CN101901502B - Global optimal registration method of multi-viewpoint cloud data during optical three-dimensional measurement - Google Patents
Global optimal registration method of multi-viewpoint cloud data during optical three-dimensional measurement Download PDFInfo
- Publication number
- CN101901502B CN101901502B CN2010102553612A CN201010255361A CN101901502B CN 101901502 B CN101901502 B CN 101901502B CN 2010102553612 A CN2010102553612 A CN 2010102553612A CN 201010255361 A CN201010255361 A CN 201010255361A CN 101901502 B CN101901502 B CN 101901502B
- Authority
- CN
- China
- Prior art keywords
- data
- registration
- vector
- viewpoint
- under test
- 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.)
- Expired - Fee Related
Links
Images
Landscapes
- Length Measuring Devices By Optical Means (AREA)
Abstract
The invention relates to a global optimal registration method of multi-viewpoint cloud data during optical three-dimensional measurement, belonging to the technical field of digital image processing and solving the problem of measured deviation of common mark points in a characteristic mark point measurement method. A data registration process comprises the following steps of: registering data to be registered to target data to obtain registration result data, wherein the registration result data is used as the target data for next registration; selecting an ith viewpoint as a viewpoint to be registered, wherein two-dimensional measurement data of an object to be measured of the viewpoint is used as data to be registered; then repeating the data registration process until all viewpoints are used as the viewpoints to be converted to finish data registration, wherein coordinate transformation vectors to be optimized are calculated by utilizing a bundle adjustment method in the registration process. The invention realizes the global optimal registration of the multi-viewpoint cloud data during the optical three-dimensional measurement and is suitable for three-dimensional measurement of objects to be measured.
Description
Technical field
The invention belongs to the digital image processing techniques field, be specifically related to the global optimization method for registering of multi-viewpoint cloud data in a kind of optical three-dimensional measurement.
Background technology
Optical three-dimensional measurement is the cutting edge technology of information optics research; Advantages such as having noncontact, do not have to destroy, the data acquisition speed is fast, simple to operate; Therefore, the optical three-dimensional measurement technology is widely used in fields such as machine vision, processing automatically, industrial online detection, production quality control, profiling in kind, biomedicines.But; Receive the restriction of measurement environment, testee and measuring equipment itself; Single measurement can only obtain the data of testee limited area, and the partial data that obtain the testee profile need take multiple measurements through different the taking measurement of an angle of conversion, also claims to look measurement more.Because the variation that at every turn takes measurement of an angle; What cause obtaining looks measurement data more and can't directly unify under a coordinate system; The measurement data unification of a plurality of viewpoints this process under the same coordinate system is referred to as registration, at present, realizes that registration mainly adopts following several method:
(1), extracts the shape facility method: from the multi-viewpoint cloud data that records, extract characteristics such as corresponding curvature or shape, utilize these characteristics to realize looking the registration of data more.But this process operand is big, arithmetic speed is slow, operational precision is low, and some object under test characteristic is not obvious, and therefore, adopting this method that object under test is carried out three-dimensional measurement has significant limitation, and reliability does not guarantee.
(2), servicing unit mensuration: detect the relative motion between testee and the measuring equipment by servicing units such as joint arm, motion platforms, directly obtain to look the coordinate transform relation between data more.But the use of servicing unit has not only improved the cost of whole measuring system, and has increased its complexity.And the working range of measuring system is restricted, can't carry out three-dimensional measurement to more large-scale testee.
(3), signature point registration method: make the signature point on the testee surface, under the same coordinate system, carry out the coordinate unification, guarantee that at least three public characteristic gauge points can be identified in twice measurement for realizing two groups of measurement data.But, receive the influence of observation angle, when repeatedly measuring same public sign point, there is certain deviation in the identification of common indicium point, cause occurring propagation of error, make the result of registration have cumulative errors.
Summary of the invention
The objective of the invention is the problem that the measurement of common indicium point existed deviation for solving in the signature point measurement method, the global optimization method for registering of multi-viewpoint cloud data in a kind of optical three-dimensional measurement is provided.
The present invention is achieved through following proposal; The global optimization method for registering of multi-viewpoint cloud data in the optical three-dimensional measurement; Said method is pasted with gauge point as object under test signature point based on a hardware system platform that includes harvester and Computer Control Unit on the object under test, Computer Control Unit control harvester is gathered the measurement data information of object under test respectively from N viewpoint; Obtain the two-dimensional measurement data of N group object under test; Therefrom extract the two-dimensional measurement data of N group object under test signature point, it is reverted to the 3 d measurement data of N group object under test signature point, wherein; N is the viewpoint number that harvester is gathered object under test, and N is the integer greater than 1;
The 3 d measurement data of the object under test signature point of N viewpoint collection carries out the method for registration:
As target view, second viewpoint be as viewpoint to be converted with first viewpoint, from the 3 d measurement data of the object under test signature point of the 3 d measurement data of the object under test signature point of target view and viewpoint to be converted, identifies M
1Individual object under test public characteristic gauge point, the 3 d measurement data of the object under test public characteristic gauge point of said target view are as target data, and the 3 d measurement data of the object under test public characteristic gauge point of said viewpoint to be converted is as treating registration data;
The process of carrying out the data registration is: will treat that registration data carries out registration to target data, obtain the registration result data, with these registration result data as the target data of registration next time;
Select i viewpoint as viewpoint to be converted, from the 3 d measurement data of the object under test signature point of the 3 d measurement data of the object under test signature point of target view and viewpoint to be converted, identify M
iIndividual object under test public characteristic gauge point; The 3 d measurement data of the object under test public characteristic gauge point of said viewpoint to be converted is as treating registration data; Repeat above-mentioned data registration process then, till all viewpoints are all accomplished the data registration as viewpoint to be converted, wherein; I=3,4 ... N, M
1, M
iFor greater than 2 integer;
Described method from registration to target data that will treat that registration data is carried out is specially:
Step 1, basis are treated registration data and target data, and Computer Control Unit calculates M
iIndividual public characteristic gauge point is from treating rotation matrix and the translation vector of registration data when target data is carried out registration; With the stored in form of described rotation matrix with unit quaternion; And the real component of unit quaternion is for just, and unit quaternion and translation vector constitute the coordinate transform vector;
Step 2, basis are treated registration data and target data, with M
iThe registration data of treating of individual public characteristic gauge point projects on the pairing plane of target view, obtains M
iThe two-dimensional coordinate of individual projected image public characteristic gauge point makes the two-dimensional coordinate of a projected image public characteristic gauge point do
, this public characteristic gauge point is x at the coordinate of the two-dimensional measurement data of target view, with the coordinate transform vector that obtains in the step 1 as coordinate transform to be optimized vector p
0
Step 3, according to the imaging principle, the two-dimensional coordinate of projected image public characteristic gauge point
With coordinate transform vector p to be optimized
0Relation table be shown
Calculating the public characteristic gauge point is that the two-dimensional coordinate of x and projected image public characteristic gauge point does at the coordinate of the two-dimensional measurement data of target view
Between deviation do
Wherein, f () is a projection function;
Whether the deviation e described in step 4, the determining step three is in predefined permissible variation scope, and judged result is for being, execution in step six, judged result be not for, execution in step five;
Step 5, utilize the light beam method of adjustment to calculate the variable quantity δ of coordinate transform vector to be optimized
p, revise coordinate transform vector p to be optimized
0, revised coordinate transform to be optimized vector p
k=p
0+ δ
p, and with described revised coordinate transform to be optimized vector p
kBe defined as coordinate transform vector p to be optimized
0, return step 3;
Step 6, with coordinate transform to be optimized vector p
0Be defined as the coordinate transform vector p after the optimization, execution in step seven;
Step 7, according to the coordinate transform after the optimization that obtains in step 6 vector p, will treat that registration data carries out registration, obtain the registration result data.
The number of the M that identifies a public characteristic gauge point described in the present invention is at least 3, in order to guarantee realizing that the coordinate of many group measurement data is unified.
The real component of the unit quaternion described in the step 3 of the present invention is for just; Adopt limiting mode of the present invention can guarantee the relation one to one of the rotation matrix and the former rotation matrix of Quaternion Representation; Former rotation matrix representation is in calculating process; The trigonometric function of rotation matrix exists ambiguity and singularity, is prone to cause the number of times of iterative computation to increase, even not convergent situation can occur.
Global optimization method for registering of the present invention is put the registration method with signature and is combined with the light beam method of adjustment; In the registration process of per two groups of measurement data; All the measured deviation of common indicium point is eliminated; Make it in allowed limits, and then reach and reduce in the registration process of many viewpoints measurement data, because the cumulative errors that propagation of error produces.Adopt method of the present invention not increase the complexity of system; Only on original optical three-dimensional measurement system-based, utilize the initial value of three-dimensional feature monumented point calculating coordinate change, utilize the accurate calculating coordinate change vector sum of light beam method of adjustment three-dimensional symbol point coordinate.The present invention has that operand is little, reliability is high, measurement range is unrestricted, measuring method simply is easy to advantages such as realization.
Description of drawings
Fig. 1 is that the embodiment one described registration data of will treating is carried out the process flow diagram of the method for registration to target data; Fig. 2 is the embodiment four described variable quantity δ that utilize the light beam method of adjustment to calculate coordinate transform vector to be optimized
pThe process flow diagram of method.
Embodiment
Embodiment one: specify this embodiment below in conjunction with Fig. 1.The global optimization method for registering of multi-viewpoint cloud data in the optical three-dimensional measurement; Said method is pasted with gauge point as object under test signature point based on a hardware system platform that includes harvester and Computer Control Unit on the object under test, Computer Control Unit control harvester is gathered the measurement data information of object under test respectively from N viewpoint; Obtain the two-dimensional measurement data of N group object under test; Therefrom extract the two-dimensional measurement data of N group object under test signature point, it is reverted to the 3 d measurement data of N group object under test signature point, wherein; N is the viewpoint number that harvester is gathered object under test, and N is the integer greater than 1;
The 3 d measurement data of the object under test signature point of N viewpoint collection carries out the method for registration:
As target view, second viewpoint be as viewpoint to be converted with first viewpoint, from the 3 d measurement data of the object under test signature point of the 3 d measurement data of the object under test signature point of target view and viewpoint to be converted, identifies M
1Individual object under test public characteristic gauge point, the 3 d measurement data of the object under test public characteristic gauge point of said target view are as target data, and the 3 d measurement data of the object under test public characteristic gauge point of said viewpoint to be converted is as treating registration data;
The process of carrying out the data registration is: will treat that registration data carries out registration to target data, obtain the registration result data, with these registration result data as the target data of registration next time;
Select i viewpoint as viewpoint to be converted, from the 3 d measurement data of the object under test signature point of the 3 d measurement data of the object under test signature point of target view and viewpoint to be converted, identify M
iIndividual object under test public characteristic gauge point; The 3 d measurement data of the object under test public characteristic gauge point of said viewpoint to be converted is as treating registration data; Repeat above-mentioned data registration process then, till all viewpoints are all accomplished the data registration as viewpoint to be converted, wherein; I=3,4 ... N, M
1, M
iFor greater than 2 integer;
Described method from registration to target data that will treat that registration data is carried out is specially:
Step 1, basis are treated registration data and target data, and Computer Control Unit calculates M
iIndividual public characteristic gauge point is from treating rotation matrix and the translation vector of registration data when target data is carried out registration; With the stored in form of described rotation matrix with unit quaternion; And the real component of unit quaternion is for just, and unit quaternion and translation vector constitute the coordinate transform vector;
Step 2, basis are treated registration data and target data, with M
iThe registration data of treating of individual public characteristic gauge point projects on the pairing plane of target view, obtains M
iThe two-dimensional coordinate of individual projected image public characteristic gauge point makes the two-dimensional coordinate of a projected image public characteristic gauge point do
This public characteristic gauge point is x at the coordinate of the two-dimensional measurement data of target view, with the coordinate transform vector that obtains in the step 1 as coordinate transform to be optimized vector p
0
Step 3, according to the imaging principle, the two-dimensional coordinate of projected image public characteristic gauge point
With coordinate transform vector p to be optimized
0Relation table be shown
Calculating the public characteristic gauge point is that the two-dimensional coordinate of x and projected image public characteristic gauge point does at the coordinate of the two-dimensional measurement data of target view
Between deviation do
Wherein, f () is a projection function;
Whether the deviation e described in step 4, the determining step three is in predefined permissible variation scope, and judged result is for being, execution in step six, judged result be not for, execution in step five;
Step 5, utilize the light beam method of adjustment to calculate the variable quantity δ of coordinate transform vector to be optimized
p, revise coordinate transform vector p to be optimized
0, revised coordinate transform to be optimized vector p
k=p
0+ δ
p, and with described revised coordinate transform to be optimized vector p
kBe defined as coordinate transform vector p to be optimized
0, return step 3;
Step 6, with coordinate transform to be optimized vector p
0Be defined as the coordinate transform vector p after the optimization, execution in step seven;
Step 7, according to the coordinate transform after the optimization that obtains in step 6 vector p, will treat that registration data carries out registration, obtain the registration result data.
The number of the M that identifies a public characteristic gauge point described in this embodiment is at least 3, in order to guarantee realizing that the coordinate of many group measurement data is unified.
The real component of the unit quaternion described in this embodiment step 1 is for just; Adopt the described limiting mode of this embodiment can guarantee the relation one to one of the rotation matrix and the former rotation matrix of Quaternion Representation; Former rotation matrix representation is in calculating process; The trigonometric function of rotation matrix exists ambiguity and singularity, is prone to cause the number of times of iterative computation to increase, even not convergent situation can occur.
The described global optimization method for registering of this embodiment is put the registration method with signature and is combined with the light beam method of adjustment; In the registration process of per two groups of measurement data; All the measured deviation of common indicium point is eliminated; Make it in allowed limits, and then reach and reduce in the registration process of many viewpoints measurement data, because the cumulative errors that propagation of error produces.Adopt the described method of this embodiment not increase the complexity of system; Only on original optical three-dimensional measurement system-based, utilize the initial value of three-dimensional feature monumented point calculating coordinate change, utilize the accurate calculating coordinate change vector sum of light beam method of adjustment three-dimensional symbol point coordinate.This embodiment has that operand is little, reliability is high, measurement range is unrestricted, measuring method simply is easy to advantages such as realization.
Embodiment two: this embodiment is that described harvester is the CCD camera to the further qualification of the global optimization method for registering of multi-viewpoint cloud data in the embodiment one described optical three-dimensional measurement.
Embodiment three: this embodiment is the further qualification to the global optimization method for registering of multi-viewpoint cloud data in the embodiment one described optical three-dimensional measurement; In the step 1; Described rotation matrix is one 3 * 3 a matrix; Described translation vector is one 3 * 1 a vector, and described unit quaternion is one 4 * 1 a vector, and the described coordinate transform vector that is made up of unit quaternion and translation vector is one 7 * 1 a vector.
Embodiment four: specify this embodiment below in conjunction with Fig. 2.This embodiment is the further qualification to the global optimization method for registering of multi-viewpoint cloud data in the embodiment one described optical three-dimensional measurement, in the step 5, and the described variable quantity δ that utilizes the light beam method of adjustment to calculate coordinate transform vector to be optimized
pMethod be:
Step May Day, at coordinate transform vector p to be optimized
0The place is for the variable quantity δ of coordinate transform vector to be optimized
p, with projection function f (p
0+ δ
p) expand into single order Taylor polynomial expression:
f(p
0+δ
p)≈f(p
0)+Jδ
p
Wherein, J is a Jacobi matrix
Described projection function f (p
0+ δ
p), be revised coordinate transform to be optimized vector p
kProjection in the plane;
The two-dimensional coordinate x of the target data of step 5 two, calculating public characteristic gauge point and revised coordinate transform to be optimized vector p
kProjection f (p in the plane
0+ δ
p) difference, || x-f (p
0+ δ
p) || ≈ || x-f (p
0)-J δ
p||=|| e-J δ
p||
Step 5 three, for to make described in the step 5 two || x-f (p
0+ δ
p) || minimum then makes e-J δ
pWith J quadrature, i.e. J
T(e-J δ
p)=0;
Equality J in step the May 4th, the arrangement step 5 three
T(e-J δ
p)=0, the acquisition form is following: J
TJ δ
p=J
TE;
Step 5 five, convert the equality after the arrangement that obtains in the step the May 4th into enhancement mode equation, (J
TJ+ μ I) δ
p=J
TE,
Wherein, I is a unit matrix, and μ is the unit matrix coefficient, and μ＞0;
Step 5 six, according to the enhancement mode equation that obtains in step the Seventh Five-Year Plan, the variable quantity δ of coordinate transform to be optimized vector
pBe δ
p=-(J
TJ+ μ I)
-1J
TE.
Adopt the described variable quantity δ that utilizes the light beam method of adjustment to calculate coordinate transform vector to be optimized of this embodiment
pMethod, have calculated amount little, be easy to realize, reliability is high, the advantage of error free transmission.
Embodiment five: this embodiment is the further supplementary notes to the global optimization method for registering of multi-viewpoint cloud data in the embodiment four described optical three-dimensional measurements, and is in the step 5, further comprising the steps of:
Record utilizes the light beam method of adjustment to calculate the variable quantity δ of coordinate transform vector to be optimized
pOperation times, when described operation times surpasses preset value, carry out END instruction.
Utilize the light beam method of adjustment to calculate the variable quantity δ of coordinate transform vector to be optimized in this embodiment
p, as the variable quantity δ that calculates coordinate transform vector to be optimized
pOperation times when surpassing preset value, jump out circulation.
Claims (3)
1. the global optimization method for registering of multi-viewpoint cloud data in the optical three-dimensional measurement; Said method is pasted with gauge point as object under test signature point based on a hardware system platform that includes harvester and Computer Control Unit on the object under test, Computer Control Unit control harvester is gathered the measurement data information of object under test respectively from N viewpoint; Obtain the two-dimensional measurement data of N group object under test; Therefrom extract the two-dimensional measurement data of N group object under test signature point, it is reverted to the 3 d measurement data of N group object under test signature point, wherein; N is the viewpoint number that harvester is gathered object under test, and N is the integer greater than 1;
The 3 d measurement data of the object under test signature point of N viewpoint collection carries out the method for registration:
As target view, second viewpoint be as viewpoint to be converted with first viewpoint, from the 3 d measurement data of the object under test signature point of the 3 d measurement data of the object under test signature point of target view and viewpoint to be converted, identifies M
1Individual object under test public characteristic gauge point, the 3 d measurement data of the object under test public characteristic gauge point of said target view are as target data, and the 3 d measurement data of the object under test public characteristic gauge point of said viewpoint to be converted is as treating registration data;
The process of carrying out the data registration is: will treat that registration data carries out registration to target data, obtain the registration result data, with these registration result data as the target data of registration next time;
Select i viewpoint as viewpoint to be converted, from the 3 d measurement data of the object under test signature point of the 3 d measurement data of the object under test signature point of target view and viewpoint to be converted, identify M
iIndividual object under test public characteristic gauge point; The 3 d measurement data of the object under test public characteristic gauge point of said viewpoint to be converted is as treating registration data; Repeat above-mentioned data registration process then, till all viewpoints are all accomplished the data registration as viewpoint to be converted, wherein; I=3,4 ... N, M
1, M
iFor greater than 2 integer;
It is characterized in that: described method from registration to target data that will treat that registration data is carried out is specially:
Step 1, basis are treated registration data and target data, and Computer Control Unit calculates M
iIndividual public characteristic gauge point is from treating rotation matrix and the translation vector of registration data when target data is carried out registration; With the stored in form of described rotation matrix with unit quaternion; And the real component of unit quaternion is for just, and unit quaternion and translation vector constitute the coordinate transform vector;
Step 2, basis are treated registration data and target data, with M
iThe registration data of treating of individual public characteristic gauge point projects on the pairing plane of target view, obtains M
iThe two-dimensional coordinate of individual projected image public characteristic gauge point makes the two-dimensional coordinate of a projected image public characteristic gauge point do
, this public characteristic gauge point is x at the coordinate of the two-dimensional measurement data of target view, with the coordinate transform vector that obtains in the step 1 as coordinate transform to be optimized vector p
0
Step 3, according to the imaging principle, the two-dimensional coordinate of projected image public characteristic gauge point
With coordinate transform vector p to be optimized
0Relation table be shown
Calculating the public characteristic gauge point is that the two-dimensional coordinate of x and projected image public characteristic gauge point does at the coordinate of the two-dimensional measurement data of target view
Between deviation do
Wherein, f () is a projection function;
Whether the deviation e described in step 4, the determining step three is in predefined permissible variation scope, and judged result is for being, execution in step six, judged result be not for, execution in step five;
Step 5, utilize the light beam method of adjustment to calculate the variable quantity δ of coordinate transform vector to be optimized
p, revise coordinate transform vector p to be optimized
0, revised coordinate transform to be optimized vector p
k=p
0+ δ
p, and with described revised coordinate transform to be optimized vector p
kBe defined as coordinate transform vector p to be optimized
0, return step 3;
Step 6, with coordinate transform to be optimized vector p
0Be defined as the coordinate transform vector p after the optimization, execution in step seven;
Step 7, according to the coordinate transform after the optimization that obtains in step 6 vector p, will treat that registration data carries out registration, obtain the registration result data,
In the step 5, the described variable quantity δ that utilizes the light beam method of adjustment to calculate coordinate transform vector to be optimized
pMethod be:
Step May Day, at coordinate transform vector p to be optimized
0The place is for the variable quantity δ of coordinate transform vector to be optimized
p, with projection function f (p
0+ δ
p) expand into single order Taylor polynomial expression:
f(p
0+δ
p)≈f(p
0)+Jδ
p
Wherein, J is a Jacobi matrix
Described projection function f (p
0+ δ
p), be revised coordinate transform to be optimized vector p
kProjection in the plane;
The two-dimensional coordinate x of the target data of step 5 two, calculating public characteristic gauge point and revised coordinate transform to be optimized vector p
kProjection f (p in the plane
0+ δ
p) difference, || x-f (p
0+ δ
p) || ≈ || x-f (p
0)-J δ
p||=|| e-J δ
p||;
Step 5 three, for to make described in the step 5 two || x-f (p
0+ δ
p) || minimum then makes e-J δ
pWith J quadrature, i.e. J
T(e-J δ
p)=0;
Equality J in step the May 4th, the arrangement step 5 three
T(e-J δ
p)=0, the acquisition form is following: J
TJ δ
p=J
TE;
Step 5 five, convert the equality after the arrangement that obtains in the step the May 4th into enhancement mode equation, (J
TJ+ μ I) δ
p=J
TE,
Wherein, I is a unit matrix, and μ is the unit matrix coefficient, and μ＞0;
Step 5 six, according to the enhancement mode equation that obtains in the step 5 five, the variable quantity δ p of coordinate transform to be optimized vector is δ
p=-(J
TJ+ μ I)
-1J
TE.
2. the global optimization method for registering of multi-viewpoint cloud data in the optical three-dimensional measurement according to claim 1 is characterized in that: described harvester is the CCD camera.
3. the global optimization method for registering of multi-viewpoint cloud data in the optical three-dimensional measurement according to claim 1; It is characterized in that: in the step 1; Described rotation matrix is one 3 * 3 a matrix; Described translation vector is one 3 * 1 a vector, and described unit quaternion is one 4 * 1 a vector, and the described coordinate transform vector that is made up of unit quaternion and translation vector is one 7 * 1 a vector.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN2010102553612A CN101901502B (en) | 2010-08-17 | 2010-08-17 | Global optimal registration method of multi-viewpoint cloud data during optical three-dimensional measurement |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN2010102553612A CN101901502B (en) | 2010-08-17 | 2010-08-17 | Global optimal registration method of multi-viewpoint cloud data during optical three-dimensional measurement |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN101901502A CN101901502A (en) | 2010-12-01 |
| CN101901502B true CN101901502B (en) | 2012-05-02 |
Family
ID=43227008
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN2010102553612A Expired - Fee Related CN101901502B (en) | 2010-08-17 | 2010-08-17 | Global optimal registration method of multi-viewpoint cloud data during optical three-dimensional measurement |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN101901502B (en) |
Families Citing this family (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9189702B2 (en) * | 2012-12-31 | 2015-11-17 | Cognex Corporation | Imaging system for determining multi-view alignment |
| CN105180830B (en) * | 2015-09-28 | 2017-09-01 | 浙江大学 | A kind of three-dimensional point cloud autoegistration method and system suitable for ToF cameras |
| CN106683105B (en) * | 2016-12-02 | 2020-05-19 | 深圳市速腾聚创科技有限公司 | Image segmentation method and image segmentation device |
| CN106846467B (en) * | 2017-01-23 | 2020-05-05 | 阿依瓦（北京）技术有限公司 | Entity scene modeling method and system based on optimization of position of each camera |
| CN108253911B (en) * | 2018-01-29 | 2019-10-11 | 西南交通大学 | A kind of workpiece pose method of adjustment based on measurement point geometrical characteristic iteration registration |
| CN109087339A (en) * | 2018-06-13 | 2018-12-25 | 武汉朗视软件有限公司 | A kind of laser scanning point and Image registration method |
| CN113486928B (en) * | 2021-06-16 | 2022-04-12 | 武汉大学 | Multi-view image alignment method based on rational polynomial model differentiable tensor expression |
| TWI803369B (en) * | 2022-06-24 | 2023-05-21 | 寶成工業股份有限公司 | Automatic Mold Spraying System |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8437518B2 (en) * | 2006-08-08 | 2013-05-07 | Koninklijke Philips Electronics N.V. | Registration of electroanatomical mapping points to corresponding image data |
| CN101645170B (en) * | 2009-09-03 | 2011-07-20 | 北京信息科技大学 | Precise registration method of multilook point cloud |
-
2010
- 2010-08-17 CN CN2010102553612A patent/CN101901502B/en not_active Expired - Fee Related
Also Published As
| Publication number | Publication date |
|---|---|
| CN101901502A (en) | 2010-12-01 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN101901502B (en) | Global optimal registration method of multi-viewpoint cloud data during optical three-dimensional measurement | |
| CN109521403B (en) | Parameter calibration method, device and equipment of multi-line laser radar and readable medium | |
| CN107301654B (en) | Multi-sensor high-precision instant positioning and mapping method | |
| Zhang et al. | A 3D reconstruction method for pipeline inspection based on multi-vision | |
| CN102663767B (en) | Method for calibrating and optimizing camera parameters of vision measuring system | |
| CN107014312A (en) | A kind of integral calibrating method of mirror-vibrating line laser structured light three-dimension measuring system | |
| CN103196370B (en) | Measuring method and measuring device of conduit connector space pose parameters | |
| CN109341668B (en) | Multi-camera measuring method based on refraction projection model and light beam tracking method | |
| CN105066962B (en) | A kind of high-precision photogrammetric apparatus of the big angle of visual field of multiresolution | |
| CN104395932A (en) | Method for registering data | |
| CN102472609A (en) | Position and orientation calibration method and apparatus | |
| CN107167073A (en) | A kind of three-dimensional rapid measurement device of linear array structure light and its measuring method | |
| CN1971206A (en) | Calibration method for binocular vision sensor based on one-dimension target | |
| CN101900531B (en) | Method for measuring and calculating binocular vision displacement measurement errors and measuring system | |
| Toschi et al. | On the evaluation of photogrammetric methods for dense 3D surface reconstruction in a metrological context | |
| CN104019799A (en) | Relative orientation method by using optimization of local parameter to calculate basis matrix | |
| CN104697463B (en) | The blanking feature constraint scaling method and device of a kind of binocular vision sensor | |
| CN110223355B (en) | Feature mark point matching method based on dual epipolar constraint | |
| CN110695982A (en) | Mechanical arm hand-eye calibration method and device based on three-dimensional vision | |
| CN109544642B (en) | N-type target-based TDI-CCD camera parameter calibration method | |
| CN110544207B (en) | Sub-aperture data splicing method, system and medium for large-area microstructure morphology | |
| Tushev et al. | Architecture of industrial close-range photogrammetric system with multi-functional coded targets | |
| Meng et al. | Defocused calibration for large field-of-view binocular cameras | |
| CN103424087A (en) | Three-dimensional measurement splicing system and method for large-scale steel plate | |
| CN114049401A (en) | Binocular camera calibration method, device, equipment and medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| C14 | Grant of patent or utility model | ||
| GR01 | Patent grant | ||
| C17 | Cessation of patent right | ||
| CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20120502 Termination date: 20120817 |