocular camera strapdown fixing tool and a detection target plate strapdown fixing tool; a pattern for camera detection is arranged on a single surface of the detection target plate, the pattern is formed by arranging squares with fixed lengths according to a black and white checkerboard pattern, and the line number of the checkerboard pattern is smaller than the column number of the checkerboard pattern; the detection target plate is mounted on the detection target plate strapdown fixing tool; the monocular camera is installed on the monocular camera strapdown fixing tool and connected to the image processing computer through a special cable, and data processing and image consistency resolving are carried out through imaging consistency resolving software. The device has low requirements on detection equipment, is simple to operate, is not easily influenced by the environment, and is low in cost and convenient to apply.

Description

Technical Field

[0001] The present invention belongs to the field of monocular camera imaging consistency detection, and in particular relates to a monocular camera imaging consistency detection device and method based on relative pixel coordinates. Background Art

[0002] As an important sensor that can stably collect environmental visual information, monocular cameras have been widely used in fields such as intelligent driving, pattern recognition, and target tracking, and are playing an increasingly important role in various intelligent systems such as unmanned vehicles and drones. In some application scenarios, monocular cameras need to ensure imaging performance after flight vibration and strong impact, so the imaging consistency detection of monocular cameras has become a necessary step in engineering applications. The current mainstream imaging consistency detection method for monocular cameras is mainly to use optical instruments for detection, that is, to use parallel light tubes to measure the camera optical path, so as to detect the main optical axis offset of the camera before and after vibration and impact, and then detect the imaging consistency of the camera.

[0003] When the above method is used to test the imaging consistency of the camera, there are requirements for the equipment, scene and operation of the test; the above method requires the presence of collimator, light source and high-precision micro-rotating table for adjusting the camera posture and other devices; the light path needs to be isolated during the test to ensure that the imaging is not interfered by external light sources; during the operation, the three axes of the camera must be adjusted separately using a high-precision micro-rotating table and the readings must be recorded, and finally the multiple groups of readings of the high-precision micro-rotating table must be processed and converted to obtain the measurement results; when using a collimator for testing, the requirements for the testing equipment are relatively stringent, there are certain requirements for the testing environment and there are also high requirements for the precision of each device. In addition, there is an alignment error when using the micro-rotating table for adjustment, and there is also a visual error when aligning the crosshair pattern of the collimator; the above factors make the collimator detection method a detection method that is costly, inconvenient to use and has systematic errors;

[0004] Taking the above situation into consideration, how to design and implement a monocular camera imaging consistency detection method that has low requirements for detection equipment, simple operation method, is not easily affected by the environment, is low in cost, and is easy to use has become a key issue that needs to be urgently solved in the current camera engineering application field. Summary of the invention

[0005] 1. Technical issues to be resolved

[0006] The technical problem to be solved by the present invention is: in the absence of an optical instrument similar to a collimator or when high detection accuracy is required, a monocular camera imaging consistency detection device is designed and implemented, which has low requirements on detection equipment, is simple to operate, is not easily affected by the environment, is low in cost, and is easy to use, so as to complete the imaging consistency detection of the monocular camera; further, the present invention designs and implements a monocular camera imaging consistency detection method based on relative pixel coordinates on the basis of the imaging consistency detection device; the method only requires a checkerboard calibration plate and a strapdown fixing tooling that meet the specified requirements, and processes the calibration plate images collected by the camera before and after the impact vibration test, so as to solve the offset of the camera's main optical axis, thereby realizing the imaging consistency detection of the monocular camera.

[0007] (II) Technical solution

[0008] In order to solve the above technical problems, the present invention provides a monocular camera imaging consistency detection device based on relative pixel coordinates, the device comprising: a strapdown fixing tool, a detection target plate and imaging consistency solving software;

[0009] The strapdown fixing tooling includes: a monocular camera strapdown fixing tooling and a detection target plate strapdown fixing tooling;

[0010] The detection target plate has a pattern for camera detection on one side, the pattern is squares of fixed length arranged in a black and white checkerboard form, and the number of rows of the checkerboard pattern is less than the number of columns; the detection target plate is installed on the detection target plate strapdown fixing tooling;

[0011] The monocular camera is installed on the monocular camera strapdown fixture, connected to the image processing computer through a dedicated cable, and performs data processing and image consistency solution through imaging consistency solution software.

[0012] In addition, the present invention also provides a monocular camera imaging consistency detection method based on relative pixel coordinates, the method is implemented based on the above device, and the method includes:

[0013] There are eight steps: assembly and placement, re-inspection and fine-tuning, image acquisition, image coordinate system construction, relative pixel coordinate calculation, re-projection error analysis and camera optical axis change angle calculation. The basic flow chart of the method is as follows Figure 1 shown.

[0014] Wherein, the method comprises the following steps:

[0015] Step A. Assemble and place the imaging consistency detection device; on a reference plane with a length of not less than 2.5m and a width of not less than 1m, assemble and place the detection target plate, the monocular camera, and the strapdown fixing tooling on the reference plane in sequence;

[0016] Step B. Recheck and fine-tune the assembled device; ensure that the installed detection target plate is at a 90-degree angle to the reference plane; ensure that the optical path of the installed monocular camera is parallel to the reference plane, and that the monocular camera is facing the checkerboard pattern of the detection target plate;

[0017] Step C. Power on and debug the device after re-inspection and fine-tuning; connect the monocular camera to the image processing computer through a dedicated cable, and observe the real-time image of the monocular camera through the image processing computer to ensure that the image is clear and the checkerboard pattern of the detection target plate is completely in the real-time image field of the monocular camera after power-on;

[0018] Step D. On the premise that Step A, Step B and Step C are completed, use a monocular camera to collect images before the flight vibration impact test; during the collection, it is necessary to ensure that the checkerboard pattern of the front target plate is basically located in the center of the camera's field of view, the size occupies more than half of the camera's field of view, and all the checkerboard patterns are within the monocular camera's field of view; if the above conditions are not met, return to Step B. Once the image collection is completed in this step, all strapdown tooling needs to remain stationary and cannot be moved;

[0019] Step E. After step D is completed, remove the monocular camera and conduct flight vibration and impact tests on it. After the test, ensure that the camera and the strapdown fixture mounting surface are not damaged;

[0020] Step F. Reinstall the monocular camera that has completed the flight vibration and impact test onto the imaging consistency detection device, during which ensure that the positions of the strapdown tooling and the detection target plate do not change. If the position changes due to careless operation, return to step B and start again;

[0021] Step G. Based on step F, the monocular camera is powered on again and a second image acquisition is performed; since the positions of the strapdown tooling and the detection target plate have not changed from step D to step F, the image acquired in step G is the image result after the flight vibration impact test;

[0022] Step H. performing checkerboard corner point detection on the checkerboard image of the detection target plate before the flight vibration impact test in step D and the checkerboard image of the detection target plate after the flight vibration impact test in step G respectively;

[0023] Step I. Construct the image pixel coordinate system; take the upper left corner of the image as the origin, the image width as the X-axis, the image height as the Y-axis, the positive direction of the X-axis is to the right, and the positive direction of the Y-axis is downward;

[0024] Step J. Based on step I, the pixel coordinates of each checkerboard corner point on the two frames of images before and after the flight vibration and impact test are calculated;

[0025] Step K. Calculate the reprojection errors of the corner point detection and extraction of the two images before and after the flight vibration shock test: m, n; if m<0.5 and n<0.5, then it is considered that the two images collected before and after the test meet the requirements, and step L can be continued; otherwise, it is considered that the data collection error is large, and return to step D and start again;

[0026] Step L. Calculate the relative pixel coordinate changes of each checkerboard corner point of the two images before and after the flight vibration shock test and perform mean quantization;

[0027] Step M. Calculate the offset angles of the camera optical axis in the pitch direction and the yaw direction before and after the flight vibration shock test;

[0028] Step N. Calculate the offset angle of the two images in the rolling direction before and after the flight vibration shock test; calculate the affine transformation between the two images and further calculate the offset angle of the two images in the rolling direction before and after the flight vibration shock test using the known corner point pixel coordinates in the two images obtained in step J.

[0029] The checkerboard corner point detection in step H is performed using the SURF method, which has high feature point detection accuracy and is widely used in related fields.

[0030] Wherein, in step J, the method for calculating the pixel coordinates of the corner points of the chessboard is:

[0031] Step J.1. Combine the detection results of step H and directly extract the pixel coordinates of the checkerboard corner points;

[0032] Step J.2. Combined with the image pixel coordinate system constructed in step I, the checkerboard corner points extracted in step J.1 are numbered and sorted in order from left to right and from top to bottom;

[0033] Step J.3. List the pixel coordinates of each checkerboard corner point on the two frames of images before and after the flight vibration and shock test and represent them with the following point set:

[0034] (x0,y0),(x1,y1),(x2,y2)…(x n-1 ,y n-1 )

[0035] and (x'0,y'0),(x'1,y'1),(x'2,y'2)...(x' n-1 ,y' n-1 ).

[0036] Wherein, in the step K, the method for calculating the reprojection error of the image corner point extraction detection is:

[0037] Step K.1. Calculate the world coordinate system coordinates of the corner points of the chessboard based on the physical length d of the side of the chessboard and project them onto the image plane to obtain the theoretical pixel coordinate values ​​of the corner points of the chessboard;

[0038] Step K.2. Combine step J to obtain the actual pixel coordinate values ​​of the corner points of the chessboard;

[0039] Step K.3. For each chessboard corner point, calculate the Euclidean distance between the actual pixel coordinate and the theoretical pixel coordinate;

[0040] Step K.4. Take the average of the Euclidean distances solved for each corner point in a frame of image to obtain the reprojection error of the frame of image; thereby calculating the reprojection errors of the corner points of the two images before and after the flight vibration impact test: β1, β2.

[0041] Wherein, in step L, the method for calculating the relative pixel coordinate change of the two images and their mean is:

[0042] Step L.1. Based on the completion of step J.3, the pixel coordinates of the corner points of the image before the test and the corner points of the image after the test are respectively subtracted:

[0043] (Δx0,Δy0),(Δx1,Δy1),(Δx2,Δy2)…(Δx n-1 ,Δy n-1 );

[0044] Step L.2. Take the average of the relative pixel coordinate changes of the two images obtained in L.1:

[0045]

[0046] Wherein, in the step M, the method for calculating the offset angles of the camera optical axis in the pitch direction and the yaw direction before and after the flight vibration shock test is:

[0047] Step M.1. Considering that the camera optical axis changes at a small angle, the angle offset of the camera main optical axis in pitch and yaw can be considered to be decoupled and can be derived separately;

[0048] Step M.2. Based on the premise of step M.1., the basic parameters required for derivation include the camera CMOS pixel size Ψ and the focal length f of the monocular camera to be tested; the basic variables required for derivation include the changes in the relative pixel coordinates of the two images before and after the test in the two axes of the X-axis and the Y-axis. The camera optical axis is offset by an angle θ in the yaw and pitch directions x ,θ y ;

[0049] Step M.3. Construct the relationship between pixel coordinates and optical axis deviation angle; considering that the camera optical axis changes at a small angle, the change in the X-axis pixel coordinate can be derived. The angle θ is offset from the camera optical axis in the yaw direction x Relationship:

[0050]

[0051] Similarly, the Y-axis pixel coordinate change can be derived The angle θ offset from the camera optical axis in the pitch direction y Relationship:

[0052]

[0053] (III) Beneficial effects

[0054] Compared with the prior art, the present invention has the following beneficial effects:

[0055] (1) The present invention designs a device that can detect the consistency of monocular camera imaging. The device can complete the monocular camera imaging consistency detection without the need for optical instruments such as collimators. Compared with the traditional method of using optical instruments for detection, the present method has low requirements on detection equipment, is simple to operate, is not easily affected by the environment, is low in cost, and is easy to use. It greatly reduces the technical threshold and detection difficulty of monocular camera imaging consistency detection.

[0056] (2) Based on the above device, the present invention proposes a monocular camera imaging consistency detection method based on relative pixel coordinates. The method has low requirements on the detection equipment. It only needs to use a specified detection target plate and a strapdown fixed tooling to collect the detection target plate image before and after the impact vibration. The offset angle of the camera's main optical axis in the pitch and yaw directions and the offset angle of the image in the roll direction can be cleverly calculated through the relative pixel coordinate change. This method is the first to realize camera imaging consistency detection based on relative pixel coordinates, filling the vacuum zone in the development of related fields.

[0057] (3) The present invention has been verified by a large number of experiments. The device and method of the invention have high detection accuracy when performing camera imaging consistency detection. A large amount of experimental data shows that when the present invention is used to perform monocular camera imaging consistency detection, the angle error is less than 0.01°, and the index is better than the optical detection method, which fully meets the needs of the camera imaging consistency detection field. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 It is a flow chart of the technical solution of the present invention. DETAILED DESCRIPTION

[0059] In order to make the purpose, content, and advantages of the present invention more clear, the specific implementation methods of the present invention are further described in detail below in conjunction with the accompanying drawings and examples.

[0060] In order to solve the above technical problems, the present invention provides a monocular camera imaging consistency detection device based on relative pixel coordinates, the device comprising: a strapdown fixing tool, a detection target plate and imaging consistency solving software;

[0061] The strapdown fixing tooling includes: a monocular camera strapdown fixing tooling and a detection target plate strapdown fixing tooling;

[0062] The detection target plate has a pattern for camera detection on one side, the pattern is squares of fixed length arranged in a black and white checkerboard form, and the number of rows of the checkerboard pattern is less than the number of columns; the detection target plate is installed on the detection target plate strapdown fixing tooling;

[0063] The monocular camera is installed on the monocular camera strapdown fixture, connected to the image processing computer through a dedicated cable, and performs data processing and image consistency solution through imaging consistency solution software.

[0064] In addition, the present invention also provides a monocular camera imaging consistency detection method based on relative pixel coordinates, the method is implemented based on the above device, and the method includes:

[0065] There are eight steps: assembly and placement, re-inspection and fine-tuning, image acquisition, image coordinate system construction, relative pixel coordinate calculation, re-projection error analysis and camera optical axis change angle calculation. The basic flow chart of the method is as follows Figure 1 shown.

[0066] Wherein, the method comprises the following steps:

[0067] Step A. Assemble and place the imaging consistency detection device; on a reference plane with a length of not less than 2.5m and a width of not less than 1m, assemble and place the detection target plate, the monocular camera, and the strapdown fixing tooling on the reference plane in sequence;

[0068] Step B. Recheck and fine-tune the assembled device; ensure that the installed detection target plate is at a 90-degree angle to the reference plane; ensure that the optical path of the installed monocular camera is parallel to the reference plane, and that the monocular camera is facing the checkerboard pattern of the detection target plate;

[0069] Step C. Power on and debug the device after re-inspection and fine-tuning; connect the monocular camera to the image processing computer through a dedicated cable, and observe the real-time image of the monocular camera through the image processing computer to ensure that the image is clear and the checkerboard pattern of the detection target plate is completely in the real-time image field of the monocular camera after power-on;

[0070] Step D. On the premise that Step A, Step B and Step C are completed, use a monocular camera to collect images before the flight vibration impact test; during the collection, it is necessary to ensure that the checkerboard pattern of the front target plate is basically located in the center of the camera's field of view, the size occupies more than half of the camera's field of view, and all the checkerboard patterns are within the monocular camera's field of view; if the above conditions are not met, return to Step B. Once the image collection is completed in this step, all strapdown tooling needs to remain stationary and cannot be moved;

[0071] Step E. After step D is completed, remove the monocular camera and conduct flight vibration and impact tests on it. After the test, ensure that the camera and the strapdown fixture mounting surface are not damaged;

[0072] Step F. Reinstall the monocular camera that has completed the flight vibration and impact test onto the imaging consistency detection device, during which ensure that the positions of the strapdown tooling and the detection target plate do not change. If the position changes due to careless operation, return to step B and start again;

[0073] Step G. Based on step F, the monocular camera is powered on again and a second image acquisition is performed; since the positions of the strapdown tooling and the detection target plate have not changed from step D to step F, the image acquired in step G is the image result after the flight vibration impact test;

[0074] Step H. performing checkerboard corner point detection on the checkerboard image of the detection target plate before the flight vibration impact test in step D and the checkerboard image of the detection target plate after the flight vibration impact test in step G respectively;

[0075] Step I. Construct the image pixel coordinate system; take the upper left corner of the image as the origin, the image width as the X-axis, the image height as the Y-axis, the positive direction of the X-axis is to the right, and the positive direction of the Y-axis is downward;

[0076] Step J. Based on step I, the pixel coordinates of each checkerboard corner point on the two frames of images before and after the flight vibration and impact test are calculated;

[0077] Step K. Calculate the reprojection errors of the corner point detection and extraction of the two images before and after the flight vibration shock test: m, n; if m<0.5 and n<0.5, then it is considered that the two images collected before and after the test meet the requirements, and step L can be continued; otherwise, it is considered that the data collection error is large, and return to step D and start again;

[0078] Step L. Calculate the relative pixel coordinate changes of each checkerboard corner point of the two images before and after the flight vibration shock test and perform mean quantization;

[0079] Step M. Calculate the offset angles of the camera optical axis in the pitch direction and the yaw direction before and after the flight vibration shock test;

[0080] Step N. Calculate the offset angle of the two images in the rolling direction before and after the flight vibration shock test; calculate the affine transformation between the two images and further calculate the offset angle of the two images in the rolling direction before and after the flight vibration shock test using the known corner point pixel coordinates in the two images obtained in step J.

[0081] The checkerboard corner point detection in step H is performed using the SURF method, which has high feature point detection accuracy and is widely used in related fields.

[0082] Wherein, in step J, the method for calculating the pixel coordinates of the corner points of the chessboard is:

[0083] Step J.1. Combine the detection results of step H and directly extract the pixel coordinates of the checkerboard corner points;

[0084] Step J.2. Combined with the image pixel coordinate system constructed in step I, the checkerboard corner points extracted in step J.1 are numbered and sorted in order from left to right and from top to bottom;

[0085] Step J.3. List the pixel coordinates of each checkerboard corner point on the two frames of images before and after the flight vibration and shock test and represent them with the following point set:

[0086] (x0,y0),(x1,y1),(x2,y2)…(x n-1 ,y n-1 )

[0087] and (x'0,y'0),(x'1,y'1),(x'2,y'2)...(x' n-1 ,y' n-1 ).

[0088] Wherein, in the step K, the method for calculating the reprojection error of the image corner point extraction detection is:

[0089] Step K.1. Calculate the world coordinate system coordinates of the corner points of the chessboard based on the physical length d of the side of the chessboard and project them onto the image plane to obtain the theoretical pixel coordinate values ​​of the corner points of the chessboard;

[0090] Step K.2. Combine step J to obtain the actual pixel coordinate values ​​of the corner points of the chessboard;

[0091] Step K.3. For each chessboard corner point, calculate the Euclidean distance between the actual pixel coordinate and the theoretical pixel coordinate;

[0092] Step K.4. Take the average of the Euclidean distances solved for each corner point in a frame of image to obtain the reprojection error of the frame of image; thereby calculating the reprojection errors of the corner points of the two images before and after the flight vibration impact test: β1, β2.

[0093] Wherein, in step L, the method for calculating the relative pixel coordinate change of the two images and their mean is:

[0094] Step L.1. Based on the completion of step J.3, the pixel coordinates of the corner points of the image before the test and the corner points of the image after the test are respectively subtracted:

[0095] (Δx0,Δy0),(Δx1,Δy1),(Δx2,Δy2)…(Δx n-1 ,Δy n-1 );

[0096] Step L.2. Take the average of the relative pixel coordinate changes of the two images obtained in L.1:

[0097]

[0098] Wherein, in the step M, the method for calculating the offset angles of the camera optical axis in the pitch direction and the yaw direction before and after the flight vibration shock test is:

[0099] Step M.1. Considering that the camera optical axis changes at a small angle, the angle offset of the camera main optical axis in pitch and yaw can be considered to be decoupled and can be derived separately;

[0100] Step M.2. Based on the premise of step M.1., the basic parameters required for derivation include the camera CMOS pixel size Ψ and the focal length f of the monocular camera to be tested; the basic variables required for derivation include the changes in the relative pixel coordinates of the two images before and after the test in the two axes of the X-axis and the Y-axis. The camera optical axis is offset by an angle θ in the yaw and pitch directions x ,θ y ;

[0101] Step M.3. Construct the relationship between pixel coordinates and optical axis deviation angle; considering that the camera optical axis changes at a small angle, the change in the X-axis pixel coordinate can be derived. The angle θ is offset from the camera optical axis in the yaw direction x Relationship:

[0102]

[0103] Similarly, the Y-axis pixel coordinate change can be derived The angle θ offset from the camera optical axis in the pitch direction y Relationship:

[0104]

[0105] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A monocular camera imaging consistency detection device based on relative pixel coordinates, characterized in that: The device comprises: a strapdown fixing tool, a detection target plate and imaging consistency solving software; The strapdown fixing tooling includes: a monocular camera strapdown fixing tooling and a detection target plate strapdown fixing tooling; The detection target plate has a pattern for camera detection on one side, the pattern is squares of fixed length arranged in a black and white checkerboard form, and the number of rows of the checkerboard pattern is less than the number of columns; the detection target plate is installed on the detection target plate strapdown fixing tooling; The monocular camera is installed on the monocular camera strapdown fixture, connected to the image processing computer through a dedicated cable, and performs data processing and image consistency solution through imaging consistency solution software.

2. A monocular camera imaging consistency detection method based on relative pixel coordinates, characterized in that: The method is implemented based on the device according to claim 1, and the method comprises: There are eight steps: assembly and placement, re-inspection and fine-tuning, image acquisition, image coordinate system construction, relative pixel coordinate calculation, re-projection error analysis and camera optical axis angle calculation.

3. The monocular camera imaging consistency detection method based on relative pixel coordinates as claimed in claim 2, characterized in that: The method comprises the following steps: Step A. Assemble and place the imaging consistency detection device; on a reference plane with a length of not less than 2.5m and a width of not less than 1m, assemble and place the detection target plate, the monocular camera, and the strapdown fixing tooling on the reference plane in sequence; Step B. Recheck and fine-tune the assembled device; Ensure that the installed detection target plate is at a 90-degree angle to the reference plane; ensure that the optical path of the installed monocular camera is parallel to the reference plane, and that the monocular camera is facing the checkerboard pattern of the detection target plate; Step C. Power on and debug the device after re-inspection and fine-tuning; connect the monocular camera to the image processing computer through a dedicated cable, and observe the real-time image of the monocular camera through the image processing computer to ensure that the image is clear and the checkerboard pattern of the detection target plate is completely in the real-time image field of the monocular camera after power-on; Step D. On the premise that Step A, Step B and Step C are completed, use a monocular camera to collect images before the flight vibration impact test; during the collection, it is necessary to ensure that the checkerboard pattern of the front target plate is basically located in the center of the camera's field of view, the size occupies more than half of the camera's field of view, and all the checkerboard patterns are within the monocular camera's field of view; if the above conditions are not met, return to Step B. Once the image collection is completed in this step, all strapdown tooling needs to remain stationary and cannot be moved; Step E. After step D is completed, remove the monocular camera and conduct flight vibration and impact tests on it. After the test, ensure that the camera and the strapdown fixture mounting surface are not damaged; Step F. Reinstall the monocular camera that has completed the flight vibration and impact test onto the imaging consistency detection device, during which ensure that the positions of the strapdown tooling and the detection target plate do not change. If the position changes due to careless operation, return to step B and start again; Step G. Based on step F, the monocular camera is powered on again and a second image acquisition is performed; since the positions of the strapdown tooling and the detection target plate have not changed from step D to step F, the image acquired in step G is the image result after the flight vibration impact test; Step H. performing checkerboard corner point detection on the checkerboard image of the detection target plate before the flight vibration impact test in step D and the checkerboard image of the detection target plate after the flight vibration impact test in step G respectively; Step I. Construct the image pixel coordinate system; take the upper left corner of the image as the origin, the image width as the X-axis, the image height as the Y-axis, the positive direction of the X-axis is to the right, and the positive direction of the Y-axis is downward; Step J. Based on step I, the pixel coordinates of each checkerboard corner point on the two frames of images before and after the flight vibration and impact test are calculated; Step K. Calculate the reprojection errors of the corner point detection and extraction of the two images before and after the flight vibration shock test: m, n; if m<0.5 and n<0.5, then it is considered that the two images collected before and after the test meet the requirements, and step L can be continued; otherwise, it is considered that the data collection error is large, and return to step D and start again; Step L. Calculate the relative pixel coordinate changes of each checkerboard corner point of the two images before and after the flight vibration shock test and perform mean quantization; Step M. Calculate the offset angles of the camera optical axis in the pitch direction and the yaw direction before and after the flight vibration shock test; Step N. Calculate the offset angle of the two images in the rolling direction before and after the flight vibration shock test; calculate the affine transformation between the two images and further calculate the offset angle of the two images in the rolling direction before and after the flight vibration shock test using the known corner point pixel coordinates in the two images obtained in step J.

4. The monocular camera imaging consistency detection method based on relative pixel coordinates as claimed in claim 3, characterized in that: The checkerboard corner point detection in step H is performed using the SURF method, which has high feature point detection accuracy and is widely used in related fields.

5. The monocular camera imaging consistency detection method based on relative pixel coordinates as claimed in claim 4, characterized in that: In step J, the method for calculating the pixel coordinates of the corner points of the chessboard is: Step J.

1. Combine the detection results of step H and directly extract the pixel coordinates of the checkerboard corner points; Step J.

2. Combined with the image pixel coordinate system constructed in step I, the checkerboard corner points extracted in step J.1 are numbered and sorted in order from left to right and from top to bottom; Step J.

3. List the pixel coordinates of each checkerboard corner point on the two frames of images before and after the flight vibration and shock test and represent them with the following point set: (x0,y0),(x1,y1),(x2,y2)…(x n-1 ,y n-1 ) and (x'0, y'0), (x'1, y'1), (x'2, y'2)…(x' n-1 , y' n-1 ).

6. The monocular camera imaging consistency detection method based on relative pixel coordinates as claimed in claim 5, characterized in that: In step K, the method for calculating the reprojection error of image corner point extraction detection is: Step K.

1. Calculate the world coordinate system coordinates of the corner points of the chessboard based on the physical length d of the side of the chessboard and project them onto the image plane to obtain the theoretical pixel coordinate values ​​of the corner points of the chessboard; Step K.

2. Combine step J to obtain the actual pixel coordinate values ​​of the corner points of the chessboard; Step K.

3. For each chessboard corner point, calculate the Euclidean distance between the actual pixel coordinate and the theoretical pixel coordinate; Step K.

4. Take the average of the Euclidean distances solved for each corner point in a frame of image to obtain the reprojection error of the frame of image; thereby calculating the reprojection errors of the corner points of the two images before and after the flight vibration impact test: β1, β2.

7. The monocular camera imaging consistency detection method based on relative pixel coordinates according to claim 6, characterized in that: In step L, the method for calculating the relative pixel coordinate change of the two images and their mean is: Step L.

1. Based on the completion of step J.3, the pixel coordinates of the corner points of the image before the test and the corner points of the image after the test are respectively subtracted: (Δx0,Δy0),(Δx1,Δy1),(Δx2,Δy2)…(Δx n-1 ,Δy n-1 ); Step L.

2. Take the average of the relative pixel coordinate changes of the two images obtained in L.1:

8. The monocular camera imaging consistency detection method based on relative pixel coordinates as claimed in claim 7, characterized in that: In the step M, the method for calculating the offset angles of the camera optical axis in the pitch direction and the yaw direction before and after the flight vibration shock test is: Step M.

1. Considering that the camera optical axis changes at a small angle, the angle offset of the camera main optical axis in pitch and yaw can be considered to be decoupled and can be derived separately; Step M.

2. Based on the premise of step M.1., the basic parameters required for derivation include the camera CMOS pixel size Ψ and the focal length f of the monocular camera to be tested; the basic variables required for derivation include the changes in the relative pixel coordinates of the two images before and after the test in the two axes of the X-axis and the Y-axis. The camera optical axis is offset by an angle θ in the yaw and pitch directions x ,θ y ; Step M.

3. Construct the relationship between pixel coordinates and optical axis deviation angle; considering that the camera optical axis changes at a small angle, the change in the X-axis pixel coordinate can be derived. The angle θ is offset from the camera optical axis in the yaw direction x Relationship: Similarly, the Y-axis pixel coordinate change can be derived The angle θ offset from the camera optical axis in the pitch direction y Relationship:

9. The monocular camera imaging consistency detection method based on relative pixel coordinates as claimed in claim 8, characterized in that: The method has low requirements on detection equipment, is simple to operate, is not easily affected by the environment, is low in cost, and is easy to apply, which greatly reduces the technical threshold and detection difficulty of monocular camera imaging consistency detection.

10. The monocular camera imaging consistency detection method based on relative pixel coordinates according to claim 8, characterized in that: The method cleverly calculates the offset angles of the camera's main optical axis in the pitch and yaw directions and the offset angle of the image in the roll direction through the relative pixel coordinate changes, thereby realizing camera imaging consistency detection based on relative pixel coordinates.

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