CN111637871A - Unmanned aerial vehicle camera steady self-checking method and device based on rotary flight - Google Patents
Unmanned aerial vehicle camera steady self-checking method and device based on rotary flight Download PDFInfo
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- CN111637871A CN111637871A CN202010467543.XA CN202010467543A CN111637871A CN 111637871 A CN111637871 A CN 111637871A CN 202010467543 A CN202010467543 A CN 202010467543A CN 111637871 A CN111637871 A CN 111637871A
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C11/00—Photogrammetry or videogrammetry, e.g. stereogrammetry; Photographic surveying
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
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Abstract
The invention discloses a method and a device for unmanned aerial vehicle camera steady self-checking based on rotating flight, belonging to the field of aerial photogrammetry. And then, independently carrying out self-checking beam adjustment on the group of data, and solving orientation elements (including focal length, image principal point offset and objective lens distortion parameters) in the camera. The method mainly aims at the problem that the self-calibration of the camera has multiple solutions due to the fact that the strong correlation exists between the internal orientation elements and the external orientation elements of the camera in the existing self-calibration method of the camera, and the accurate and stable self-calibration result of the camera is obtained. Meanwhile, because the image data volume participating in the self-checking calculation is small, the calculation memory required in the self-checking processing process can be greatly reduced, the self-checking processing time is reduced, and high-precision self-checking parameters are provided for subsequent large-scale data processing.
Description
Technical Field
The invention belongs to the field of aerial photogrammetry, and particularly relates to a method and a device for unmanned aerial vehicle camera robustness self-checking based on rotary flight, in particular to rotary flight at a certain camera inclination angle.
Background
Photogrammetry is science and technology for recovering three-dimensional information of an object from an image, wherein internal orientation elements (camera focal length, image principal point coordinates) and external orientation elements (position and posture) of the recovered image are calculated through block adjustment, and the premise and the basis of subsequent information extraction are provided. Currently, unmanned aerial vehicle photogrammetry has become a new type of photogrammetry and is widely used. However, UAV photogrammetry typically uses non-metrology cameras, and the camera lens often has some degree of optical distortion (typically radial distortion (k)1,k2,k3) And tangential distortion (p)1,p2) In parallel, the image principal point coordinates (x) of the camera0,y0) And focal length f also often vary with time and environment. Inaccurate camera internal orientation elements cause errors in measured or matched image point coordinates, thereby influencing the accuracy of image position and attitude parameter calculation. Therefore, accurate geometric calibration of the camera is a necessary link for image photogrammetry processing, and is also a precondition for ensuring adjustment calculation precision.
The traditional close-range photogrammetry camera calibration method mainly utilizes a laboratory three-dimensional calibration field to calibrate the camera. The three-dimensional calibration field has the highest calibration precision, but the calibration field is too high in establishment cost and high in requirements, and instrument maintenance and high-precision measurement are required to be carried out at intervals so as to ensure the precision of the calibration field. For most small-scale, short-lived projects, the setup and maintenance of the checkfield will far exceed the project's own budget. In order to reduce the cost of camera calibration, Zhang Zhengyou propose a checkerboard self-calibration method which is widely applied to camera calibration. The method comprises the steps of assuming that a plane calibration board is placed on a horizontal plane of a world coordinate system where the plane calibration board is located, obtaining initial values of camera parameters through a linear imaging model, then giving an objective function considering nonlinear distortion based on a nonlinear imaging model, and finally obtaining an optimal solution of the camera parameters by using a nonlinear optimization method. The calibration method has better robustness and practicability, but has lower calibration precision. No matter the laboratory field calibration method or the checkerboard calibration method, the method is only suitable for the camera for short-distance shooting, but not suitable for the unmanned aerial vehicle camera focusing at infinity. Therefore, an outdoor discrete target laying method is proposed, the method can enlarge the scene, and therefore the unmanned aerial vehicle camera can obtain a clear calibration image when focusing at infinity, but the method has high requirements on the terrain, is only suitable for large-scale production tasks, and has great limitation on data processing of town areas.
The camera calibration method based on the scene needs to spend more time for camera calibration before data processing, so that the efficiency of data production processing is reduced. Therefore, some camera self-checking methods without a checking field are introduced in succession, such as a vanishing point-based camera checking method in computer vision, a Kruppa equation-based self-checking method, and a beam-method adjustment-based self-checking method in photogrammetry. The method does not need control fields such as checkerboards, off-site targets and the like, only needs to establish a constraint equation between more than two images, does not consider the photographic reconstruction process of an image sequence, and is therefore widely applied to photogrammetry and computer software, such as Photoscan, ContextCapture, Pix4dMapper and other three-dimensional modeling software. However, such methods have some defects, for example, when the image data volume is large, the consistency of all images with respect to the infinite plane of the determined projective space cannot be guaranteed, the stability of the calibration algorithm will be affected, and the calculation amount of the method for solving the equation is too large, the convergence after nonlinear optimization is not good, and a more accurate self-calibration initial value is required. Particularly, due to strong correlation among camera calibration parameters, different methods for solving the super-large equation set and different initial value selections can cause larger difference of camera self-calibration results of different modeling software on the same set of image data, and the instability of the method is further reflected.
Disclosure of Invention
Aiming at the defects or improvement requirements in the prior art, the invention provides a method and a device for unmanned aerial vehicle camera robust self-checking based on rotating flight, so that the technical problems that in the existing camera self-checking method, multiple solutions exist in camera inner orientation elements and self-checking results are unstable due to strong correlation between camera self-checking parameters and image outer orientation elements are solved.
In order to achieve the above object, according to an aspect of the present invention, there is provided a method for robust self-calibration of an unmanned aerial vehicle camera based on rotating flight, including:
(1) adjusting the unmanned aerial vehicle to a target height above a feature on a certain ground;
(2) setting the inclination angle of a camera of the unmanned aerial vehicle during rotary flight;
(3) estimating the target radius of the rotating flight according to the target height and the inclination angle, and constructing a rotating flight path, wherein the circle center of the rotating flight path is the center of the feature, and the radius of the rotating flight path is the target radius;
(4) according to the rotating flight path, aligning the camera to the feature object at the inclination angle for circular shooting, and acquiring image data;
(5) and performing camera self-calibration by using the image data, and stably acquiring relevant parameters of the camera.
Preferably, step (1) comprises:
before surveying district with unmanned aerial vehicle and carrying out conventional image acquisition or after gathering, adjust unmanned aerial vehicle to survey district ground feature overhead target height department, wherein, target height needs can guarantee that unmanned aerial vehicle shoots survey district ground feature.
Preferably, the target radius R of the spinning flight is estimated by R ≈ H × tan (θ), where H is the target height and θ is the inclination angle.
Preferably, step (4) comprises:
and aligning the camera to the feature object at the inclination angle according to the rotating flight path, adjusting the camera to place the feature object in the middle position of the image, performing ring shooting at a constant speed, and collecting image data.
Preferably, step (5) comprises:
and performing camera self-checking by using the image data and adopting a self-checking light beam adjustment method to robustly acquire relevant parameters of the camera.
According to another aspect of the invention, there is provided a robust self-calibration device for unmanned aerial vehicle camera based on rotating flight, comprising:
the device comprises a setting module, a control module and a control module, wherein the setting module is used for adjusting the unmanned aerial vehicle to a target height above a certain ground feature and setting the inclination angle of a camera when the unmanned aerial vehicle rotates and flies;
the path building module is used for estimating the target radius of the rotating flight according to the target height and the inclination angle and building a rotating flight path, wherein the circle center of the rotating flight path is the center of the feature, and the radius of the rotating flight path is the target radius;
the image acquisition module is used for aligning the camera to the feature object at the inclination angle for circular shooting according to the rotating flight path and acquiring image data;
and the self-checking module is used for performing camera self-checking by using the image data and steadily acquiring the related parameters of the camera.
Preferably, the setting module is used for adjusting the unmanned aerial vehicle to a target height above the ground feature of the survey area before or after the unmanned aerial vehicle for the survey area acquires the conventional image, wherein the target height needs to ensure that the unmanned aerial vehicle shoots the ground feature of the survey area.
Preferably, the target radius R of the spinning flight is estimated by R ≈ H × tan (θ), where H is the target height and θ is the inclination angle.
Preferably, the image acquisition module is specifically configured to align the camera with the feature at the inclination angle according to the rotating flight path, adjust the camera to place the feature in an intermediate position of an image, perform a circular shooting at a constant speed, and acquire image data.
According to another aspect of the invention, a computer-readable storage medium is provided, on which a computer program is stored, characterized in that the computer program realizes the steps of any of the methods described above when executed by a processor.
In general, compared with the prior art, the above technical solution contemplated by the present invention can achieve the following beneficial effects:
the method is simple to operate and easy to realize, and the stable self-checking parameters can be solved only by a small amount of data, so that the memory consumed during adjustment calculation can be greatly reduced, and the efficiency of self-checking processing is improved. Meanwhile, the invention fully considers the problem of strong correlation of the unknown number in the existing self-checking method, reduces the correlation of the unknown number in the equation by utilizing the inclination data based on the rotating flight and improves the precision of the self-checking.
Drawings
Fig. 1 is a schematic flowchart of a robust self-checking method for an unmanned aerial vehicle camera based on rotating flight according to an embodiment of the present invention;
fig. 2 is a schematic diagram of a position and an attitude of a camera when the unmanned aerial vehicle flies around the vehicle according to an embodiment of the present invention, where R is a rotating flying radius, H is a flying height of the unmanned aerial vehicle, and θ is a camera inclination angle;
fig. 3 is a flight path distribution diagram of an unmanned aerial vehicle during rotating flight according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram of an apparatus according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. In addition, the technical features involved in the embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.
The invention provides a method for the steady self-calibration of an unmanned aerial vehicle camera based on rotary flight, which aims to solve the problems that in the existing self-calibration method, the inner orientation elements of the camera have multiple resolvability and the self-calibration result is unstable due to the fact that strong correlation exists between the self-calibration parameters of the camera and the outer orientation elements of the image. The method has the core idea that before each flight task or after the completion of the task, a small number of images are shot and obtained in a rotary flight-based mode aiming at a ground local area on the height capable of ensuring that the ground and an object of a measuring area are shot, and then the self-checking beam method adjustment is independently carried out on the group of rotary flight data so as to solve and obtain stable camera self-checking parameters. Because the image shooting direction and angle obtained by rotating flight are changed greatly, the strong correlation between the inner orientation element and the outer orientation element of the image is broken, and therefore the self-checking and calculating precision of the inner orientation element of the camera is high.
Example one
Fig. 1 is a schematic flow chart of a robust self-checking method for an unmanned aerial vehicle camera based on rotating flight according to an embodiment of the present invention, where the method shown in fig. 1 includes the following steps:
s1: adjusting the unmanned aerial vehicle to a target height above a feature on a certain ground;
further, the step (1) comprises:
before surveying district with unmanned aerial vehicle and carrying out conventional image acquisition or after gathering, adjust unmanned aerial vehicle to survey district ground feature overhead target height department, wherein, target height needs can guarantee that unmanned aerial vehicle shoots survey district ground feature.
S2: setting the inclination angle of a camera when the unmanned aerial vehicle rotates and flies;
in the embodiment of the invention, the inclination angle can be determined according to actual conditions, and the embodiment of the invention does not perform unique determination.
In the present embodiment, the inclination angle is preferably 30 °.
S3: estimating the target radius of the rotary flight according to the target height and the inclination angle, and constructing a rotary flight path, wherein the circle center of the rotary flight path is the center of the feature, and the radius of the rotary flight path is the target radius;
as shown in fig. 2, in the embodiment of the present invention, the target radius R of the rotational flight may be estimated by R ≈ H ═ tan (θ), where H is the target height and θ is the inclination angle, where ≈ in the formula indicates that the value of the radius R is close to the value of H ≈ tan (θ), that is, the difference between the value of R and the value of H ≈ tan (θ) is within a preset range, and the preset range may be determined according to the actual situation.
(4) According to the rotating flight path, aligning a camera to the feature object at an inclined angle for circular shooting, and acquiring image data;
as shown in fig. 3, in the embodiment of the present invention, step (4) includes:
according to the rotating flight path, the camera is aligned to the feature object at an inclination angle theta, the camera is adjusted to place the feature object in the middle position of the image, the circular shooting is carried out at a constant speed, and image data are collected.
The target characteristic object is mainly used as a reference mark in the embodiment of the invention, so that an unmanned aerial vehicle operator can conveniently judge whether the flight track deviates and the camera attitude is opposite to the position.
(5) The image data is used for carrying out camera self-checking correction, and relevant parameters of the camera are acquired steadily.
In an embodiment of the present invention, step (5) includes:
the camera self-checking is carried out by utilizing the image data and adopting a self-checking light beam method adjustment method, and relevant parameters of the camera, such as parameters including focal length, image principal point offset, objective lens distortion and the like, are acquired steadily.
The method can effectively reduce the influence of the correlation between the self-checking parameters and the external orientation elements of the image during adjustment calculation, and obtain accurate self-checking results of the camera. Meanwhile, because the image data volume participating in the self-checking is small, the calculation memory required in the self-checking processing process can be greatly reduced, the self-checking processing time is reduced, and high-precision self-checking parameters are provided for subsequent large-scale data processing.
Example two
Fig. 4 is a schematic structural diagram of an apparatus according to an embodiment of the present invention, including:
the setting module 401 is used for adjusting the unmanned aerial vehicle to a target height above a certain ground feature and setting the inclination angle of a camera when the unmanned aerial vehicle rotates and flies;
a path construction module 402, configured to estimate a target radius of the rotating flight according to the target height and the inclination angle, and construct a rotating flight path, where a circle center of the rotating flight path is a center of the feature, and a radius of the rotating flight path is a target radius;
the image acquisition module 403 is configured to perform a circular shooting with the camera aiming at the feature object at an inclined angle according to the rotating flight path, and acquire image data;
the self-calibration module 404 is configured to perform camera self-calibration using the image data, and robustly obtain the relevant parameters of the camera.
Further, above-mentioned module 401 that sets up for before surveying district with unmanned aerial vehicle and carrying out conventional image acquisition or gather after, adjust unmanned aerial vehicle to survey the overhead target height department of district ground feature, wherein, the target height needs can guarantee that unmanned aerial vehicle shoots and surveys district ground feature.
Further, the target radius R of the spinning flight is estimated by R ≈ H × tan (θ), where H is the target height and θ is the inclination angle.
Further, the image acquisition module 403 is specifically configured to align the camera with the feature object at an inclined angle according to the rotating flight path, adjust the camera to place the feature object in the middle of the image, perform a circular shooting at a constant speed, and acquire image data.
The specific implementation of each module may refer to the description of the above method embodiment, and the embodiment of the present invention will not be repeated.
Example four
The present application also provides a computer-readable storage medium, such as a flash memory, a hard disk, a multimedia card, a card-type memory (e.g., SD or DX memory, etc.), a Random Access Memory (RAM), a Static Random Access Memory (SRAM), a read-only memory (ROM), an electrically erasable programmable read-only memory (EEPROM), a programmable read-only memory (PROM), a magnetic memory, a magnetic disk, an optical disk, a server, an App application mall, etc., on which a computer program is stored, which when executed by a processor implements the method for robust self-calibration of a drone camera based on rotational flight in the method embodiments.
It should be noted that, according to the implementation requirement, each step/component described in the present application can be divided into more steps/components, and two or more steps/components or partial operations of the steps/components can be combined into new steps/components to achieve the purpose of the present invention.
It will be understood by those skilled in the art that the foregoing is only a preferred embodiment of the present invention, and is not intended to limit the invention, and that any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (10)
1. The utility model provides an unmanned aerial vehicle camera is steady self-checking school method based on rotatory flight which characterized in that includes:
(1) adjusting the unmanned aerial vehicle to a target height above a feature on a certain ground;
(2) setting the inclination angle of a camera of the unmanned aerial vehicle during rotary flight;
(3) estimating the target radius of the rotating flight according to the target height and the inclination angle, and constructing a rotating flight path, wherein the circle center of the rotating flight path is the center of the feature, and the radius of the rotating flight path is the target radius;
(4) according to the rotating flight path, aligning the camera to the feature object at the inclination angle for circular shooting, and acquiring image data;
(5) and performing camera self-calibration by using the image data, and stably acquiring relevant parameters of the camera.
2. The method of claim 1, wherein step (1) comprises:
before surveying district with unmanned aerial vehicle and carrying out conventional image acquisition or after gathering, adjust unmanned aerial vehicle to survey district ground feature overhead target height department, wherein, target height needs can guarantee that unmanned aerial vehicle shoots survey district ground feature.
3. Method according to claim 1 or 2, characterized in that the target radius R of the rotating flight is estimated by R ≈ H tan (θ), where H is the target height and θ is the inclination angle.
4. The method of claim 3, wherein step (4) comprises:
and aligning the camera to the feature object at the inclination angle according to the rotating flight path, adjusting the camera to place the feature object in the middle position of the image, performing ring shooting at a constant speed, and collecting image data.
5. The method of claim 4, wherein step (5) comprises:
and performing camera self-checking by using the image data and adopting a self-checking light beam adjustment method to robustly acquire relevant parameters of the camera.
6. The utility model provides an unmanned aerial vehicle camera is steady from checking device based on rotatory flight which characterized in that includes:
the device comprises a setting module, a control module and a control module, wherein the setting module is used for adjusting the unmanned aerial vehicle to a target height above a certain ground feature and setting the inclination angle of a camera when the unmanned aerial vehicle rotates and flies;
the path building module is used for estimating the target radius of the rotating flight according to the target height and the inclination angle and building a rotating flight path, wherein the circle center of the rotating flight path is the center of the feature, and the radius of the rotating flight path is the target radius;
the image acquisition module is used for aligning the camera to the feature object at the inclination angle for circular shooting according to the rotating flight path and acquiring image data;
and the self-checking module is used for performing camera self-checking by using the image data and steadily acquiring the related parameters of the camera.
7. The apparatus of claim 6, wherein the setting module is configured to adjust the drone to a target height above the survey area ground feature before or after conventional image capture by the drone for the survey area, wherein the target height is required to ensure that the drone captures the survey area ground feature.
8. The device according to claim 6 or 7, characterized in that the target radius R of the spinning flight is estimated by R ≈ H · (θ), where H is the target height and θ is the inclination angle.
9. The apparatus according to claim 8, wherein the image capturing module is specifically configured to align the camera with the feature at the tilt angle according to the rotating flight path, adjust the camera to place the feature in an intermediate position of an image, perform a circular shooting at a constant speed, and capture image data.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any one of the preceding claims 1 to 5.
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| CN108415459A (en) * | 2018-05-23 | 2018-08-17 | 宜昌快马仕网络科技有限公司 | A kind of unmanned plane is around the circumvolant control method and device of target point |
| CN110799923A (en) * | 2018-07-20 | 2020-02-14 | 深圳市大疆创新科技有限公司 | Method for flying around points of interest and control terminal |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN104457710A (en) * | 2014-12-15 | 2015-03-25 | 重庆市勘测院 | Digital photogrammetry method based on non-metric digital camera |
| CN108351653A (en) * | 2015-12-09 | 2018-07-31 | 深圳市大疆创新科技有限公司 | System and method for UAV flight controls |
| CN108415459A (en) * | 2018-05-23 | 2018-08-17 | 宜昌快马仕网络科技有限公司 | A kind of unmanned plane is around the circumvolant control method and device of target point |
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Application publication date: 20200908 |