CN103955939B - 3 D scanning system midpoint cloud edge feature point method for registering - Google Patents
3 D scanning system midpoint cloud edge feature point method for registering Download PDFInfo
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Abstract
The invention discloses a kind of 3 D scanning system midpoint cloud edge feature point method for registering, step is as follows:1) three-dimensional laser scanner obtains the spatial sampling point on different visual angles real-world object surface;2) the point cloud boundary characteristics point of the boundary detection method extraction different visual angles of point of use cloud centroidal distance feature;3) closest point (ICP) the algorithm registration point cloud of improved iteration is used according to the point cloud boundary characteristics of extraction point;4) registration accuracy is evaluated according to registration error standard, whether verification registration result reaches with alignment request.The present invention carries out edge feature point extraction by treating registration point cloud, the defects of each point searches corresponding points in point cloud data need to be traversed by avoiding traditional ICP algorithm, on the basis of registration accuracy is ensured, the efficiency of point cloud registering is significantly improved while effectively reducing algorithm complexity.
Description
Technical field
The invention belongs to the technical fields such as reverse-engineering, image procossing, and in particular to a kind of 3 D scanning system point cloud
Splicing edge feature point method for registering.
Background technology
The three-dimensional geometry that point cloud data obtains body surface discrete point generally by measuring instruments such as spatial digitizers is sat
Mark due to being limited by factors such as size, environment and the measuring appliances for measuring object, can only measure a side of object every time
Therefore face, to obtain the partial data information of testee, needs to be scanned object from multiple and different angles.It will be different
Point cloud data under visual angle is uniformly the registration of point cloud data to the process in the same coordinate system.Along with reconstructing three-dimensional model
In the extensive use of the various fields such as reverse-engineering, industrial detection, Medical Image Processing, historical relic's protection, three dimensional point cloud is matched
Quasi- technology also becomes popular and important research topic.
Existing cloud data registration technology is broadly divided into:Manual registration relies on instrument registration and autoregistration, wherein automatically
Registration technique obtains most commonly used application, which mainly includes two steps of rough registration and accuracy registration.Rough registration is
The premise of good initial value is provided for accuracy registration, accuracy registration is the key that then to determine point cloud registering error, associated specialist
Person has done a large amount of correlative studys in terms of accuracy registration.It is at present the closest point of iteration with most commonly used accuracy registration algorithm
Algorithm (iterative close point, ICP).ICP algorithm proposes respectively by Besl et al. and Chen et al. earliest, but its
Shortcoming mainly has:The quality of initial point cloud directly influences splicing precision, and iteration takes, restrains relatively slow, registration is easily done by noise
It disturbs, be easily absorbed in local optimum etc..For obtain accuracy registration as a result, domestic and foreign scholars classical ICP algorithm has been done numerous studies and
It improves.Such as in terms of Irregular Boundary Surface point cloud registering, it is registrated using rough registration and the improved ICP algorithm of rigidity characteristic, it is real
The accuracy registration of Irregular Boundary Surface point cloud is showed.Although the convergence speed of the algorithm and joining quality are obtained for certain raising,
But influence of the noise to registration accuracy is not accounted for, robustness is poor in some cases.As combined based on curvature feature
The ICP innovatory algorithms of point can effectively be registrated unknown point cloud, have a clear superiority on Quasi velosity.It but must be first
Begin under the guarantee of registration, registration can just be made to be unlikely to tend to the opposite way round.Otherwise, when the rotary shifted of two panels point cloud and translation are wrong
When bit comparison is big, only registration can be caused to be absorbed in local optimum using only improved ICP accuracy registrations.Such as assign corresponding points weight and
The Revised ICP algorithm of M-estimation is introduced, accuracy registration is carried out with Revised ICP algorithm on the basis of rough registration.The innovatory algorithm
Solve the efficiency bottle neck of classical ICP algorithm, effectively eliminate influence of the abnormal point to algorithm, improve algorithm accuracy and
Reliability, but need to be further improved on proximity pair search efficiency.
Currently, closest point (ICP) method of existing iteration is:In the point cloud number that different visual angles are obtained by spatial digitizer
According to rear, the closest approach that two points are concentrated is found every time with ICP algorithm, and make the quadratic sum of its Euclidean distance minimum, so as to count
Calculate the rigid body translation between point cloud.The rigid body translation is applied to point cloud and obtains new target point cloud, if object function error is received
It holds back in given threshold value, terminates iteration, otherwise continue to search for closest approach.
As shown in Figure 1, Fig. 1, which is used, improves iterative closest point approach progress cloud data registration, however the point cloud registering side
Method has the following disadvantages and defect:
(1) algorithm complexity of algorithm extraction point cloud boundary characteristics point is higher and without improved search strategy, causes most
Near point is not high to search efficiency, so as to influence to be registrated efficiency.
(2) algorithm must can just make registration be unlikely to tend to the opposite way round under the guarantee of initial registration.Otherwise, when
When the rotary shifted and translation dislocation of two panels point cloud is bigger, only registration can be caused to be absorbed in using only improved ICP accuracy registrations
Local optimum.
Therefore, to obtain quickly and accurately registration result, boundary characteristic is obtained with new point cloud boundary extracting method
Point is searched for by K-D trees in edge feature point and accelerates to search proximity pair, can accelerated search speed, further improve and match
Quasi- efficiency.
Invention content
For above-mentioned deficiency in the prior art, the 3-D scanning system with Quasi velosity is improved the present invention provides a kind of
System midpoint cloud edge feature point method for registering.
In order to solve the above-mentioned technical problem, present invention employs following technical solutions:
3 D scanning system midpoint cloud edge feature point method for registering, this method comprises the following steps:
(1), it scans the profile of mock-up with different view with spatial digitizer, obtains multi-view angle three-dimensional sampling number
According to;
(2), for two pieces of three-dimensional sample point datas of mock-up same under different visual angles, by with a cloud distance of centre of gravity
Boundary detection method from feature extracts the edge feature point of two pieces of three-dimensional sample point datas respectively;
(3), in two block boundary characteristic points of extraction, it is with reference to point set to fix one of, and another piece is target point set,
Corresponding proximity pair is obtained using K-D tree methods acceleration search, and is calculated according to unit Quaternion method so that corresponding proximity pair
The rigid body translation of average distance minimum, the rigid body translation are spin matrix R and translation matrix T;
(4), coordinate transform is carried out to target point set using spin matrix R and translation matrix T, obtains new target point set,
New target point set is judged with whether the distance with reference to point set is less than given threshold value according to stopping criterion for iteration to realize a cloud
Registration;
(5), it can quickly be carried out a little come Revised ICP algorithm based on the boundary extraction method of cloud centroidal distance feature with this
Cloud is registrated, and obtains accuracy registration effect.
As a preferred embodiment of the present invention, the boundary detection method of described cloud centroidal distance feature, specific steps
It is as follows:
The numerical value that point cloud elevation information is represented on Z axis is converted into matter of the gray value between 0-255 as point cloud first
Attribute is measured, is denoted as H;Current point P (X are found secondly by k nearest neighbor searchi,Yi,Zi) K arest neighbors, K=16;Finally calculate K
The barycentric coodinates (X, Y, Z) of the point group of a arest neighbors composition, threshold value δ is obtained by maximum kind differences method;Point P is calculated to point group weight
The Euclidean distance of the heart, as the Euclidean distance of fruit dot P to point group center of gravity is more than threshold value δ, then it is assumed that point P is marginal point;Conversely, point P
It is not marginal point;
Barycentric coodinates (X, Y, Z) formula of the point group of wherein K arest neighbors composition is as follows:
Z=Hi
The Euclidean distance formula of point P to point group center of gravity is as follows:
As another preferred embodiment of the present invention, the unit Quaternion method is as follows:
Assuming that target point set X={ xi|xi∈R3, i=1,2 ... m }, with reference to point set Y={ yj|yj∈R3, j=1,2,
... m }, for target point set X with being corresponded with reference to the point in point set Y, m is point cloud data amount;
1) target point set X and the center of gravity with reference to point set Y are asked respectively;
2) covariance matrix is constructed;
Wherein:(yi-μY)TFor matrix (yi-μY) transposition;
3) 4 × 4 symmetrical matrixes are constructed according to covariance matrix;
Wherein:tr∑X,YIt is matrix ∑X,YMark, if Ai,j=(∑X,Y-∑X,Y T)i,j, Δ=[A23,A21,A12]T, I3It is 3
× 3 unit matrixs, Ai,jFor (∑X,Y-∑X,Y T)i,jThe matrix of construction;
4) 4 × 4 symmetrical matrix Q (∑s are calculatedX,Y) characteristic value and feature vector, the corresponding feature vector of maximum eigenvalue
As unit quaternion [q0,q1,q2,q3]T;Rotating vector is qR=[q0,q1,q2,q3]T;
5) spin matrix is calculated
6) translation matrix is calculated
T(qR)=[q4,q5,q6]T=μX-R(qR)μY。
As another preferred embodiment of the present invention, the iteration ends Rule of judgment is as follows:
If new transformation point set is less than given threshold value λ with the European average distance with reference to point set, stop iteration, it is on the contrary
New point set is then continued into iteration as initial value, until meeting the requirement of object function;Wherein object function is:
Wherein:N be point cloud data amount, miFor reference point clouds to be registered, R is spin matrix, and T is translation matrix, SiFor mesh
Punctuate cloud, i=1,2...n.
The beneficial effects of the invention are as follows:It needs to traverse each point for ICP algorithm to calculate corresponding points, algorithm calculation amount
Larger, the shortcomings that efficiency is very low, the present invention proposes a kind of Revised ICP algorithm of the edge detection based on gravity center characteristics for point
The accuracy registration of cloud data, this method not only solves the bottleneck on ICP algorithm search strategy, and is ensureing registration accuracy
On the basis of effectively increase registration efficiency;Meanwhile the simplification rate based on border detection point cloud data reaches 2.2431%, compared to
The registration efficiency of congenic method improves more than 25.8%, shows that this method is more suitable for the larger point cloud data of data volume and matches
It is accurate.
Description of the drawings
Fig. 1 is the schematic diagram of cloud data registration of the prior art;
Fig. 2 is the schematic diagram of existing boundary detection method and the embodiment one of boundary detection method of the present invention comparison;
The embodiment two that Fig. 3 is classical ICP algorithm, existing improvement ICP methods are compared with present invention improvement ICP methods is shown
It is intended to;
Fig. 4 is the implementation that existing improvement ICP methods are directed to the comparison of different visual angles point cloud registering with present invention improvement ICP methods
The schematic diagram of example three;
Fig. 5 is that existing ICP algorithm is registrated the schematic diagram for taking comparison with the method for the present invention.
Specific embodiment
The present invention is described in further detail with reference to the accompanying drawings and detailed description.
3 D scanning system midpoint cloud edge feature point method for registering, this method comprises the following steps:
(1), it scans the profile of mock-up with different view with spatial digitizer, obtains multi-view angle three-dimensional sampling number
According to;
(2), for two pieces of three-dimensional sample point datas of mock-up same under different visual angles, by with a cloud distance of centre of gravity
Boundary detection method from feature extracts the edge feature point of two pieces of three-dimensional sample point datas respectively;
(3), in two block boundary characteristic points of extraction, it is with reference to point set to fix one of, and another piece is target point set,
Corresponding proximity pair is obtained using K-D tree methods acceleration search, and is calculated according to unit Quaternion method so that corresponding proximity pair
The rigid body translation of average distance minimum, as spin matrix R and translation matrix T;
(4), coordinate transform is carried out to target point set using spin matrix R and translation matrix T, obtains new target point set,
New target point set is judged with whether the distance with reference to point set is less than given threshold value according to stopping criterion for iteration to realize a cloud
Registration;
(5), it can quickly be carried out a little come Revised ICP algorithm based on the boundary extraction method of cloud centroidal distance feature with this
Cloud is registrated, and obtains accuracy registration effect.
Based on a boundary detection method for cloud centroidal distance feature, step is as follows:
1) numerical value that point cloud elevation information is represented on Z axis is converted into quality of the gray value between 0-255 as point cloud
Attribute is denoted as H;
2) current point P (X are found by k nearest neighbor searchi,Yi,Zi) K arest neighbors, K=16;
3) barycentric coodinates (X, Y, Z) of the point group of K arest neighbors composition are calculated, formula is as follows:
Z=Hi
Threshold value δ is obtained by Otsu (maximum kind differences method);
4) point P is calculated to the Euclidean distance of point group center of gravity, and formula is as follows:
5) as the Euclidean distance of fruit dot P to point group center of gravity is more than threshold value δ, then it is assumed that point P is marginal point;Conversely, point P is not
Marginal point.
Rigid body translation is carried out using unit Quaternion method, is as follows:
Assuming that target point set X={ xi|xi∈R3, i=1,2 ... m }, with reference to point set Y={ yj|yj∈R3, j=1,2,
... m }, for target point set X with being corresponded with reference to the point in point set Y, m is point cloud data amount;
1) target point set X and the center of gravity with reference to point set Y are asked respectively
2) covariance matrix is constructed;
Wherein:(yi-μY)TFor matrix (yi-μY) transposition;
3) 4 × 4 symmetrical matrixes are constructed according to covariance matrix;
Wherein tr ∑sX,YIt is matrix ∑X,YMark, if Ai,j=(∑X,Y-∑X,Y T)i,j, Δ=[A23,A21,A12]T, I3It is 3
× 3 unit matrixs, Ai,jFor (∑X,Y-∑X,Y T)i,jThe matrix of construction;
4) 4 × 4 symmetrical matrix Q (∑s are calculatedX,Y) characteristic value and feature vector, the corresponding feature vector of maximum feature is
For unit quaternary number [q0,q1,q2,q3]T;Rotating vector is qR=[q0,q1,q2,q3]T;
5) spin matrix is calculated
6) translation matrix is calculated
T(qR)=[q4,q5,q6]T=μX-R(qR)μY。
Iteration ends Rule of judgment is as follows:
If new transformation point set is less than given threshold value λ with the European average distance with reference to point set, stop iteration, it is on the contrary
New point set is then continued into iteration as initial value, until meeting the requirement of object function;Wherein object function is:
Wherein:N be point cloud data amount, miFor reference point clouds to be registered, R is spin matrix, and T is translation matrix, SiFor mesh
Punctuate cloud, i=1,2...n.
Present invention employs new point cloud boundary detection method Revised ICP algorithms.Based on a side for cloud centroidal distance feature
Boundary is detected as shown in Fig. 2, point cloud boundary extraction comparison is as shown in table 1 below;
Table 1:
By as shown in table 2 below to being compared after Fig. 3 progress accuracy registration algorithms;
Table 2:
Method for registering | It is registrated time/ms | Registration error/mm | Iterations/time |
Classical ICP algorithm | 112.1820 | 0.1544 | 137 |
Revised ICP algorithm | 80.4597 | 0.1287 | 87 |
This paper algorithms | 51.5570 | 0.0965 | 30 |
It is as shown in table 3 below by carrying out the comparison of different visual angles cloud data registration to Fig. 4;
Table 3:
From Fig. 3, Fig. 4 and Fig. 5 and table 2 and table 3 as can be seen that the present invention carries out cloud data registration with edge feature point
Method solves the bottleneck on ICP algorithm search strategy, and registration effect is effectively increased on the basis of registration accuracy is ensured
Rate.Meanwhile the simplification rate based on border detection point cloud data reaches 2.2431%, the registration efficiency compared to congenic method improves
More than 25.8%, show that the improved ICP algorithm of the present invention is more suitable for the larger cloud data registration of data volume.
Finally illustrate, the above embodiments are merely illustrative of the technical solutions of the present invention and it is unrestricted, although with reference to compared with
The present invention is described in detail in good embodiment, it will be understood by those of ordinary skill in the art that, it can be to the skill of the present invention
Art scheme is modified or replaced equivalently, and without departing from the objective and range of technical solution of the present invention, should all be covered at this
In the right of invention.
Claims (1)
1. 3 D scanning system midpoint cloud edge feature point method for registering, which is characterized in that this method includes following step
Suddenly:
(1), it scans the profile of mock-up with different view with spatial digitizer, obtains multi-view angle three-dimensional sample point data;
(2), for two pieces of three-dimensional sample point datas of mock-up same under different visual angles, by special with point cloud centroidal distance
The boundary detection method of sign extracts the edge feature point of two pieces of three-dimensional sample point datas respectively;Wherein, the boundary detection method
It is as follows:
The numerical value that point cloud elevation information is represented on Z axis is converted into quality category of the gray value between 0-255 as point cloud first
Property, it is denoted as H;Current point P (X are found secondly by k nearest neighbor searchi,Yi,Zi) K arest neighbors, K=16;Finally calculate K most
The barycentric coodinates (X, Y, Z) of the point group of neighbour's composition obtain threshold value δ by maximum kind differences method;Point P is calculated to point group center of gravity
Euclidean distance, as the Euclidean distance of fruit dot P to point group center of gravity is more than threshold value δ, then it is assumed that point P is marginal point;Conversely, point P is not
Marginal point;
Barycentric coodinates (X, Y, Z) formula of the point group of wherein K arest neighbors composition is as follows:
Z=Hi
The Euclidean distance formula of point P to point group center of gravity is as follows:
(3), it is fixed one of for reference to point set, another piece is target point set, is utilized in two block boundary characteristic points of extraction
K-D tree methods acceleration search obtains corresponding proximity pair, and is calculated according to unit quaternion method so that corresponding proximity pair is averaged
The minimum rigid body translation of distance, the rigid body translation are spin matrix R and translation matrix T;Wherein, the unit quaternion method, tool
Body step is as follows:
Assuming that target point set X={ xi|xi∈R3, i=1,2 ... m }, with reference to point set Y={ yj|yj∈R3, j=1,2 ... m },
For target point set X with being corresponded with reference to the point in point set Y, m is point cloud data amount;
1) target point set X and the center of gravity with reference to point set Y are asked respectively;
2) covariance matrix is constructed;
Wherein:(yi-μY)TFor matrix (yi-μY) transposition;
3) 4 × 4 symmetrical matrixes are constructed according to covariance matrix;
Wherein:tr∑X,YIt is matrix ∑X,YMark, if Ai,j=(∑X,Y-∑X,Y T)i,j, Δ=[A23,A21,A12]T, I3It is 3 × 3
Unit matrix, Ai,jFor (∑X,Y-∑X,Y T)i,jThe matrix of construction;
4) 4 × 4 symmetrical matrix Q (∑s are calculatedX,Y) characteristic value and feature vector, the corresponding feature vector of maximum eigenvalue is
Unit quaternion [q0,q1,q2,q3]T;Rotating vector is qR=[q0,q1,q2,q3]T;
5) spin matrix is calculated
6) translation matrix is calculated
T(qR)=[q4,q5,q6]T=μX-R(qR)μY;
(4), coordinate transform is carried out to target point set using spin matrix R and translation matrix T, obtains new target point set, according to
Stopping criterion for iteration judges new target point set with whether the distance with reference to point set is less than given threshold value to realize point cloud registering;
(5), a cloud can be quickly carried out based on the boundary extraction method of cloud centroidal distance feature come Revised ICP algorithm with this to match
Standard obtains accuracy registration effect;
The iteration ends Rule of judgment is as follows:
If new transformation point set is less than given threshold value λ with the European average distance with reference to point set, stop iteration, it is on the contrary then by
New point set continues iteration as initial value, until meeting the requirement of object function;Wherein object function is:
Wherein:N be point cloud data amount, miFor reference point clouds to be registered, R is spin matrix, and T is translation matrix, SiFor target point
Cloud, i=1,2...n.
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