CN113920201A - Polar line geometric constraint fisheye camera calibration method - Google Patents
Polar line geometric constraint fisheye camera calibration method Download PDFInfo
- Publication number
- CN113920201A CN113920201A CN202110746242.5A CN202110746242A CN113920201A CN 113920201 A CN113920201 A CN 113920201A CN 202110746242 A CN202110746242 A CN 202110746242A CN 113920201 A CN113920201 A CN 113920201A
- Authority
- CN
- China
- Prior art keywords
- fisheye
- distortion
- points
- dimensional
- transformation matrix
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 42
- 239000011159 matrix material Substances 0.000 claims abstract description 32
- 230000009466 transformation Effects 0.000 claims abstract description 23
- 230000003287 optical effect Effects 0.000 claims abstract description 6
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 3
- 238000005259 measurement Methods 0.000 claims description 9
- 238000004364 calculation method Methods 0.000 claims description 5
- 230000007774 longterm Effects 0.000 claims 1
- 238000012423 maintenance Methods 0.000 claims 1
- 229910000831 Steel Inorganic materials 0.000 description 9
- 239000010959 steel Substances 0.000 description 9
- 229910052738 indium Inorganic materials 0.000 description 4
- APFVFJFRJDLVQX-UHFFFAOYSA-N indium atom Chemical compound [In] APFVFJFRJDLVQX-UHFFFAOYSA-N 0.000 description 4
- 238000003384 imaging method Methods 0.000 description 3
- 241000251468 Actinopterygii Species 0.000 description 2
- -1 Oxy Chemical class 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 2
- 238000001514 detection method Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 229920001651 Cyanoacrylate Polymers 0.000 description 1
- 238000012897 Levenberg–Marquardt algorithm Methods 0.000 description 1
- 239000004830 Super Glue Substances 0.000 description 1
- 238000009795 derivation Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000018109 developmental process Effects 0.000 description 1
- 238000009826 distribution Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
- 238000000605 extraction Methods 0.000 description 1
- 238000001914 filtration Methods 0.000 description 1
- 238000005286 illumination Methods 0.000 description 1
- 238000003709 image segmentation Methods 0.000 description 1
- 238000007689 inspection Methods 0.000 description 1
- 238000013178 mathematical model Methods 0.000 description 1
- 229910052751 metal Inorganic materials 0.000 description 1
- 239000002184 metal Substances 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 230000011218 segmentation Effects 0.000 description 1
- 239000007787 solid Substances 0.000 description 1
- 238000013519 translation Methods 0.000 description 1
- 238000009827 uniform distribution Methods 0.000 description 1
- 230000000007 visual effect Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/80—Analysis of captured images to determine intrinsic or extrinsic camera parameters, i.e. camera calibration
- G06T7/85—Stereo camera calibration
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/16—Matrix or vector computation, e.g. matrix-matrix or matrix-vector multiplication, matrix factorization
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T3/00—Geometric image transformations in the plane of the image
- G06T3/04—Context-preserving transformations, e.g. by using an importance map
- G06T3/047—Fisheye or wide-angle transformations
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Mathematical Physics (AREA)
- Mathematical Optimization (AREA)
- Mathematical Analysis (AREA)
- Computational Mathematics (AREA)
- Pure & Applied Mathematics (AREA)
- Data Mining & Analysis (AREA)
- Computing Systems (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Algebra (AREA)
- Databases & Information Systems (AREA)
- Software Systems (AREA)
- General Engineering & Computer Science (AREA)
- Image Processing (AREA)
Abstract
The invention discloses a fisheye camera calibration method based on epipolar geometric constraint, which comprises the following implementation steps: firstly, establishing an indoor three-dimensional calibration field, measuring the three-dimensional coordinates and fisheye image coordinates of artificial mark points, then establishing the relationship between fisheye image points and perspective projection image points through the relationship between fisheye cameras and projection functions of traditional optical cameras, and resolving the optimal projection transformation matrix and the basic matrix of the fisheye image points and the space three-dimensional coordinate points by using Levenberg-Marquardt (LM algorithm) to minimize errors; and then adding the radial distortion and tangential distortion model into an epipolar geometric constraint formula, solving radial and tangential distortion parameters by a Newton method, and correcting fisheye image points. And finally, calculating a projection transformation matrix and a basic matrix of the fisheye image points and the spatial three-dimensional coordinate points, and decomposing the projection transformation matrix to obtain the internal and external orientation elements. The method can calculate the internal parameters of the fisheye camera and improve the solving precision of the internal parameters.
Description
Technical Field
The invention relates to the technical field of photogrammetry, in particular to a fisheye camera calibration method which is suitable for fisheye camera three-dimensional reconstruction work.
Background
In the development process of photogrammetry for more than 150 years, all theoretical bases (such as collinearity equations, adjustment by a beam method, differential orthorectification and the like) are 'ordinary' aerial cameras (such as frame cameras and linear array sensors) based on perspective (central) projection, and relevant aerial image projective measurement processing is relatively mature. However, such cameras are designed based on vision, the observation range is generally limited to 40-50 degrees, the whole information of the surrounding environment cannot be obtained, and partial targets are difficult to find, namely, the targets easily "escape" from the visual field and the observation range. Therefore, international proposals have been made for "omnidirectional vision", i.e., obtaining panoramic information of a large field of view of 180 ° or 360 ° at a time. In recent years, the research on such cameras (such as 180-degree fisheye cameras) is internationally keen, and the cameras are gradually applied to the fields of robot navigation, star exploration, panoramic monitoring, public security, pipeline detection, auxiliary driving, field detection, vehicle-mounted inspection, aircraft guidance, virtual reality and the like.
The existing fisheye camera calibration method can be roughly divided into three types:
(1) fisheye camera calibration based on camera models (e.g., Zhang, y., Zhao, l., & Hu, W. (2013). advance of catadioptic uniformity camera calibration. international Journal of Information Technology and Computer Science (ijtcs), 5(3), 13.). Although the method is reasonable in derivation, the distortion of the camera has various factors, so that a very strict mathematical model cannot be constructed, the relation between a space point and an image point is not established, the method belongs to self calibration, and the actual application precision has uncertainty.
(2) Fisheye camera calibration based on imaging geometry (e.g., Schwalbe, E. (2005, February). Geotric modeling and calibration of fish lens camera systems. In Proc.2nd Panoramic photo metrology works, int. archives of photo metrology and Remote Sensing (Vol.36, No. part 5, p.W8)). The method can calculate calibration parameters more correctly, but only one fisheye image is used for calibration, so that the information of a single image is incomplete, some important view geometric information is easy to miss, and the method is not beneficial to three-dimensional reconstruction.
(3) Fisheye camera calibration based on single view geometric constraints (e.g., Fitzgibbon, A.W. (2001, Decumber). Simultaneous linearity estimation of multiple view geometry and lens translation. in Proceedings of the 2001IEEE Computer Society Conference on Computer Vision and Pattern registration. CVPR2001(Vol.1, pp.I-I). IEEE.). Most of the methods are calibrated by using the geometric constraint of a single image, but the distortion of a fisheye camera is not caused by a single factor, some constraints of the single image are not strictly established, and the methods do not use a spatial three-dimensional coordinate, belong to self-calibration and have uncertainty in the precision of practical application. The fisheye camera calibration of view geometry constraints has great limitations.
Aiming at the limitations of the three fisheye camera calibration methods, the invention discloses a fisheye camera calibration method based on polar line geometric constraint.
Disclosure of Invention
The invention discloses a fisheye camera calibration method based on epipolar line geometric constraint. In order to achieve the above purpose, the present invention adopts the following technical scheme, including the following steps.
And 2, deducing the mathematical relationship between the fisheye image point and the perspective projection image point by using the relationship between the equidistant projection function of the fisheye camera and the perspective projection function of the traditional optical camera, and then deducing the mathematical relationship between the fisheye image point and the spatial three-dimensional coordinate point, wherein the process needs to determine the initial value of the internal orientation element. And finally, solving a projection transformation matrix to obtain a basic matrix.
The method for determining the initial value of the internal orientation element comprises the following steps: since the fisheye camera cannot be manufactured without error, the fisheye image appears as an ellipse of an approximate circle. According to the characteristic of the fisheye camera, the images are fitted into a curve, and noise is removed. The centers and radii of the two images are estimated from the curve equation. Using optical principles, an initial value of the focal length can be calculated.
The projective transformation matrix determining method comprises the following steps: firstly, obtaining a mathematical relation between a fisheye image point and a perspective projection image point and an initial value of an internal orientation element, then using a plurality of spatial three-dimensional coordinate points and fisheye image points corresponding to the spatial three-dimensional coordinate points to calculate an optimal projection transformation matrix based on a Direct Linear Transformation (DLT) method and an LM algorithm, and calculating an initial value of a basic matrix through a polar line geometric theory formula.
And 3, adding the distortion model into the polar line geometric constraint formula, and solving the distortion parameter of the fisheye camera by using a Newton method.
Distortion parameter determination method: after the distortion parameters are added into the polar line geometric constraint formula, the initial value of the basic matrix and the initial value of the internal orientation element are solved, so that the constraint formula only has unknown distortion parameters, and the distortion parameters can be obtained by solving a nonlinear equation set by a Newton method.
And 4, correcting the fisheye image points by using distortion, solving a projection transformation matrix, and decomposing the projection transformation matrix to obtain inner and outer orientation elements.
Drawings
FIG. 1 is a flow chart of the present invention
FIG. 2 is an indoor three-dimensional calibration field of the present invention
FIG. 3 is a schematic diagram of the distortion of the fisheye lens of the present invention
FIG. 4 is a schematic diagram of the imaging process of the fisheye camera of the present invention
FIG. 5 is a fisheye image of an indoor calibration field according to the invention
FIG. 6 is a fish-eye image segmentation chart of the present invention
FIG. 7 is a graph of a curve fit of the present invention to determine the internal orientation element
FIG. 8 is a schematic view of epipolar geometry constraint according to the present invention
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, preferred embodiments are described below in detail with reference to the accompanying drawings.
The steps of the present invention will be described in further detail with reference to the accompanying drawings and examples.
For calibration purposes, a coordinate system is first established:
(1)(XW,YW,ZW) Is a space coordinate system OW-XWYWZWCoordinates of any point in the space, OWIs the origin of the spatial coordinate system.
(2)(XS,YS,ZS) Is the camera coordinate system OS-XSYSZSCoordinates of any point in the space, OSIs the origin of the camera coordinate system.
(3) (x, y) fisheye image coordinate system Oxy-any point in xy, OxyIs the origin of the coordinate system of the fisheye image.
(4) (u, v) is the coordinates of any point in the fisheye image pixel coordinate system O-uv, and O is the origin of the fisheye image pixel coordinate system.
(5) (x ', y ') is a virtual perspective projection image coordinate system O 'xy-coordinates of any point in x 'y', Ox'yIs the origin of the virtual perspective projection image coordinate system.
(6) (u ', v') is an arbitrary point in the pixel coordinate system O '-u' v 'of the virtual perspective projection image, and O' is the origin of the pixel coordinate system of the virtual perspective projection image.
(7)Oxy(u0,v0) Is the center of the fisheye image and the virtual perspective projection image,
radial direction
(see fig. 3).
With reference to fig. 1, the steps of the fisheye camera calibration method with epipolar geometric constraint are described.
The establishment of an indoor three-dimensional calibration field is described in connection with fig. 2. The calibration field is 6.5m long, 1.5m wide and 4.0m high, and has 30 steel columns in total, each steel column is uniformly provided with 10 ground control points (hereinafter abbreviated as GCP), the steel column distribution structure is three layers in front and at the back of uniform distribution, the distance between two adjacent steel columns is 0.75m, and meanwhile, an indium steel ruler is arranged below the calibration field and used as a standard ruler during measurement, the background wall is white, the illumination condition is good, and multi-angle shooting can be performed at different heights and different angles, as shown in fig. 2 (a). In order to observe all GCP as far as possible, two solid measuring stations with the height of 1.15m are respectively arranged at the position 4.8m in front of the left side and 3.2m in front of the right side of the calibration field to serve as measuring stations. The GCP artificial mark is a white square light-reflecting sticker of 40mm multiplied by 40mm, the center is a circle with the diameter of 40mm and alternating black and white, the center adopts a precise cross wire, the line width of the cross wire at the center of the GCP artificial mark is 0.5mm as shown in figure 2(c), not only is the precise observation convenient, but also the figure is beneficial to the extraction of the center of the GCP artificial mark of the digital photo. The GCP artificial mark is fixed on the metal rod by using the super glue, and the relative position of the GCP artificial mark is kept stable for a long time.
Establishing a free coordinate system with the indium steel ruler O in combination with the graph 21The point is taken as the origin O of the free coordinate systemWA right-handed coordinate system is used, as in fig. 2 (a). The plane coordinates of 2 measuring stations A, B are determined by a rear intersection method, and 4 points are uniformly selected on an indium steel ruler. The two Nikon CX-102 total stations are respectively erected on A, B two measurement stations, the horizontal angle and the vertical angle of three points on the indium steel ruler are respectively measured, the other point is used as a redundant observation value to be subjected to adjustment, 8 returns are measured, and the three-dimensional coordinates of the measurement stations are obtained through calculation based on a back intersection calculation formula and a triangle elevation calculation formula. And uniformly selecting a plurality of GCPs and Check Points (CP) on three layers of steel frames of the calibration field to carry out fisheye camera calibration experiments. The horizontal angle and the vertical angle of the GCP are measured at A, B two measuring stations respectively by using the same instrument, and the measured number is 4. And calculating three-dimensional coordinates of the GCP and the CP by using a forward intersection and triangulation elevation measurement formula.
And 2, deducing a mathematical relationship between the fisheye image point and the spatial three-dimensional coordinate point by using the relation between the equidistant projection function of the fisheye camera and the perspective projection function of the traditional optical camera, solving a projection transformation matrix, and calculating an initial value of the basic matrix by using the epipolar geometry theory. The specific implementation mode is as follows:
perspective projection model
r'＝ftanθ (1)
Equidistant projection model
r＝fθ (2)
θ is the angle of incidence of the incident ray and f is the focal length.
The relation between the fisheye image point and the perspective projection image point obtained by the fisheye camera equidistant projection model and the perspective projection model will be described with reference to fig. 4(a) and (b)
Obtaining a mathematical relation between the fisheye image point and the spatial three-dimensional coordinate point according to Direct Linear Transformation (DLT):
where H is a projective transformation matrix. Order to
Then, a Levenberg-Marquardt algorithm is used for minimizing the reprojection error, and the optimized projection transformation matrix is solved based on least square adjustment.
min(∑id(xi，HZi)2)
Suppose HLAnd HRThe first fisheye image and the second fisheye image are respectively projection transformation matrixes. H can be obtained by performing the same procedure as above using two fisheye imagesLAnd HR。
The basic matrix is obtained by calculation according to a polar line geometric constraint theoretical formula:
wherein
The initial values of the interior orientation elements required to solve equation (4) are described with reference to fig. 5, 6, and 7. The specific steps of solving the initial value of the internal orientation element are as follows:
because the fisheye camera cannot be manufactured without errors, the imaging of the fisheye image shows an ellipse which is approximate to a circle, according to the characteristic of the fisheye camera, curve fitting denoising is carried out by using the two images, and the centers and the radiuses of the two images are obtained according to a curve equation. Wherein fig. 5(a) and 5(b) are calibration field fisheye images taken in two orientations, fig. 6(a) and 6(b) are images which are first denoised by median filtering and then segmented according to the threshold segmentation principle, and fig. 7(a) and 7(b) are the results of the final fitting. Some curve edge points are selected in fig. 7(a) and 7(b) and then calculated according to the general equation of the ellipse and the optical principle of the focal length to obtain the initial value of the internal orientation element.
And 3, adding the distortion model into the polar line geometric constraint formula, and solving the distortion parameter of the fisheye camera by using a Newton iteration method. The specific embodiment is explained with reference to fig. 3:
distortion models for fisheye cameras are generally considered only two, the radial distortion model and the tangential distortion model are shown in fig. 3. Radial distortion model
Tangential distortion model
The coordinates of the undistorted fish-eye image point A can be obtained from the distortion model, see FIG. 3
In the above formula, k1，k2Is the radial distortion parameter, p1，p2Is the tangential distortion parameter.
Δ(u-u0)rAnd Δ (v-v)0)rRadial distortion in the u and v directions, respectively;
Δ(u-u0)tand Δ (v-v)0)tTangential distortions in the u and v directions, respectively;
(u(id)，v(id)) Is the coordinates of the undistorted fisheye image points.
The mathematical relationship between the fisheye image point and the perspective projection image point can obtain the perspective projection image coordinate without distortion:
of the above formula (u'(id)，v'(id)) Are the undistorted perspective projection image coordinates.
The distortion parameter calibration model of the fisheye camera can be obtained by adding the undistorted perspective projection image coordinates into the polar line geometric constraint formula
Of the above formula (u'L(id),v'L(id)) Is the virtual perspective projection image coordinate (u ') corresponding to the first fish-eye image'R(id),v'R(id)) Is the virtual perspective projection image coordinate corresponding to the second fisheye image.
And (5) unfolding and simplifying the calibration model (10) and solving by using a Newton method.
After the expansion, the equation is as follows,
the formula, A, B, C, D, E, G, I, J, L, M, N, S, U, Y and T are (U)0，v0，fu,fv) Constant of (u)0,v0,fu,fv) The value of (2) is obtained by the expression (4).
The solution process is as follows.
The expanded equation (11) is denoted as f (x), and the newton's iterative equation for solving the nonlinear system of equations is:
X(k+1)＝X(k)-f'(X(k))-1f(X(k)),k＝0,1,... (12)
namely, it is
In the formula, f' (X)(k)) Is the Jacobian matrix of f (X), with the initial value X ═ 0,0,0,0]T. Solving to obtain fish-eye camera distortion parameter X ═ k1,k2,p1,p2]T。
And 4, correcting the fisheye image points by using distortion, and solving a projection transformation matrix and a basic matrix. And decomposing the projection transformation matrix to obtain the internal and external orientation elements. The specific embodiment is as follows.
Correcting coordinates of fish eye image points by distortion parameters and solving projection transformation matrix H(id)(ideal projective transformation matrix):
[u'(id),v'(id),1]T＝H(id)Z (13)
order to
The inside orientation elements are:
and calculating to obtain the inner orientation element and the distortion parameter.
The above embodiments are merely illustrative, and not restrictive, and those skilled in the relevant art can make various changes and modifications without departing from the spirit and scope of the invention. Therefore, all equivalent technical solutions also belong to the scope of the present invention, and the protection scope of the present invention should be defined by the claims.
The technical contents not described in detail in the present invention are all known techniques.
Claims (5)
1. The fisheye camera calibration method based on epipolar geometric constraint is characterized by comprising the following steps:
step 1, establishing an indoor three-dimensional calibration field, carrying out coordinate measurement on artificial mark points in the calibration field, then carrying out shooting of a plurality of angles on the calibration field, and measuring fisheye image point coordinates of the artificial mark points on an image;
step 2, deducing a mathematical relationship between the fisheye image point and a spatial three-dimensional coordinate point by using the relationship between the equidistant projection function of the fisheye camera and the perspective projection function of the traditional optical camera, minimizing errors by using an LM algorithm to obtain an optimized projection transformation matrix, and calculating an initial value of a basic matrix by using the theory of polar line geometry;
step 3, adding the distortion model into a polar line geometric constraint formula, and solving distortion parameters of the fisheye camera by using a Newton iteration method;
and 4, correcting the fisheye image points by distortion, solving a projection transformation matrix, a basic matrix and a projection transformation matrix, and decomposing the projection transformation matrix to obtain inner and outer orientation elements.
2. The polar geometry constrained fisheye camera calibration method of claim 1, characterized in that: the method comprises the following steps that 1, an indoor high-precision three-dimensional calibration field with depth is established, long-term maintenance is needed, and a total station is used for carrying out high-precision measurement on the three-dimensional calibration field; the fish-eye image points are acquired finely due to the large distortion characteristic of the fish-eye images.
3. The polar geometry constrained fisheye camera calibration method of claim 1, characterized in that: and 2, deriving the relation between the coordinates of the fisheye image points and the space three-dimensional coordinates by using perspective projection as a medium and according to the relation between the coordinates of the perspective projection image and the space three-dimensional coordinates, wherein in the process, an initial value of an internal orientation element is determined firstly.
4. The polar geometry constrained fisheye camera calibration method of claim 1, characterized in that: and 3, adding the distortion model into an epipolar geometric constraint formula, wherein a basic matrix is a known number, the unknown number of the epipolar geometric constraint formula at the moment is only the distortion parameter of the camera, and the distortion parameter can be solved by using a plurality of high-precision homonymous image points.
5. The polar geometry constrained fisheye camera calibration method of claim 1, characterized in that: and 4, the solving precision of the inner and outer orientation elements is further improved, because the fisheye image points are not corrected in the process of solving the projection transformation matrix for the first time, and the calculated distortion-corrected fisheye image points are used for calculation in order to improve the precision.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110746242.5A CN113920201A (en) | 2021-07-01 | 2021-07-01 | Polar line geometric constraint fisheye camera calibration method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110746242.5A CN113920201A (en) | 2021-07-01 | 2021-07-01 | Polar line geometric constraint fisheye camera calibration method |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN113920201A true CN113920201A (en) | 2022-01-11 |
Family
ID=79232857
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110746242.5A Pending CN113920201A (en) | 2021-07-01 | 2021-07-01 | Polar line geometric constraint fisheye camera calibration method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113920201A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114862717A (en) * | 2022-05-11 | 2022-08-05 | 桂林理工大学 | Fisheye camera image pair checking and correcting method for simultaneously constraining orthogonal principal optical axis and collinear lateral axis of epipolar line |
| CN114972013A (en) * | 2022-05-11 | 2022-08-30 | 桂林理工大学 | Fisheye image fast orthorectification model with spherical geometry single transformation |
-
2021
- 2021-07-01 CN CN202110746242.5A patent/CN113920201A/en active Pending
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114862717A (en) * | 2022-05-11 | 2022-08-05 | 桂林理工大学 | Fisheye camera image pair checking and correcting method for simultaneously constraining orthogonal principal optical axis and collinear lateral axis of epipolar line |
| CN114972013A (en) * | 2022-05-11 | 2022-08-30 | 桂林理工大学 | Fisheye image fast orthorectification model with spherical geometry single transformation |
| CN114972013B (en) * | 2022-05-11 | 2023-10-13 | 桂林理工大学 | Fisheye image rapid orthorectification method based on spherical geometry single transformation |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN113436260B (en) | Mobile robot pose estimation method and system based on multi-sensor tight coupling | |
| CN110070615B (en) | Multi-camera cooperation-based panoramic vision SLAM method | |
| CN110296691B (en) | IMU calibration-fused binocular stereo vision measurement method and system | |
| JP5832341B2 (en) | Movie processing apparatus, movie processing method, and movie processing program | |
| CN114399554B (en) | Calibration method and system of multi-camera system | |
| CN109523595B (en) | Visual measurement method for linear angular spacing of building engineering | |
| US8655094B2 (en) | Photogrammetry system and method for determining relative motion between two bodies | |
| Chatterjee et al. | Algorithms for coplanar camera calibration | |
| CN103278138A (en) | Method for measuring three-dimensional position and posture of thin component with complex structure | |
| CN113744351B (en) | Underwater structure light measurement calibration method and system based on multi-medium refraction imaging | |
| Moussa | Integration of digital photogrammetry and terrestrial laser scanning for cultural heritage data recording | |
| CN113947638B (en) | Method for correcting orthographic image of fish-eye camera | |
| CN113920201A (en) | Polar line geometric constraint fisheye camera calibration method | |
| CN114998448B (en) | Multi-constraint binocular fisheye camera calibration and space point positioning method | |
| CN111879354A (en) | Unmanned aerial vehicle measurement system that becomes more meticulous | |
| Chen et al. | A novel calibration method between a camera and a 3D LiDAR with infrared images | |
| CN112270698A (en) | Non-rigid geometric registration method based on nearest curved surface | |
| CN113240749B (en) | Remote binocular calibration and ranging method for recovery of unmanned aerial vehicle facing offshore ship platform | |
| CN113902809A (en) | Method for jointly calibrating infrared camera and laser radar | |
| Wang et al. | Corners positioning for binocular ultra-wide angle long-wave infrared camera calibration | |
| CN117197241B (en) | Robot tail end absolute pose high-precision tracking method based on multi-eye vision | |
| Ji et al. | Comparison of two panoramic sensor models for precise 3d measurements | |
| WO2023159180A1 (en) | Single-pass 3d reconstruction of internal surface of pipelines using depth camera array | |
| CN107784666B (en) | Three-dimensional change detection and updating method for terrain and ground features based on three-dimensional images | |
| CN114663520B (en) | Double-camera combined calibration method and system for ultra-large-range vision measurement |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |