CN108154536A - The camera calibration method of two dimensional surface iteration - Google Patents
The camera calibration method of two dimensional surface iteration Download PDFInfo
- Publication number
- CN108154536A CN108154536A CN201711325128.5A CN201711325128A CN108154536A CN 108154536 A CN108154536 A CN 108154536A CN 201711325128 A CN201711325128 A CN 201711325128A CN 108154536 A CN108154536 A CN 108154536A
- Authority
- CN
- China
- Prior art keywords
- camera
- coordinate system
- image
- point
- 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
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
Landscapes
- Engineering & Computer Science (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Image Processing (AREA)
Abstract
The invention discloses a kind of camera calibration method of two dimensional surface iteration, it is characterized in that, after obtaining camera intrinsic parameter on the basis of the camera marking method measured in two-dimensional visual, by calibration plane around world coordinate system origin OWIt rotates a certain angle, intrinsic parameter is solved again, until the adjacent parameter acquired twice is less than certain threshold value.Optimization objective function is finally established, calibration result is optimized using Levenberg Marquardt algorithms.Compared with traditional measurement scaling method based on two-dimensional visual, it is contemplated that the distortion of camera lens in shooting process improves camera calibration precision.
Description
Technical field
The present invention relates to a kind of camera calibration methods of two dimensional surface iteration, belong to technical field of visual navigation.
Background technology
Camera calibration be in vision system one it is important the problem of and it is unmanned by vision positioning navigation realize
One key technology of autonomous driving, is all widely used in SLAM, three-dimensional reconstruction and visual odometry.
But in existing technology, camera lens is mostly the distortion that can occur slightly in shooting process, this will be yes
The stated accuracy of video camera is relatively low, so as to the accuracy of entire effect vision system.
Invention content
To solve the deficiencies in the prior art, the purpose of the present invention is to provide a kind of camera calibrations of two dimensional surface iteration
Method, it is contemplated that the distortion of camera lens in shooting process improves camera calibration precision.
In order to realize above-mentioned target, the present invention adopts the following technical scheme that：
A kind of camera calibration method of two dimensional surface iteration, it is characterized in that, include the following steps：
1) camera coordinate system and world coordinate system are established, and the conversion for establishing camera coordinate system and world coordinate system is closed
It is matrix；
2) image of collecting work plane；
3) transformational relation square is substituted by the space coordinate of any two points in working face and with the image coordinate of its picture point
Battle array acquires the intrinsic parameter of video camera；
4) working face, rotation alpha angle are turned about the Z axis, and calculates the rotation between world coordinate system and camera coordinate system
Torque battle array R；
5) after working face is rotated by a certain angle, above procedure is repeated, acquires the intrinsic parameter of video camera, Zhi Daoxiang again
The difference of camera intrinsic parameter that neighbour is calculated twice is less than threshold value, stops calculating, and then asks for last experimental data twice
Averagely as last result；
6) Optimization Steps 5) handling result, the n width images used in calibration process, the spy obtained from each image
The quantity of sign point is m, establishes following optimization objective functionWherein,
MinFor the Intrinsic Matrix of camera, k is the radial distortion factor, RiFor the spin matrix between the i-th width uncalibrated image and camera, ti
For the translation vector between the i-th width uncalibrated image and camera, pijIt is characterized point PjThe practical picture formed on the i-th width uncalibrated image
Point, andIt is PjOuter parameter where the intrinsic parameter and the i-th width uncalibrated image that come is calibrated is obtained
Camera model under the virtual picture point that is formed.
A kind of camera calibration method of aforementioned two dimensional surface iteration, it is characterized in that, the particular content of the step 1) is：
Camera coordinate system, camera coordinate system Z are established in camera light axis centerCAxis direction is parallel to camera light
Axis, and using the direction from video camera to scenery as principal direction, camera coordinate system XCAxis direction takes image coordinate to increase along horizontal
Direction be positive direction；
World coordinate system origin OWSelect the intersection point of camera light shaft centre line and working face, XwTo put in world coordinates
It is the coordinate of X-direction, YwTo put the coordinate in world coordinate system Y-direction, ZWAxis direction and camera coordinate system ZCAxis direction phase
Together, XWAxis direction and camera coordinate system XCAxis direction is identical；
The position of video camera and inside and outside parameter are fixed, and it is the outer ginseng spin matrix of video camera to have spin matrix R=I, R, and I is
3 rank unit matrixs, when camera coordinate system is set as stated above with world coordinate system, outer ginseng spin matrix R and unit square
Battle array I is equal, translation matrix p=[0,0, d]T, d is the optical axis center O in camera coordinate systemCTo the distance of working face；
According to the relationship of the coordinate of spatial point in the coordinate and world coordinate system of picture point under image pixel coordinates system：Wherein, picpointed coordinate (u, v) represents the picture respectively under image pixel coordinates system
Vegetarian refreshments columns in the picture and line number, s are invariant, and R is the outer ginseng spin matrix of camera, and t is that the outer ginseng of camera translates
Vector.
A kind of camera calibration method of aforementioned two dimensional surface iteration, it is characterized in that, the step 2) the specific steps are：
After obtaining uncalibrated image, image is pre-processed；
Binary conversion treatment is carried out by Threshold segmentation, obtains a width bianry image；
Utilize the pixel coordinate of each point in the picture obtained the characteristics of bianry image pixel value in working face.
A kind of camera calibration method of aforementioned two dimensional surface iteration, it is characterized in that, threshold value in the step 5) is according to taking the photograph
Camera application scenarios are previously set.
A kind of camera calibration method of aforementioned two dimensional surface iteration, it is characterized in that, the optimum target in the step 6)
Function, to optimizing, finally obtains the higher camera intrinsic parameter calibration of precision using Levenberg-Marquardt algorithms
As a result.
The advantageous effect that the present invention is reached：This method considers lens distortion in shooting process in calculating, so as to carry
High camera calibration precision.
Description of the drawings
Fig. 1 is the camera imaging model schematic diagram being distorted；
Fig. 2 is the coordinate system schematic diagram that monocular two-dimensional visual measures；
Fig. 3 is postrotational working face schematic diagram.
Specific embodiment
The invention will be further described below in conjunction with the accompanying drawings.Following embodiment is only used for clearly illustrating the present invention
Technical solution, and be not intended to limit the protection scope of the present invention and limit the scope of the invention.
This method is after obtaining camera intrinsic parameter on the basis of the camera marking method measured in two-dimensional visual, will to demarcate
Plane is around world coordinate system origin OWIt rotates a certain angle, intrinsic parameter is solved again, acquired twice until adjacent
Parameter is less than certain threshold value.Optimization objective function is finally established, calibration is tied using Levenberg-Marquardt algorithms
Fruit optimizes.
Under each point meaning in Fig. 1 enters：OW、XW、YW、ZWRepresent the origin and reference axis of world coordinate system, OC、XC、YC、
ZCRepresent the origin and reference axis of camera coordinate system, O1, X, Y be expressed as photo coordinate system, P (XW、YW、ZW) represent point P
Coordinate under world coordinate system, P (XC、YC、ZC) represent coordinates of the point P under camera coordinate system, POZ(0、0、ZW) represent point
P is in OCZCProjection on axis, Pu(Xu、Yu) represent ideal image points of the point P on imaging plane, Pd(Xd、Yd) represent point P into
Actual imaging point in image plane.
It is as follows：
1) camera coordinate system and world coordinate system are established, and the conversion for establishing camera coordinate system and world coordinate system is closed
It is matrix：
Camera coordinate system, Z are established in camera light axis centerCAxis direction is parallel to camera optical axis, and with from camera shooting
Machine to the direction of scenery be principal direction, XCIt is positive direction that axis direction, which takes the horizontal increased direction in image coordinate edge,；
World coordinate system origin OWSelect the intersection point of camera light shaft centre line and working face, ZWAxis direction and ZCAxis side
To identical, XWAxis direction and XCAxis direction is identical；
The position of video camera and inside and outside parameter are fixed, and have spin matrix R=I, translation matrix p=[0,0, d]T, d is optical axis
Center OCTo the distance of working face；
According to the relationship of the coordinate of spatial point in the coordinate and world coordinate system of picture point under image pixel coordinates system：Picpointed coordinate (u, v) represents the pixel and exists respectively under image pixel coordinates system
Columns and line number in image, s are invariant, and R is the outer ginseng spin matrix of camera, and t is the outer ginseng translation vector of camera.
2) image of collecting work plane：
After obtaining uncalibrated image, image is pre-processed, is eliminated in shooting process because of illumination variation or other factors
The noise of generation；
Binary conversion treatment is carried out by Threshold segmentation, obtains a width bianry image；
Utilize the pixel coordinate of each point in the picture obtained the characteristics of bianry image pixel value in working face.
3) it is substituted by the space coordinate of any two points (point 1 and point 2) in working face and with the image coordinate of its picture point
Transformational relation matrix, acquires the intrinsic parameter of video camera, and solving result is：kxIt represents on u direction
Scale factor, kyRepresent the scale factor on v directions, u0、v0Represent point under image pixel coordinates system (u, v coordinate system)
Coordinate, d are the optical axis center O in camera coordinate systemCTo the distance of working face, (u1,v1) represent that point 1 is sat in image pixel
Coordinate under mark system (u, v coordinate system), (u2,v2) represent 2 coordinate under image pixel coordinates system (u, v coordinate system) of point,
(XW1,YW1) represent 1 coordinate under world coordinate system of point, (XW2,YW2) represent 2 coordinate under world coordinate system of point.
4) working face is turned about the Z axis, rotation alpha angle, as shown in figure 3, and calculating world coordinate system and camera coordinates
Spin matrix R between system,
5) after working face is rotated by a certain angle, above procedure is repeated, acquires the intrinsic parameter of video camera, Zhi Daoxiang again
The difference of camera intrinsic parameter that neighbour is calculated twice is less than threshold value (threshold value is previously set according to video camera application scenarios), stops
It calculates, then asks for the average as last result of last experimental data twice；
6) Optimization Steps 5) handling result, the n width images used in calibration process, the spy obtained from each image
The quantity of sign point is m, establishes following optimization objective function：Its
In, pijIt is characterized point PjThe actual image point formed on the i-th width uncalibrated image, andIt is PjIt is calibrating
The virtual picture point formed under the camera model that outer parameter where the intrinsic parameter and the i-th width uncalibrated image that come is obtained.
Optimization objective function, to optimizing, it is higher to finally obtain precision using Levenberg-Marquardt algorithms
Camera intrinsic parameter calibration result.
The above is only the preferred embodiment of the present invention, it is noted that for the ordinary skill people of the art
For member, without departing from the technical principles of the invention, several improvement and deformation can also be made, these are improved and deformation
Also it should be regarded as protection scope of the present invention.
Claims (6)
1. a kind of camera calibration method of two dimensional surface iteration, it is characterized in that, include the following steps：
1) camera coordinate system and world coordinate system are established, and establishes the transformational relation square of camera coordinate system and world coordinate system
Battle array；
2) image of collecting work plane；
3) transformational relation matrix is substituted by the space coordinate of any two points in working face and with the image coordinate of its picture point, asked
Obtain the intrinsic parameter M of video camerain；
4) the Z axis rotation work plane in world coordinate system, rotation alpha angle, and calculate world coordinate system and camera coordinates
Spin matrix R between system；
5) after working face is rotated by a certain angle, above procedure is repeated, the intrinsic parameter of video camera is acquired again, until adjacent two
The difference of the secondary camera intrinsic parameter being calculated is less than threshold value, stops calculating, then asks for being averaged for last experimental data twice
As last result；
6) Optimization Steps 5) handling result, the n width images used in calibration process, the characteristic point obtained from each image
Quantity for m, establish following optimization objective functionWherein, MinFor
The Intrinsic Matrix of camera, k be the radial distortion factor, RiFor the spin matrix between the i-th width uncalibrated image and camera, tiIt is i-th
Translation vector between width uncalibrated image and camera, pijIt is characterized point PjThe actual image point formed on the i-th width uncalibrated image, andIt is PjThe camera shooting that outer parameter where the intrinsic parameter and the i-th width uncalibrated image that come is calibrated is obtained
The virtual picture point formed under machine model, | | | | representing matrix norm.
2. a kind of camera calibration method of two dimensional surface iteration according to claim 1, it is characterized in that, the tool of the step 1)
Hold in vivo and be：
Camera coordinate system, camera coordinate system Z are established in camera light axis centerCAxis direction is parallel to camera optical axis, and with
Direction from video camera to scenery is principal direction, camera coordinate system XCAxis direction takes the image coordinate to be along horizontal increased direction
Positive direction；
World coordinate system origin OWSelect the intersection point of camera light shaft centre line and working face, XwTo put in world coordinate system X side
To coordinate, YwTo put the coordinate in world coordinate system Y-direction, ZWAxis direction and camera coordinate system ZCAxis direction is identical, XWAxis
Direction and camera coordinate system XCAxis direction is identical；
The position of video camera and inside and outside parameter are fixed, and it is the outer ginseng spin matrix of video camera to have spin matrix R=I, R, and I is 3 ranks
Unit matrix, when camera coordinate system is set as stated above with world coordinate system, outer ginseng spin matrix R and unit matrix I
It is equal, translation matrix p=[0,0, d]T, d is the optical axis center O in camera coordinate systemCTo the distance of working face；
According to the relationship of the coordinate of spatial point in the coordinate and world coordinate system of picture point under image pixel coordinates system：Wherein, picpointed coordinate (u, v) represents the picture respectively under image pixel coordinates system
Vegetarian refreshments columns in the picture and line number, s are invariant, and R is the outer ginseng spin matrix of camera, and t is that the outer ginseng of camera translates
Vector.
3. a kind of camera calibration method of two dimensional surface iteration according to claim 1, it is characterized in that, the tool of the step 2)
Body step is：
After obtaining uncalibrated image, image is pre-processed；
Binary conversion treatment is carried out by Threshold segmentation, obtains a width bianry image；
Utilize the pixel coordinate of each point in the picture obtained the characteristics of bianry image pixel value in working face.
4. a kind of camera calibration method of two dimensional surface iteration according to claim 3, it is characterized in that, the pretreatment uses
Filtering and noise reduction.
5. a kind of camera calibration method of two dimensional surface iteration according to claim 1, it is characterized in that, in the step 5)
Threshold value is previously set according to video camera application scenarios.
6. a kind of camera calibration method of two dimensional surface iteration according to claim 1, it is characterized in that, in the step 6)
Optimization objective function, to optimizing, finally obtains the higher video camera of precision using Levenberg-Marquardt algorithms
Intrinsic parameter calibration result.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201711325128.5A CN108154536A (en) | 2017-12-13 | 2017-12-13 | The camera calibration method of two dimensional surface iteration |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201711325128.5A CN108154536A (en) | 2017-12-13 | 2017-12-13 | The camera calibration method of two dimensional surface iteration |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN108154536A true CN108154536A (en) | 2018-06-12 |
Family
ID=62466723
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201711325128.5A Pending CN108154536A (en) | 2017-12-13 | 2017-12-13 | The camera calibration method of two dimensional surface iteration |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN108154536A (en) |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109993802A (en) * | 2019-04-03 | 2019-07-09 | 浙江工业大学 | A kind of Hybrid camera scaling method in urban environment |
| CN110298890A (en) * | 2019-06-24 | 2019-10-01 | 西北工业大学 | A kind of light-field camera scaling method based on Planck parametrization |
| CN110610523A (en) * | 2018-06-15 | 2019-12-24 | 杭州海康威视数字技术股份有限公司 | Automobile look-around calibration method and device and computer readable storage medium |
| CN110930458A (en) * | 2019-10-22 | 2020-03-27 | 同济大学 | Simple Nao robot camera external parameter calibration method |
| CN111145268A (en) * | 2019-12-26 | 2020-05-12 | 四川航天神坤科技有限公司 | Video registration method and device |
| GB2581792A (en) * | 2019-02-25 | 2020-09-02 | Mo-Sys Engineering Ltd | Lens calibration system |
| CN111968177A (en) * | 2020-07-22 | 2020-11-20 | 东南大学 | Mobile robot positioning method based on fixed camera vision |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105139411A (en) * | 2015-09-24 | 2015-12-09 | 大连理工大学 | Large visual field camera calibration method based on four sets of collinear constraint calibration rulers |
| CN206460516U (en) * | 2017-01-24 | 2017-09-01 | 长沙全度影像科技有限公司 | A kind of multichannel fisheye camera binocular calibration device |
| CN107192348A (en) * | 2016-03-14 | 2017-09-22 | 武汉小狮科技有限公司 | A kind of high-precision 3D vision measuring methods |
-
2017
- 2017-12-13 CN CN201711325128.5A patent/CN108154536A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105139411A (en) * | 2015-09-24 | 2015-12-09 | 大连理工大学 | Large visual field camera calibration method based on four sets of collinear constraint calibration rulers |
| CN107192348A (en) * | 2016-03-14 | 2017-09-22 | 武汉小狮科技有限公司 | A kind of high-precision 3D vision measuring methods |
| CN206460516U (en) * | 2017-01-24 | 2017-09-01 | 长沙全度影像科技有限公司 | A kind of multichannel fisheye camera binocular calibration device |
Cited By (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110610523A (en) * | 2018-06-15 | 2019-12-24 | 杭州海康威视数字技术股份有限公司 | Automobile look-around calibration method and device and computer readable storage medium |
| GB2581792A (en) * | 2019-02-25 | 2020-09-02 | Mo-Sys Engineering Ltd | Lens calibration system |
| GB2581792B (en) * | 2019-02-25 | 2023-01-04 | Mo Sys Engineering Ltd | Lens calibration system |
| CN109993802A (en) * | 2019-04-03 | 2019-07-09 | 浙江工业大学 | A kind of Hybrid camera scaling method in urban environment |
| CN109993802B (en) * | 2019-04-03 | 2020-12-25 | 浙江工业大学 | Hybrid camera calibration method in urban environment |
| CN110298890A (en) * | 2019-06-24 | 2019-10-01 | 西北工业大学 | A kind of light-field camera scaling method based on Planck parametrization |
| CN110298890B (en) * | 2019-06-24 | 2022-09-06 | 西北工业大学 | Light field camera calibration method based on Planck parameterization |
| CN110930458A (en) * | 2019-10-22 | 2020-03-27 | 同济大学 | Simple Nao robot camera external parameter calibration method |
| CN110930458B (en) * | 2019-10-22 | 2023-05-02 | 同济大学 | Simple Nao robot camera external parameter calibration method |
| CN111145268A (en) * | 2019-12-26 | 2020-05-12 | 四川航天神坤科技有限公司 | Video registration method and device |
| CN111145268B (en) * | 2019-12-26 | 2023-10-31 | 四川航天神坤科技有限公司 | Video registration method and device |
| CN111968177A (en) * | 2020-07-22 | 2020-11-20 | 东南大学 | Mobile robot positioning method based on fixed camera vision |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108154536A (en) | The camera calibration method of two dimensional surface iteration | |
| CN106091984B (en) | A kind of three dimensional point cloud acquisition methods based on line laser | |
| CN110197466B (en) | Wide-angle fisheye image correction method | |
| CN111243033B (en) | Method for optimizing external parameters of binocular camera | |
| CN103198487B (en) | A kind of automatic marking method for video monitoring system | |
| CN110782394A (en) | Panoramic video rapid splicing method and system | |
| CN111028155B (en) | Parallax image splicing method based on multiple pairs of binocular cameras | |
| CN110889829B (en) | Monocular distance measurement method based on fish eye lens | |
| CN104778656B (en) | Fisheye image correcting method based on spherical perspective projection | |
| CN110009690A (en) | Binocular stereo vision image measuring method based on polar curve correction | |
| CN113129430B (en) | Underwater three-dimensional reconstruction method based on binocular structured light | |
| CN102567989A (en) | Space positioning method based on binocular stereo vision | |
| CN110060304B (en) | Method for acquiring three-dimensional information of organism | |
| CN111524195B (en) | Camera calibration method in positioning of cutting head of heading machine | |
| CN104463791A (en) | Fisheye image correction method based on spherical model | |
| CN109859137B (en) | Wide-angle camera irregular distortion global correction method | |
| CN110807815B (en) | Quick underwater calibration method based on corresponding vanishing points of two groups of mutually orthogonal parallel lines | |
| CN116129037B (en) | Visual touch sensor, three-dimensional reconstruction method, system, equipment and storage medium thereof | |
| CN112232319A (en) | Scanning splicing method based on monocular vision positioning | |
| CN105513074B (en) | A kind of scaling method of shuttlecock robot camera and vehicle body to world coordinate system | |
| CN108269234A (en) | A kind of lens of panoramic camera Attitude estimation method and panorama camera | |
| CN114998448A (en) | Method for calibrating multi-constraint binocular fisheye camera and positioning space point | |
| CN112258581A (en) | On-site calibration method for multi-fish glasses head panoramic camera | |
| CN112288801A (en) | Four-in-one self-adaptive tracking shooting method and device applied to inspection robot | |
| CN116402904A (en) | Combined calibration method based on laser radar inter-camera and monocular camera |
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 | ||
| WD01 | Invention patent application deemed withdrawn after publication | ||
| WD01 | Invention patent application deemed withdrawn after publication |
Application publication date: 20180612 |