Rapid calibration and imaging method for aerial photography

Abstract

The invention relates to the technical field of imaging methods, in particular to an aerial photography rapid calibration and imaging method, which comprises the following steps of: performing transmission quality analysis on current data transmission through a photography transmission quality analysis module and generating a transmission quality unqualified signal or a transmission quality qualified signal; the transmission quality qualification signal or the transmission quality disqualification signal is sent to the aerial photography management and control platform, and the transmission quality disqualification signal or the transmission quality qualification signal is sent to the unmanned aerial vehicle supervision terminal through the aerial photography management and control platform, so that supervision personnel can know the data transmission condition in time; according to the method and the system, corresponding supervisors can conveniently carry out follow-up targeted maintenance and repair, so that the follow-up data transmission stability and efficiency of the corresponding unmanned aerial vehicle are ensured, and in addition, when the image orientation parameters measured by the POS system are used for carrying out placement element stereo mapping, a special calibration field does not need to be arranged to calibrate the POS system.

Description

Technical Field

[0001] This invention relates to the field of imaging methods, and more specifically, to a rapid calibration and imaging method for aerial photography. Background Technology

[0002] Photogrammetry and remote sensing are technologies, sciences, and techniques for acquiring reliable information about the Earth, its environment, and other objects through recording, measurement, analysis, and representation using non-contact imaging and other sensor systems. Photogrammetry focuses on extracting geometric information, while remote sensing focuses on extracting physical information. In other words, photogrammetry is a technology, science, and technique for acquiring reliable information about the Earth, its environment, and other objects' geometry and properties through recording, measurement, analysis, and representation using non-contact imaging systems. Aerial photogrammetry involves taking photographs of the ground from an aircraft flying along a specific flight path. The images have a certain degree of overlap; the overlap along the flight path (called forward overlap) should be no less than 60%, and the overlap between adjacent flight paths (called lateral overlap) should be no less than 20%. It is a fundamental method for mapping national topographic maps and constructing corresponding Geographic Information Systems (GIS) in national economic and defense construction. Intelligent fixed-point exposure aerial photography was developed to replace traditional manual or timed exposure methods in aerial photogrammetry and airborne remote sensing. Its results achieve a fixed-point exposure accuracy within 5 meters along the flight path, and can even reach sub-meter level. The intelligent fixed-point exposure aerial photography method involves technical fields such as surveying and mapping, aerial photogrammetry, close-range measurement, engineering surveying, and airborne remote sensing.

[0003] Currently, the two commonly used methods of timed exposure or manual exposure cannot meet the needs of digital aerial photography and airborne remote sensing. The intelligent fixed-point exposure aerial photography method can overcome the drawback of increasing the workload in order to meet the requirements of the regulations. At present, when conducting UAV aerial photography, the movement and shooting of the UAV are mainly controlled by manual operation. It is difficult to effectively process the relevant measurement data, automatically correct the position of the UAV at the deployment point, and conduct pre-strategy analysis before aerial photography to determine whether the aerial photography environment and UAV status are normal. It is difficult to ensure the clarity of the captured images and the safe operation of the UAV. Furthermore, it is impossible to detect and analyze the data transmission status of the UAV, which is not conducive to the subsequent maintenance and supervision of the UAV. Summary of the Invention

[0004] In view of the problems existing in the prior art, the present invention provides a rapid calibration and imaging method for aerial photography to solve the technical problems mentioned in the background art. Technical solution

[0005] To achieve the above objectives, the present invention provides the following technical solution: a rapid aerial photography calibration and imaging method, comprising the following steps: Step 1: Digital camera calibration. The self-calibration method is used to solve the parameters of the digital camera by using the correspondence between the sequence of images taken by the digital camera during movement. When performing aerial photography operations, the central control computer can learn the flight platform's trajectory when the GPS signal is normal. Step 2: Starting from the collinearity condition equation, derive the theoretical relationship between the image exterior orientation element error and the target point position error. Based on this theoretical relationship, construct the POS system error compensation model. When the GPS signal is abnormal, the central control computer can predict the real-time coordinates of the flight platform based on the trajectory learned in step 1. Step 3: Field data acquisition; Office data processing, including obtaining spatial coordinates by correcting the single-image field feature acquisition results with encrypted office data and DEM, or obtaining the spatial coordinates of features by using the post-intersection-pre-intersection method from two-image stereo, performing data stitching and map sheet cropping to form feature data in map sheet units, and converting the format to the editing system for direct aerial survey office editing work; Step 4: The aerial photography control platform sets up several photography control points in the required photography area and marks them as analysis control points i. The longitude, latitude, and altitude of analysis control point i are sent to the corresponding UAV. The corresponding UAV enters the corresponding analysis control point i according to the preset flight path. The UAV position offset correction analysis module performs position offset correction analysis on the corresponding UAV. After analysis, the UAV correction signal or UAV position qualified signal is sent to the aerial photography control platform. After receiving the UAV correction signal, the aerial photography control platform issues a correction command to the corresponding UAV. After receiving the corresponding correction command, the UAV automatically performs position correction. Step 5: Introduce the POS system error compensation model into the collinearity condition equation to construct a rigorous adjustment model for direct ground target positioning of the self-calibrated POS. Utilize the least squares adjustment principle to simultaneously solve for the additional parameters and the 3D ground coordinates of the points to be determined, achieving direct ground target positioning of the self-calibrated POS with additional parameters. The straddle exposure strategy employed ensures that the exposure error is less than 5 meters for flight platforms at different flight speeds. In flat areas undergoing equal baseline aerial photography, the intelligent fixed-point exposure aerial photography method ensures accurate exposure at individual exposure points where cloud cover affects image quality. In areas with significant terrain undulations undergoing variable baseline aerial photography, the intelligent fixed-point exposure aerial photography method ensures that the overlap between adjacent images is close to the designed overlap.

[0006] The present invention is further configured to employ a digital camera calibration method based on the essential matrix and the fundamental matrix. The method first finds the correspondence between matching points between pairs of images, calculates the fundamental matrix F or the essential matrix E, and then decomposes the intrinsic and extrinsic parameters of the digital camera.

[0007] The present invention is further configured to perform digital camera calibration based on the Kruppa equation, derive the Kruppa equation using the absolute quadratic curve and epipolar transformation, solve the equation of the quadratic curve between two images, and solve for the camera's intrinsic parameters.

[0008] The present invention is further configured such that, after the corresponding UAV position correction is completed, the UAV at the corresponding analysis control point i is analyzed by the pre-photography decision module. The analysis determines whether the pre-preparation operation of the corresponding UAV is qualified. If the pre-preparation operation is unqualified, a pre-preparation failure signal is generated and sent to the aerial photography control platform. After receiving the pre-preparation failure signal, the aerial photography control platform stops performing UAV aerial photography measurement; otherwise, the next step is carried out. The camera mechanism of the corresponding UAV performs aerial photography at the corresponding analysis control point i, and packages the captured images, shooting time, shooting position, shooting environment information, and UAV shooting status information into a data folder. The corresponding data folder is sent to the data storage module in the aerial photography control platform for storage.

[0009] The present invention is further configured such that, after the corresponding data folder is transferred, the photography transmission quality analysis module performs transmission quality analysis on the data transmission, generates a transmission quality failure signal or a transmission quality qualification signal through the transmission quality analysis, and sends the transmission quality qualification signal or transmission quality failure signal to the aerial photography control platform.

[0010] The present invention is further configured to perform digital camera calibration based on an active vision calibration method, wherein the active vision calibration method employs a motion platform to actively control the motion trajectory of the digital camera, and the motion parameters are accurately recorded, and the internal and external parameters are calculated using known motion parameters and image information.

[0011] The present invention is further configured to perform digital camera calibration using a hierarchical step-by-step calibration method. First, the sequence is projectively reconstructed according to the hierarchical relationship of projective geometry, and then the intrinsic parameters of the digital camera are calculated using constraint conditions.

[0012] The present invention is further configured such that the specific analysis process of photographic environment detection and analysis is as follows: obtaining the illumination data of the corresponding UAV at the corresponding analysis control point i, retrieving the preset suitable illumination range for photography and calculating the average of the maximum and minimum values ​​of the preset suitable illumination range to obtain the optimal brightness value, calculating the difference between the illumination data and the optimal brightness value and taking the absolute value to obtain the brightness deviation data, and obtaining the wind speed data and air visibility data of the corresponding UAV at the corresponding analysis control point i, and performing numerical calculations on the brightness deviation data, wind speed data and air visibility data to obtain the environmental decision value. Beneficial effects

[0013] Compared with the prior art, the present invention has the following beneficial effects: In this invention, the transmission quality analysis module analyzes the transmission quality of the current data transmission and generates a transmission quality failure signal or a transmission quality success signal. The transmission quality success signal or transmission quality failure signal is sent to the aerial photography control platform, and the aerial photography control platform sends the transmission quality failure signal or transmission quality success signal to the UAV monitoring terminal. This helps the monitoring personnel to understand the data transmission status in a timely manner, which facilitates the corresponding monitoring personnel to carry out targeted maintenance and repairs, so as to ensure the stability and efficiency of subsequent data transmission of the corresponding UAV. In addition, when the present invention uses the image orientation parameters measured by the POS system to perform stereo mapping of placement elements, there is no need to set up a special calibration field to calibrate the POS system. Its system error can be completely compensated by introducing appropriate additional parameters in the direct ground target positioning of the POS. Attached Figure Description

[0014] Figure 1 This is a schematic diagram of the overall structure of the present invention. Detailed Implementation

[0015] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0016] It should be noted that, unless otherwise specified, all technical and scientific terms used in this application have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains. Example

[0017] Please see Figure 1 A rapid calibration and imaging method for aerial photography includes the following steps: Step 1: Digital camera calibration. The self-calibration method is used to solve the parameters of the digital camera by using the correspondence between the sequence of images taken by the digital camera during movement. When performing aerial photography operations, the central control computer can learn the flight platform's trajectory when the GPS signal is normal. Step 2: Starting from the collinearity condition equation, derive the theoretical relationship between the image exterior orientation element error and the target point position error. Based on this theoretical relationship, construct the POS system error compensation model. When the GPS signal is abnormal, the central control computer can predict the real-time coordinates of the flight platform based on the trajectory learned in step 1. Step 3: Field data acquisition; Office data processing, including obtaining spatial coordinates by correcting the single-image field feature acquisition results with encrypted office data and DEM, or obtaining the spatial coordinates of features by using the post-intersection-pre-intersection method from two-image stereo, performing data stitching and map sheet cropping to form feature data in map sheet units, and converting the format to the editing system for direct aerial survey office editing work; Step 4: The aerial photography control platform sets up several photography control points in the required photography area and marks them as analysis control points i. The longitude, latitude, and altitude of analysis control point i are sent to the corresponding UAV. The corresponding UAV enters the corresponding analysis control point i according to the preset flight path. The UAV position offset correction analysis module performs position offset correction analysis on the corresponding UAV. After analysis, the UAV correction signal or UAV position qualified signal is sent to the aerial photography control platform. After receiving the UAV correction signal, the aerial photography control platform issues a correction command to the corresponding UAV. After receiving the corresponding correction command, the UAV automatically performs position correction. Step 5: Introduce the POS system error compensation model into the collinearity condition equation to construct a rigorous adjustment model for direct ground target positioning of the self-calibrated POS. Utilize the least squares adjustment principle to simultaneously solve for the additional parameters and the 3D ground coordinates of the points to be determined, achieving direct ground target positioning of the self-calibrated POS with additional parameters. The straddle exposure strategy employed ensures that the exposure error is less than 5 meters for flight platforms at different flight speeds. In flat areas undergoing equal baseline aerial photography, the intelligent fixed-point exposure aerial photography method ensures accurate exposure at individual exposure points where cloud cover affects image quality. In areas with significant terrain undulations undergoing variable baseline aerial photography, the intelligent fixed-point exposure aerial photography method ensures that the overlap between adjacent images is close to the designed overlap. Example

[0018] Building upon Example 1, a further embodiment employs a digital camera calibration method based on the essential matrix and fundamental matrix. This method first identifies the correspondence between matching points in each pair of images, calculates the fundamental matrix F or the essential matrix E, and then decomposes the intrinsic and extrinsic parameters of the digital camera. It then uses the Kruppa equation for digital camera calibration, deriving the Kruppa equation using absolute quadratic curves and epipolar transformations, solving for the equation of the quadratic curve between two images, and thus solving for the camera's intrinsic parameters. After the corresponding UAV position correction is completed, the pre-processing decision module analyzes the UAV corresponding to the analysis and control point i, and determines the corresponding UAV position based on the analysis. The system checks whether the pre-operation preparation of the drone is successful. If the pre-operation preparation is unsuccessful, a pre-operation failure signal is generated and sent to the aerial photography control platform. Upon receiving the pre-operation failure signal, the aerial photography control platform stops the drone aerial photography measurement; otherwise, it proceeds to the next step. The corresponding drone's camera mechanism takes aerial photos at the corresponding analysis and control point i, and packages the captured images, shooting time, shooting location, shooting environment information, and drone shooting status information into a data folder. This data folder is then sent to the data storage module within the aerial photography control platform for storage. After the corresponding data folder is transferred, the photography transmission... The quality analysis module performs transmission quality analysis on the current data transmission, generating either a transmission quality failure signal or a transmission quality pass signal, which is then sent to the aerial photography control platform. A digital camera calibration method based on active vision is used. This method employs a motion platform to actively control the digital camera's trajectory, and the motion parameters are precisely recorded. Internal and external parameters are calculated using known motion parameters and image information. A hierarchical, step-by-step calibration method is used for digital camera calibration, first aligning the sequence according to the hierarchical relationship of projective geometry. Projective reconstruction is performed, and then the intrinsic parameters of the digital camera are calculated using constraints. The specific analysis process of photographic environment detection and analysis is as follows: the illumination data of the corresponding UAV at the corresponding analysis control point i is obtained, the preset suitable illumination range for photography is retrieved, and the maximum and minimum values ​​of the preset suitable illumination range are averaged to obtain the optimal brightness value. The difference between the illumination data and the optimal brightness value is calculated and the absolute value is taken to obtain the brightness deviation data. The wind speed data and air visibility data of the corresponding UAV at the corresponding analysis control point i are also obtained. The brightness deviation data, wind speed data, and air visibility data are numerically calculated to obtain the environmental decision value.

[0019] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A rapid calibration and imaging method for aerial photography, characterized by: Includes the following steps: Step 1: Digital camera calibration. The self-calibration method is used to solve the parameters of the digital camera by using the correspondence between the sequence of images taken by the digital camera during movement. When performing aerial photography operations, the central control computer can learn the flight platform's trajectory when the GPS signal is normal. Step 2: Starting from the collinearity condition equation, derive the theoretical relationship between the image exterior orientation element error and the target point position error. Based on this theoretical relationship, construct the POS system error compensation model. When the GPS signal is abnormal, the central control computer can predict the real-time coordinates of the flight platform based on the trajectory learned in step 1. Step 3: Field data acquisition; Office data processing, including obtaining spatial coordinates by correcting the single-image field feature acquisition results with encrypted office data and DEM, or obtaining the spatial coordinates of features by using the post-intersection-pre-intersection method from two-image stereo, performing data stitching and map sheet cropping to form feature data in map sheet units, and converting the format to the editing system for direct aerial survey office editing work; Step 4: The aerial photography control platform sets up several photography control points in the required photography area and marks them as analysis control points i. The longitude, latitude, and altitude of analysis control point i are sent to the corresponding UAV. The corresponding UAV enters the corresponding analysis control point i according to the preset flight path. The UAV position offset correction analysis module performs position offset correction analysis on the corresponding UAV. After analysis, the UAV correction signal or UAV position qualified signal is sent to the aerial photography control platform. After receiving the UAV correction signal, the aerial photography control platform issues a correction command to the corresponding UAV. After receiving the corresponding correction command, the UAV automatically performs position correction. Step 5: Introduce the POS system error compensation model into the collinearity condition equation to construct a rigorous adjustment model for direct ground target positioning of the self-calibrated POS. Utilize the least squares adjustment principle to simultaneously solve for the additional parameters and the 3D ground coordinates of the points to be determined, achieving direct ground target positioning of the self-calibrated POS with additional parameters. The straddle exposure strategy employed ensures that the exposure error is less than 5 meters for flight platforms at different flight speeds. In flat areas undergoing equal baseline aerial photography, the intelligent fixed-point exposure aerial photography method ensures accurate exposure at individual exposure points where cloud cover affects image quality. In areas with significant terrain undulations undergoing variable baseline aerial photography, the intelligent fixed-point exposure aerial photography method ensures that the overlap between adjacent images is close to the designed overlap.

2. The rapid calibration and imaging method for aerial photography according to claim 1, characterized in that: A digital camera calibration method based on the essential matrix and the fundamental matrix is ​​adopted. The method first finds the correspondence between matching points between pairs of images, calculates the fundamental matrix F or the essential matrix E, and then decomposes the intrinsic and extrinsic parameters of the digital camera.

3. The rapid calibration and imaging method for aerial photography according to claim 2, characterized in that: Digital camera calibration is performed based on the Kruppa equation. The Kruppa equation is derived using the absolute quadratic curve and epipolar transformation. The equation of the quadratic curve is solved between two images to obtain the camera's intrinsic parameters.

4. The rapid aerial photography calibration and imaging method according to claim 1, characterized in that: After the corresponding UAV position is corrected, the pre-photography decision module analyzes the UAV at the corresponding analysis control point i. The analysis determines whether the UAV's pre-preparation operation is qualified. If the pre-preparation operation is unqualified, a pre-preparation failure signal is generated and sent to the aerial photography control platform. Upon receiving the pre-preparation failure signal, the aerial photography control platform stops the UAV aerial photography measurement; otherwise, it proceeds to the next step. The corresponding UAV's camera mechanism takes aerial photos at the corresponding analysis control point i, and packages the captured images, shooting time, shooting location, shooting environment information, and UAV shooting status information into a data folder. The corresponding data folder is then sent to the data storage module in the aerial photography control platform for storage.

5. The rapid calibration and imaging method for aerial photography according to claim 1, characterized in that: After the corresponding data folder is transferred, the photography transmission quality analysis module will perform transmission quality analysis on the data transmission. The transmission quality analysis will generate a transmission quality failure signal or a transmission quality pass signal, and send the transmission quality pass signal or transmission quality failure signal to the aerial photography control platform.

6. The rapid calibration and imaging method for aerial photography according to claim 5, characterized in that: The calibration method based on active vision is used to calibrate digital cameras. The active vision-based calibration method uses a motion platform to actively control the motion trajectory of the digital camera, and the motion parameters are accurately recorded. The internal and external parameters are calculated using the known motion parameters and image information.

7. The rapid calibration and imaging method for aerial photography according to claim 5, characterized in that: A hierarchical step-by-step calibration method is adopted for digital camera calibration. First, the sequence is projectively reconstructed according to the hierarchical relationship of projective geometry. Then, the intrinsic parameters of the digital camera are calculated using constraints.

8. The rapid calibration and imaging method for aerial photography according to claim 5, characterized in that: The specific analysis process of photographic environment detection and analysis is as follows: Obtain the illumination data of the corresponding UAV at the corresponding analysis control point i; retrieve the preset suitable illumination range for photography and calculate the average of the maximum and minimum values ​​of the preset suitable illumination range to obtain the optimal brightness value; calculate the difference between the illumination data and the optimal brightness value and take the absolute value to obtain the brightness deviation data; and obtain the wind speed data and air visibility data of the corresponding UAV at the corresponding analysis control point i; and perform numerical calculations on the brightness deviation data, wind speed data, and air visibility data to obtain the environmental decision value.

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