CN117422776A - Underwater vision camera calibration device and method for non-parametric distortion model - Google Patents
Underwater vision camera calibration device and method for non-parametric distortion model Download PDFInfo
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- G—PHYSICS
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- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
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Abstract
The invention provides an underwater vision camera calibration device and method of a non-parametric distortion model, which belong to the technical field of underwater camera calibration, and the underwater vision camera calibration device and method of the non-parametric distortion model comprises the following steps: installing a calibration device into a measurement view field of a vision system, and collecting a calibration image; calibrating a camera of the underwater vision system by using a calibration image and an ideal pinhole model, and calculating a re-projection error by using a calibration result so as to obtain a distorted field; fitting and calibrating the reprojection error by using a spline surface function, and further constructing a non-parametric imaging distortion correction model; combining an ideal pinhole model and a distortion correction model to establish a complete imaging model of the underwater vision system; carrying out integral one-time calculation on all camera parameters of the underwater vision system by utilizing a beam adjustment algorithm to obtain nonlinear optimization of the beam adjustment; the invention can solve the problem that the traditional parameterized optical distortion model cannot adapt to an underwater vision system.
Description
Technical Field
The invention belongs to the technical field of underwater camera calibration, and particularly relates to an underwater vision camera calibration device and method of a non-parametric distortion model.
Background
Underwater three-dimensional reconstruction has become one of the hot spots in marine exploration research. There are a number of techniques for obtaining distance and geometric information in underwater environments, such as SONAR navigation ranging (Sound Navigation and Ranging, SONAR) and optical detection and ranging (Underwater Optics, cebrian 2016). Underwater photometry technologies mainly include stereoscopic vision (Zhang Menghui 2018), simultaneous localization and mapping (Simultaneous Localization and Mapping, SLAM), laser triangulation (Barrett 2018), and the like. The accuracy of camera calibration directly affects the accuracy of underwater vision reconstruction. For underwater vision measurement, the camera is usually required to be sealed in a waterproof shell, and an optical glass seal is arranged in front of the camera, so that light is inevitably refracted when passing through water, glass and air, and the camera is accurately calibrated, which can be a great challenge. The camera calibration of the underwater vision system mainly comprises three types: the first method directly adopts a traditional small-hole imaging model, and does not provide a proper refraction model, so that the real position of the measured object cannot be obtained. The second type of method uses a refraction model in which a camera is placed in the air and a measured object is placed under water. Because the influence of the waterproof cover is not considered, the calculation is simplified, and the method is only suitable for areas with shallow water and low water pollution. The third type of method uses a refraction model in which a camera is placed in water and a measured object is placed under water. The problems of insufficient underwater environment light and high calculation amount and poor solving stability are solved, and the internal and external parameters of the camera can be obtained.
In summary, the accuracy of the current underwater optical three-dimensional reconstruction is still not high, and an important reason is that the refractive distortion generated by light passing through three media of water body, waterproof glass and air in practical underwater application seriously affects the calibration accuracy of an imaging system, and the problem is difficult to be substantially solved by directly adopting an imaging mathematical model commonly used in air and an existing calibration method/algorithm.
Disclosure of Invention
In view of the above, the invention provides an underwater vision camera calibration device and method for a non-parametric distortion model, which can solve the problem that the traditional parametric optical distortion model cannot adapt to an underwater vision system.
The invention is realized in the following way:
the invention provides an underwater vision camera calibration device of a non-parametric distortion model, which comprises a first calibration surface and a second calibration surface, wherein an included angle is formed by the first calibration surface and the second calibration surface, a calibration point is arranged on the outer side of the included angle, and the calibration point is used for reflecting light.
On the basis of the technical scheme, the underwater vision camera calibration device of the non-parametric distortion model can be further improved as follows:
the calibration device is located in the measuring range of the vision system.
Further, the standard points are blue-green directional reflection mark points.
Due to the strong absorption and scattering of light in the underwater environment, the penetrability of visible light is very low, especially in the deep water environment. The blue-green wavelength light has better propagation performance in water, so the reflective mark points can provide visibility for underwater navigation.
Further, the included angle is 120-170 degrees.
The second aspect of the present invention provides a method for calibrating an underwater vision camera with a non-parametric distortion model, wherein the method comprises the following steps:
s10: installing a calibration device into a measuring view field of a motion turntable visual system, starting a camera of an underwater visual system, and collecting calibration images, wherein the calibration images are five groups;
s20: calibrating a camera of the underwater vision system by using the calibration image and the ideal pinhole model, and calculating a re-projection error by using the calibration result so as to obtain a distorted field;
s30: fitting and calibrating the re-projection error by using a spline surface function, and further constructing a non-parametric imaging distortion correction model;
s40: combining the ideal pinhole model and the distortion correction model to establish a complete imaging model of the underwater vision system;
s50: and (3) carrying out integral one-time calculation on all camera parameters of the underwater vision system by using a beam adjustment algorithm to obtain nonlinear optimization of the beam adjustment.
On the basis of the technical scheme, the underwater vision camera calibration method of the non-parametric distortion model can be further improved as follows:
further, the distortion correction model is obtained through a successive super relaxation algorithm.
Further, the step of fitting the calibrated re-projection error by using a spline surface function to construct a non-parametric imaging distortion correction model specifically comprises the following steps:
constructing spline curves of a distortion field, wherein the spline curves are respectivelyDirection and->A direction;
fitting the image points by using spline curves to obtain a distortion correction model after fitting, wherein the distortion correction model comprisesDirection and->A direction;
and optimizing the distortion correction model.
Further, the combination of the ideal pinhole model and the distortion correction model establishes a complete imaging model of the underwater vision system, wherein the complete imaging model of the underwater vision system is as follows:
；
wherein,for the distortion correction model +.>Is a matrix of in-camera parameters of the underwater vision system, < >>The camera projection matrix is used to represent the parameters outside the camera. />For the coordinates of three-dimensional points in space>The coordinates of the corresponding underwater two-dimensional image points are obtained.
Further, the method utilizes a beam adjustment algorithm to carry out integral one-time calculation on all camera parameters of the underwater vision system to obtain beam adjustment nonlinear optimization, wherein the optimization targets are as follows:
；
wherein,indicating the calibration plate->Projection of the individual marker points to +.>Obtaining a reprojection error on the individual image planes, < >>Indicate->Projection matrix of each camera, < >>Representing the parameter matrix in the camera,/->Representing an out-of-camera parameter matrix,/->A distortion matrix.
Further, in the optimizing the distortion correction model, the optimizing target is:
；
wherein,for the parameter vector of the distortion correction model, +.>，/>Indicate->Image point reprojection calculated +.>Directional distortion value +_>Representing a distortion correction model.
Compared with the prior art, the underwater vision camera calibration device and method for the non-parametric distortion model provided by the invention have the beneficial effects that:
1. accurate modeling and correction: parameterized distortion models are generally able to model the distortion characteristics of a camera system more accurately. By selecting a proper parameterized form, the actual distortion scene can be better fitted, and the calibration accuracy is improved. This is critical for precision tasks and measurements in underwater vision systems, such as positioning and navigation of underwater robots;
2. fast calculation and real-time: parameterized distortion models typically have compact mathematical expressions that make distortion correction easier in real-time applications. For underwater robots or other real-time underwater applications, such as underwater detection, search and rescue tasks, real-time is a critical factor. The parameterized model generally has lower computational complexity, which is helpful for maintaining the real-time performance of the system;
3. utilization of a priori knowledge: the parameterized distortion model may take advantage of a priori knowledge about the system characteristics. By knowing the form of distortion that may be present in the underwater environment, an appropriate parameterized model can be selected more specifically. This helps to increase the robustness of the system, making it more adaptable to the distortion characteristics of a particular underwater environment;
4. consistency with traditional photogrammetry: in certain scientific and engineering applications, consistency with traditional photogrammetry methods is required. Parameterized distortion models are generally more in line with the framework of these traditional methods so that the underwater vision system can be better integrated with other ground or surface applications, forming a more comprehensive system;
5. easy to understand and explain: parameterized distortion models typically employ parameters that have intuitive physical meaning, which makes understanding and interpretation of the model easier. This is important to engineers, researchers, and operators because they can better understand the distortion behavior of the system and adjust and optimize it as needed;
6. adapting to a specific application scenario: in some underwater application scenarios, there may be special distortion characteristics that need to be captured by specially designed parameterized models. The parameterized distortion model is adopted, so that the system can be more flexibly adapted to the requirements of the special scenes, and the performance of the system is improved;
7. continuation of the traditional calibration method: if the underwater vision system needs to be fused with the land vision system, the traditional calibration method can be better continued by adopting the parameterized distortion model. This is very important for those systems that need to be switched between land and underwater, as a similar calibration framework can be used.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the description of the embodiments of the present invention will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
In the drawings, the list of components represented by the various numbers is as follows:
FIG. 1 is a schematic diagram of an underwater vision camera calibration device for a non-parametric distortion model;
FIG. 2 is a flow chart of a method for calibrating an underwater vision camera with a non-parametric distortion model;
FIG. 3 is a complete imaging model of an underwater vision system of an underwater vision camera calibration method of a non-parametric distortion model;
1. a first calibration surface; 2. a second calibration surface; 3. and (5) marking points.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.
Referring to fig. 1, in a first embodiment of an underwater vision camera calibration device for a non-parametric distortion model according to a first aspect of the present invention, in this embodiment, the device includes a first calibration surface 1 and a second calibration surface 2, where the first calibration surface 1 and the second calibration surface 2 form an included angle, a calibration point 3 is disposed outside the included angle, and the calibration point 3 is designed by using a directional reflective material and is used for providing calibration three-dimensional information.
In the technical scheme, the calibration device is positioned in the measuring range of the vision system.
Further, in the above technical solution, the calibration point 3 is a blue-green directional reflection mark point.
Further, in the above technical solution, the included angle is 120 °.
As shown in fig. 2, which is a first embodiment of an underwater vision camera calibration method for a non-parametric distortion model according to a second aspect of the present invention, in this embodiment, an underwater vision camera calibration device for a non-parametric distortion model includes the following steps:
s10: installing a calibration device into a measuring view field of a motion turntable visual system, starting a camera of an underwater visual system, and collecting calibration images, wherein the calibration images are five groups;
s20: calibrating a camera of the underwater vision system by using a calibration image and an ideal pinhole model, and calculating a re-projection error by using a calibration result so as to obtain a distorted field;
s30: fitting and calibrating the reprojection error by using a spline surface function, and further constructing a non-parametric imaging distortion correction model;
s40: combining an ideal pinhole model and a distortion correction model to establish a complete imaging model of the underwater vision system;
s50: and (3) carrying out integral one-time calculation on all camera parameters of the underwater vision system by using a beam adjustment algorithm to obtain nonlinear optimization of the beam adjustment.
Further, in the above technical solution, the distortion correction model is obtained by a successive super relaxation algorithm.
And carrying out Gaussian filtering, gray level binarization, edge detection, center positioning, code identification and other treatments on all the acquired calibration images to obtain the two-dimensional image coordinates of the reflective marker points.
Furthermore, in the above technical solution, the spline surface function is used to fit the calibrated re-projection error, so as to construct a non-parametric imaging distortion correction model, which specifically includes the following steps:
the distortion field can be represented by two B-spline curves:
；
wherein,，/>，/>and->Are respectively->And->Spline node number of direction, +.>For B-spline times, +.>,/>Is spline surface->Spline coefficients->And->Spline surface +.>And->A basis function of direction.
Using B-spline functions for measured image points（/>) Coordinates->And->（/>) Coordinates of->Fitting, wherein->Is a matrix of parameters in the camera,>representing a camera projection matrix, < >>Representing three-dimensional point coordinates in the world coordinate system.
Assume thatThe distortion correction models in the two directions are respectively: />And->Fitting data is +.>Personal coordinate point->And->（/>) Then:
（2）
wherein:、/>respectively is a function->And->Parameter vector of>、/>Spline coefficients representing spline surfaces, +.>、/>Is a basis function.
Fitting is an approximate process forThe direction fitting and optimization targets are as follows:
（3）
wherein:，/>representing an optimized objective function ∈ ->The representation represents +.>Image point reprojection calculated +.>Directional distortion values.
Order theSolving the extremum of equation (3) can be accomplished by solving the bias derivative, namely:
（4）
wherein:，/>。
order the，
。
The derivation according to equation (4) can be:
（5）
（6）
from (7), an image can be calculatedThe parameter vector of the direction distortion correction model is obtained>Distortion correction model of direction, similarly, can calculate +.>A distortion correction model of the direction.
Furthermore, in the above technical solution, by combining the ideal pinhole model and the distortion correction model, the method specifically includes:
let the distortion correction model function beA complete imaging model of the underwater vision system shown in fig. 3 can be constructed. Wherein,is a matrix of parameters in the camera,>the camera projection matrix is used to represent the parameters outside the camera. />For the coordinates of three-dimensional points in space>The coordinates of the corresponding underwater two-dimensional image points are obtained. The mathematical model is as follows:
（7）
further, in the above technical solution, the method for obtaining the nonlinear optimization of the beam adjustment includes the steps of:
calibrating the imaging model of the vision system by using a beam adjustment algorithm, namely solving，/>And->. The solving process is a nonlinear optimization process aiming at all the re-projection errors, and the optimization targets are expressed as follows:
（8）
wherein,indicating the calibration plate +.>Projection of the individual marker points to +.>Obtaining a reprojection error on the individual image planes, < >>Indicate->Projection matrix of each camera, < >>Representing the parameter matrix in the camera,/->Representing a matrix of parameters outside the camera,a distortion matrix. Due to the three-dimensional coordinates of the marker points on the calibration device +.>The accuracy of (2) is greatly affected by the level of the manufacturing process and is not a fixed value, so the actual optimization process also optimizes the beam adjustment together.
Further, in the above technical solution, in optimizing the distortion correction model, the objective of optimization is:
；
wherein,for the parameter vector of the distortion correction model, +.>，/>Indicate->Image point reprojection calculated +.>Directional distortion value +_>Representing a distortion correction model.
Specifically, the principle of the invention is as follows: installing a calibration device into a measuring view field of a motion turntable visual system, starting a camera of an underwater visual system, and collecting calibration images, wherein the calibration images are five groups; calibrating a camera of the underwater vision system by using the calibration image and the ideal pinhole model, and calculating a re-projection error by using the calibration result so as to obtain a distorted field; fitting and calibrating the re-projection error by using a spline surface function, and further constructing a non-parametric imaging distortion correction model; combining the ideal pinhole model and the distortion correction model to establish a complete imaging model of the underwater vision system; and (3) carrying out integral one-time calculation on all camera parameters of the underwater vision system by using a beam adjustment algorithm to obtain nonlinear optimization of the beam adjustment.
Claims (10)
1. The underwater vision camera calibration device of the non-parametric distortion model is characterized by comprising a first calibration surface (1) and a second calibration surface (2), wherein an included angle is formed by the first calibration surface (1) and the second calibration surface (2), a calibration point (3) is arranged on the outer side of the included angle, and the calibration point (3) is used for reflecting light.
2. An underwater vision camera calibration apparatus for a non-parametric distortion model as in claim 1, wherein the calibration apparatus is located within the vision system measurement range.
3. An underwater vision camera calibration device for a non-parametric distortion model as claimed in claim 2, wherein the calibration points (3) are blue-green retro-reflective marker points.
4. An underwater vision camera calibration device for a non-parametric distortion model as in claim 3, wherein the included angle is 120 ° to 170 °.
5. An underwater vision camera calibration method of a non-parametric distortion model, characterized by comprising the underwater vision camera calibration device of the non-parametric distortion model according to any one of claims 1-4, the method comprising the following steps:
s10: installing a calibration device into a measuring view field of a motion turntable visual system, starting a camera of an underwater visual system, and collecting calibration images, wherein the calibration images are five groups;
s20: calibrating a camera of the underwater vision system by using the calibration image and the ideal pinhole model, and calculating a re-projection error by using the calibration result so as to obtain a distorted field;
s30: fitting and calibrating the re-projection error by using a spline surface function, and further constructing a non-parametric imaging distortion correction model;
s40: combining the ideal pinhole model and the distortion correction model to establish a complete imaging model of the underwater vision system;
s50: and (3) carrying out integral one-time calculation on all camera parameters of the underwater vision system by using a beam adjustment algorithm to obtain nonlinear optimization of the beam adjustment.
6. The method for calibrating an underwater vision camera with a non-parametric distortion model as set forth in claim 5, wherein the distortion correction model is obtained by a successive super relaxation algorithm.
7. The method for calibrating an underwater vision camera with a non-parametric distortion model according to claim 6, wherein the fitting of the calibrated re-projection error by using a spline surface function further constructs a non-parametric imaging distortion correction model, and specifically comprises the following steps:
constructing spline curves of a distorted field, wherein the spline curves are in an x direction and a y direction respectively;
fitting the image points by using a spline curve to obtain a distortion correction model after fitting, wherein the distortion correction model comprises an x direction and a y direction;
and optimizing the distortion correction model.
8. The method for calibrating an underwater vision camera with a non-parametric distortion model according to claim 7, wherein the combination of the ideal pinhole model and the distortion correction model creates a complete imaging model of an underwater vision system, and the complete imaging model of the underwater vision system is:
；
wherein,for the distortion correction model +.>Is a matrix of in-camera parameters of the underwater vision system, < >>Representing camera external parameters for a camera projection matrix; />For the coordinates of three-dimensional points in space>The coordinates of the corresponding underwater two-dimensional image points are obtained.
9. The method for calibrating an underwater vision camera with a non-parametric distortion model according to claim 8, wherein the method is characterized in that all camera parameters of the underwater vision system are resolved integrally and once by using a beam adjustment algorithm to obtain a beam adjustment nonlinear optimization, and the optimization objective is:
；
wherein,indicating the calibration plate->Projection of the individual marker points to +.>Obtaining a reprojection error on the individual image planes, < >>Indicate->Projection matrix of each camera, < >>Representing the parameter matrix in the camera,/->Representing an out-of-camera parameter matrix,/->A distortion matrix.
10. The method for calibrating an underwater vision camera with a non-parametric distortion model according to claim 9, wherein in the optimization of the distortion correction model, the optimization targets are:
；
wherein,for the parameter vector of the distortion correction model, +.>，/>Indicate->Image point reprojection calculated +.>Directional distortion value +_>Representing a distortion correction model.
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