CN106340059A - Automatic registration method based on multi-body-feeling-acquisition-device three-dimensional modeling - Google Patents
Automatic registration method based on multi-body-feeling-acquisition-device three-dimensional modeling Download PDFInfo
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Abstract
The invention relates to the technical field of computer modeling, and discloses an automatic registration method based on multi-body-feeling-acquisition-device three-dimensional modeling. The method comprises the following steps: 1) utilizing different body feeling acquisition devices to collect data of a three-dimensional object needing modeling to obtain a point cloud set in an RGB-D format; 2) calculating a relative space position transformation matrix M of any two adjacent body feeling acquisition devices by utilizing a pairwise calibration method of a standard plane calibration plate having feature textures; 3) selecting a coordinate system of a point cloud set collected by any body feeling acquisition device as a reference coordinate system, through superimposed transformation of the relative space position transformation matrix M, calculating an absolute space position transformation matrix G, and displaying the point cloud data of each body feeling acquisition device in the reference coordinate system by utilizing the absolute space position transformation matrix G, and finishing automatic preliminary registration; and 4) carrying out precise registration correction on the preliminary registration point cloud data set, and finishing automatic precise registration. The method is convenient to operate and easy to realize, and can reach a higher modeling precision.
Description
Technical field
The present invention relates to microcomputer modelling technical field, more particularly, to a kind of based on many body-sensing collecting device three-dimensional modeling
Method for automatically split-jointing.
Background technology
During multiple views three-dimensional geometric mode building system builds to three-dimensional profile, several between adjacent viewpoint picture
What modeling is merely capable of obtaining the point cloud geometric data of testee surface regional area, wants to obtain whole three-dimensional profile number
According to needing to carry out multiple views and obtain image, be simultaneous for each two adjacent viewpoint and carry out Geometric Modeling, this leads under different points of view
The geometric coordinate system of calculated cloud geometric data is different.
In order to obtain the whole three-dimensional profile geometric data on testee surface, needing will be several for the local under different coordinates
What data transforms under same unified coordinate system, but the automatic spelling of the three-dimensional point cloud geometric data under completing different coordinates
Connect and registration is always a stubborn problem.Existing method mainly has:
1. in object being measured surface mount aid mark point, so not only can destroy the texture letter on object being measured surface
Breath, cannot paste the object being measured surface geometry at covering simultaneously to mark point and data texturing is modeled.
2. determine the change in location relation between object being measured and multiple views using some heads, the method stably may be used
Lean on, and have higher precision, but need additional high-accuracy mechanical equipment, thus it is multiple to lead to multiple views to obtain device structure
Miscellaneous it is impossible to measure to larger object.
3. the manual characteristic point choosing coupling is mated in advance, then passes through existing business software Processing Algorithm and completes
To a splicing for cloud geometric data.Such method needs to intervene the coupling realizing data in advance by artificial first, but artificial coupling
Error is excessive to be unable to reach preferable splicing effect it is impossible to realize to the point cloud geometric data after multi-view image three-dimensional modeling
Full-automatic splicing.
Content of the invention
In order to solve existing issue, the invention provides a kind of automatic Mosaic based on many body-sensing collecting device three-dimensional modeling
Method, needs the point modeling the rgb-d form of three-dimensional body to converge by the collection of body-sensing collecting device, using relative tertiary location
Transition matrix and absolute spatial position transition matrix complete the automatic Mosaic putting cloud geometric data after three-dimensional modeling.
The present invention can be achieved through the following technical solutions:
A kind of method for automatically split-jointing based on many body-sensing collecting device three-dimensional modeling, comprising:
Step one, the data of the three-dimensional body being modeled using different body-sensing collecting device collection needs, obtain described three
The point of the rgb-d form of dimension object converges；
Step 2, the point to the collection of any two adjacent body-sensing collecting device converge, using the standard with feature texture
The bearing calibration two-by-two of plane correction plate, solves relative tertiary location transition matrix m；
Step 3, the coordinate system that converges of point choosing the collection of any one body-sensing collecting device are reference frame, pass through
The superposition conversion of relative tertiary location transition matrix m, calculates the coordinate system that the point of each body-sensing collecting device converges relative to reference
Coordinate system absolute spatial position transition matrix g, using absolute spatial position transition matrix g by the point cloud of each body-sensing collecting device
Data is indicated under reference frame, completes the preliminary corrections of cloud data, realizes the point cloud of each body-sensing collecting device
Automatically tentatively the splicing of data；
Step 4, the cloud data collection to preliminary splicing carry out accuracy registration school by point cloud iteration optimization registration Algorithm
Just, to complete automatically accurately the splicing of cloud data of each body-sensing collecting device.
In the described bearing calibration two-by-two with the standard flat correcting plate of feature texture, described body-sensing collecting device setting
There is q group, every group n, any of which spatial point coordinate representation is p=(x, y, z), after being converted to 1 × 4 vector matrix, represent
For p=(x, y, z, 1) ', then the point cloud matrix of 4 × n of i-th group of j-th body-sensing collecting device collection is expressed asN is point cloud number, 1≤i≤q, 1≤j≤n, the described standard flat correction with feature texture
The bearing calibration two-by-two of plate includes:
Step, i-th of the standard flat correcting plate with feature texture1Group jth1Individual body-sensing collecting device and i-th2Group
Jth2During the point of individual body-sensing collecting device converges, choose s to check point pair, wherein s >=4,
Step, solves below equation using unit quaternion method, obtains relative tertiary location transition matrix m,
p(i2,j2),s=m[(i1,j1),(i2,j2)]p(i1,j1),s
Wherein, m[(i1,j1),(i2,j2)]It is i-th for 4 × 4 matrixes2Group jth2Individual body-sensing collecting device is with respect to i-th1Group jth1
Relative tertiary location transition matrix between individual body-sensing collecting device,
Step, i-th2Group jth2The point of individual body-sensing collecting device collection is converged and is completed with respect to the by below equation
i1Group jth1The point of individual body-sensing collecting device collection converges locus conversion,
pt(i2,j2),n=m[(i1,j1),(i2,j2)]p(i2,j2),n
Wherein, pt(i2,j2),nFor i-th2Group jth2Point cloud matrix after individual body-sensing collecting device conversion.
In described step 3, the superposition conversion of relative position transition matrix m includes:
Make the 1st group of the 1st body-sensing coordinate system be reference frame, be labeled as g(1,1)=i4×4, i.e. 4 × 4 unit matrix, then
I-th group of j-th body-sensing collecting device absolute spatial position transformation matrix g is:
As i=1,
As j=1,
g(1,1)=i4×4
When j ≠ 1,
g(1,j)=m[(1,j-1),(1,j)]g(1,j-1)
Otherwise
As j=1,
g(i,1)=m[(i-1,1),(i,1)]g(i-1,1)
When j ≠ 1,
g(i,j)=m[(i,j-1),(i,j)]g(i,j-1)
After absolute spatial position transition matrix g conversion, the tentatively spliced point of i-th group of j-th body-sensing collecting device
Cloud matrix is pt(i,j),n=g(i,j)p(i,j),n.
Accuracy registration correction in described step 4 includes:
Step, using high-precision laser scanning device, one standard people's mould is carried out with three-dimensional point cloud scanning, manual led
Enter environment of sampling, as the master pattern of accuracy registration correction,
Step, in the point of preliminary splicing converges and converges with the point of master pattern, manual choose check point pair, using such as
Bearing calibration two-by-two described in claim 2 solves relative tertiary location transition matrix, and applies this matrix to become master pattern
Shift in precorrection data space, the master pattern cloud data after rotation and displacement is labeled asWherein m is master pattern point cloud number,
Step, makes the point of the preliminary splicing of i-th group of j-th body-sensing collecting device converge and is expressed as pt(i,j),n, i-th group
The accuracy registration correction matrix of j-th body-sensing collecting device is gp(i,j), calculated by a cloud iteration optimization registration, solve following mesh
Scalar functions are optimum:
Wherein r is that the point that the preliminary point splicing of i-th group of j-th body-sensing collecting device is converged after being converted with master pattern converges
The point cloud number of intersection, r is r-th point of cloud of intersection.
This method for automatically split-jointing requires q >=3, n >=2.
Beneficial the having the technical effect that of the present invention
(1) utilize body-sensing collecting device gathered data, add depth information, improve the accuracy of modeling.
(2) pass through the conversion of relative tertiary location matrix and absolute spatial position matrix, complete by different body-sensing collecting devices
The point of collection converges to be transformed into and shows under the same coordinate system, realizes automatic Mosaic.
(3) complete accuracy registration correction using standard people's mould, improve the precision of modeling.
Brief description
Fig. 1 is the flow chart of the present invention.
Specific embodiment
Below in conjunction with the accompanying drawings, the specific embodiment of the present invention is further elaborated.
Fig. 1 is the flow chart of the present invention, as illustrated, a kind of automatic Mosaic based on many body-sensing collecting device three-dimensional modeling
Method, comprises the steps:
1. calculate relative tertiary location transition matrix
Assume total q (q >=3) group, every group is provided with the individual body-sensing collecting device of n (n >=2), being gathered using these equipment is needed
The data of the three-dimensional body of modeling, the point obtaining the rgb-d form of this three-dimensional body converges.This body-sensing collecting device can be
The three-dimension sensor of kinect or primsense series.
Define arbitrary space point coordinates that p=(x, y, z) is during this point converges, after being converted to 1 × 4 vector matrix, table
It is shown as p=(x, y, z, 1) ', then the point cloud matrix of 4 × n of i-th group of j-th body-sensing collecting device collection is expressed asWherein n is point cloud number, 1≤i≤q, 1≤j≤n,
m[(i1,j1),(i2,j2)]Represent i-th for 4 × 4 matrixes2Group jth2Individual body-sensing collecting device is with respect to i-th1Group jth1Individual
Relative tertiary location transition matrix between sense collecting device,
Using the standard flat correcting plate with feature texture, relative tertiary location conversion is solved using bearing calibration two-by-two
Matrix m[(i1,j1),(i2,j2)].
Step, i-th of the standard flat correcting plate with feature texture1Group jth1Individual body-sensing collecting device and i-th2Group
Jth2During the point of individual body-sensing collecting device converges, choose s to check point pair, wherein s >=4,
Step, solves below equation using unit quaternion method, obtains relative tertiary location transition matrix m, specifically
Algorithm bibliography: a method for registration of 3d shapes, ieee tpami, vol 14, no 2,
1992,
p(i2,j2),s=m[(i1,j1),(i2,j2)]p(i1,j1),s
Step, i-th2Group jth2The point of individual body-sensing collecting device collection is converged and is completed with respect to the by below equation
i1Group jth1The point of individual body-sensing collecting device collection converges locus conversion,
pt(i2,j2),n=m[(i1,j1),(i2,j2)]p(i2,j2),n
Wherein, pt(i2,j2),nFor i-th2Group jth2Point cloud matrix after individual body-sensing collecting device conversion.
2. utilize the superposition conversion of relative tertiary location transition matrix m, solve absolute spatial position transition matrix g, complete
The first correction of different body-sensing collecting device relative positions, realizes automatically tentatively splicing of modeling.
Make the 1st group of the 1st body-sensing coordinate system be reference frame, be labeled as g(1,1)=i4×4, i.e. 4 × 4 unit matrix, then
I-th group of j-th body-sensing collecting device absolute spatial position transformation matrix g is:
As i=1,
As j=1,
g(1,1)=i4×4
When j ≠ 1,
g(1,j)=m[(1,j-1),(1,j)]g(1,j-1)
Otherwise
As j=1,
g(i,1)=m[(i-1,1),(i,1)]g(i-1,1)
When j ≠ 1,
g(i,j)=m[(i,j-1),(i,j)]g(i,j-1)
Point after absolute spatial position transition matrix g conversion, after i-th group of j-th body-sensing collecting device correction for the first time
Cloud matrix is pt(i,j),n=g(i,j)p(i,j),n, the point of each body-sensing collecting device is converged and shows under reference frame,
Complete the preliminary splicing of cloud data.
3. using standard people's mould, tentatively spliced point is converged and do accuracy registration correction, realize automatically accurately spelling of modeling
Connect.
(1) three-dimensional point cloud scanning is carried out using high-precision laser scanning device to standard people's mould, manual importing is sampled
Environment, as the master pattern of follow-up somatosensory device fine adjustment.
(2) manual selection between precorrection data and master pattern corrects point set, then calculates relative tertiary location conversion
Matrix, and (concrete grammar is with above-mentioned correction side two-by-two to apply this matrix that master pattern is converted in precorrection data space
Method), the master pattern cloud data after rotation and displacement is labeled asWherein m is master pattern
Point cloud number.
(3) make the preliminary point splicing of i-th group of j-th body-sensing collecting device converge and be expressed as pt(i,j),n, i-th group j-th
The accuracy registration correction matrix of body-sensing collecting device is gp(i,j), by a cloud iteration optimization registration Algorithm, solve following target letter
Number is optimum:
Wherein r is that the point that the preliminary point splicing of i-th group of j-th body-sensing collecting device is converged after being converted with master pattern converges
The point cloud number of intersection, r is r-th point of cloud of intersection.
Specific point cloud iteration optimization registration Algorithm refers to algorithm routine bag of increasing income: point cloud library
(pcl),https://www.pointclouds.org/, apply class libraries: iterative closest point, bibliography
For: a method for registration of 3d shapes, ieee tpami, vol 14, no.2,1992.
Although the foregoing describing the specific embodiment of the present invention, it will be appreciated by those of skill in the art that these
It is merely illustrative of, on the premise of the principle without departing substantially from the present invention and essence, these embodiments can be made with multiple changes
More or modification, therefore, protection scope of the present invention is defined by the appended claims.
Claims (5)
1. a kind of method for automatically split-jointing based on many body-sensing collecting device three-dimensional modeling is it is characterised in that include:
Step one, the data of the three-dimensional body being modeled using different body-sensing collecting device collection needs, obtain described three-dimensional article
The point of the rgb-d form of body converges；
Step 2, the point to the collection of any two adjacent body-sensing collecting device converge, using the standard flat with feature texture
The bearing calibration two-by-two of correcting plate, solves relative tertiary location transition matrix m；
Step 3, the coordinate system that converges of point choosing the collection of any one body-sensing collecting device are reference frame, by relatively
The superposition conversion of locus transition matrix m, calculates the coordinate system that the point of each body-sensing collecting device converges relative to reference coordinate
It is absolute spatial position transition matrix g, using absolute spatial position transition matrix g by the cloud data of each body-sensing collecting device
It is indicated under reference frame, completes the preliminary corrections of cloud data, realize the cloud data of each body-sensing collecting device
Automatically tentatively splice；
Step 4, the cloud data collection to preliminary splicing carry out accuracy registration correction by point cloud iteration optimization registration Algorithm, complete
Become automatically accurately the splicing of cloud data of each body-sensing collecting device.
2. method for automatically split-jointing as claimed in claim 1 is it is characterised in that the described standard flat with feature texture corrects
In the bearing calibration two-by-two of plate, described body-sensing collecting device is provided with q group, and every group n, any of which spatial point coordinate representation is
P=(x, y, z), after being converted to 1 × 4 vector matrix, is expressed as p=(x, y, z, 1) ', then i-th group of j-th body-sensing collection sets
The point cloud matrix of 4 × n of standby collection is expressed asN is point cloud number, 1≤i≤q, 1≤j≤n,
The bearing calibration two-by-two of the described standard flat correcting plate with feature texture includes:
Step, i-th of the standard flat correcting plate with feature texture1Group jth1Individual body-sensing collecting device and i-th2Group jth2
During the point of individual body-sensing collecting device converges, choose s to check point pair, wherein s >=4,
Step, solves below equation using unit quaternion method, obtains relative tertiary location transition matrix m, p(i2,j2),s=
m[(i1,j1),(i2,j2)]p(i1,j1),s
Wherein, m[(i1,j1),(i2,j2)]It is i-th for 4 × 4 matrixes2Group jth2Individual body-sensing collecting device is with respect to i-th1Group jth1Individual
Relative tertiary location transition matrix between sense collecting device,
Step, i-th2Group jth2The point of individual body-sensing collecting device collection is converged and is completed with respect to i-th by below equation1Group
Jth1The point of individual body-sensing collecting device collection converges locus conversion,
pt(i2,j2),n=m[(i1,j1),(i2,j2)]p(i2,j2),n
Wherein, pt(i2,j2),nFor i-th2Group jth2Point cloud matrix after individual body-sensing collecting device conversion.
3. method for automatically split-jointing as claimed in claim 1 or 2 it is characterised in that in described step 3 relative position conversion square
The superposition conversion of battle array m includes:
Make the 1st group of the 1st body-sensing coordinate system be reference frame, be labeled as g(1,1)=i4×4, i.e. 4 × 4 unit matrix, then i-th group
J-th body-sensing collecting device absolute spatial position transformation matrix g is:
As i=1,
As j=1,
g(1,1)=i4×4
When j ≠ 1,
g(1,j)=m[(1,j-1),(1,j)]g(1,j-1)
Otherwise
As j=1,
g(i,1)=m[(i-1,1),(i,1)]g(i-1,1)
When j ≠ 1,
g(i,j)=m[(i,j-1),(i,j)]g(i,j-1)
After absolute spatial position transition matrix g conversion, tentatively spliced cloud square of i-th group of j-th body-sensing collecting device
Battle array is pt(i,j),n=g(i,j)p(i,j),n.
4. method for automatically split-jointing as claimed in claim 3 is it is characterised in that the accuracy registration correction in described step 4 is wrapped
Include:
Step, three-dimensional point cloud scanning is carried out to standard people's mould using high-precision laser scanning device, be conducted into by hand adopting
Sample environment, as the master pattern of accuracy registration correction,
Step, in the point of preliminary splicing converges and converges with the point of master pattern, manual choose check point pair, using as right
Require the bearing calibration two-by-two described in 2 to solve relative tertiary location transition matrix, and apply this matrix to be converted into master pattern
In precorrection data space, the master pattern cloud data after rotation and displacement is labeled asIts
Middle m is master pattern point cloud number,
Step, makes the point of the preliminary splicing of i-th group of j-th body-sensing collecting device converge and is expressed as pt(i,j),n, i-th group j-th
The accuracy registration correction matrix of body-sensing collecting device is gp(i,j), calculated by a cloud iteration optimization registration, solve following object function
Optimum:
Wherein r is that the point of the preliminary splicing of i-th group of j-th body-sensing collecting device is converged to be converged with the point after master pattern conversion and overlaps
Partial point cloud number, r is r-th point of cloud of intersection.
5. method for automatically split-jointing as claimed in claim 2 is it is characterised in that described body-sensing collecting device is provided with q group, every group
N, wherein q >=3, n >=2.
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