CN108986149A - A kind of point cloud Precision Registration based on adaptive threshold - Google Patents
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Abstract
The invention belongs to three-dimensional measurement technical fields, and in particular to the point cloud Precision Registration based on adaptive threshold in a kind of industrial automation detection process, this method comprises: 1) obtaining workpiece for measurement measurement pointcloud and CAD model point cloud；2) point cloud pretreatment, including establish kd-tree and the setting condition of convergence；3) influence that extraneous data is rejected using calculated adaptive threshold is carried out rigid transformation to measurement pointcloud and reaches accuracy registration purpose.The point cloud Precision Registration of adaptive threshold of the invention can complete the accuracy registration of measurement pointcloud and CAD model during industrial detection based on adaptive threshold, simultaneously, this method is due to can effectively distinguish extraneous background data, calculation amount during having gradually decreased, therefore significantly improve registration efficiency.The method of the invention robustness with higher in the case where industrial automation detects environment.
Description
Technical field
The invention belongs to three-dimensional measurement technical fields, and in particular to based on adaptive in a kind of industrial automation detection process
The point cloud Precision Registration of threshold value.
Background technique
With the development of three-dimensional measurement technology, the measurement accuracy of 3-D measuring apparatus is higher and higher, and measuring speed is also more next
It is faster, industrial detection industry is had become based on measurement model and the Digital Three-Dimensional detection method of CAD model comparative analysis
Main trend.In this detection method, after obtaining measured workpiece surface three dimension point cloud data by 3-D measuring apparatus, need
Measurement pointcloud is aligned with CAD model in advance.This is because measurement pointcloud is the coordinate system one under measurement coordinate system
As establish at the optical center of some camera of measuring device, and CAD model is obtained according to the design coordinate system of its own, and system is worked as
One under the same coordinate system when, pose between the two is different.Be therefore, the accuracy of raising measured workpiece measurement,
Guarantee the measured workpiece accuracy of manufacture and qualification rate, realizes that measurement pointcloud model just becomes Digital Three-Dimensional with being registrated for CAD model
One key of detection technique application.
Although forefathers have had more detailed research to point cloud registering technology, for measurement pointcloud and CAD mould
The accuracy registration of type there is a problem.In automatized three-dimensional detection process, the measurement accessory pair such as fixture, brace generally will use
It measures posture and carries out positioning measurement, and be registrated roughly to reach measurement pointcloud with CAD model point cloud by effectively pre- calibration
Effect, but also result in measurement data comprising the measurement background datas such as accessory, influence subsequent accuracy registration process.
Although existing certain methods can reduce extraneous data to a shadow for cloud accuracy registration result to a certain extent
It rings, but not can solve a large amount of influences of the extraneous data to registration result present in industrial automation detection data.
Its basic reason is that these methods can not effectively distinguish workpiece data and extraneous background data, in accuracy registration mistake
Still participating in calculating using extraneous background data in journey causes registration result mistake occur.
Summary of the invention
For the technical problems in the prior art, the object of the present invention is to provide one kind to be based on adaptive threshold
Point cloud Precision Registration, this method can effectively solve the three-dimensional point cloud that automatic detection in industrial production obtains and sets with industry
The problem of CAD model of meter accuracy registration as caused by extraneous background data fails.
To achieve the above object, the present invention is achieved by the following technical solutions:
A kind of point cloud Precision Registration based on adaptive threshold, comprising the following steps:
(1) workpiece for measurement measurement pointcloud and CAD model point cloud are obtained；
(2) point cloud pretreatment:
(2a) establishes kd-tree: establishing kd-tree respectively to workpiece calibration point cloud to be measured and CAD model point cloud, and is based on
Kd-tree seeks K Neighbor Points of each point in workpiece for measurement measurement pointcloud and CAD model point cloud；
The condition of convergence is arranged in (2b): the condition of convergence of iterative process is arranged, the condition of convergence includes the rotation condition of convergence
With the translation condition of convergence；
(3) rigid transformation is carried out to workpiece calibration point cloud iteration to be measured:
(3a) calculates arest neighbors matching double points: to workpiece calibration point cloud P to be measureddataIn point piUse structure in step (2a)
The kd-tree built is in CAD model point cloud PmodelIt is middle to search corresponding closest approach qi, constitute one group of match point CiTo and retain two o'clock
The distance betweenEach point in workpiece for measurement measurement pointcloud is traversed, to obtain a matching double points collection { Ci}；
(3b) calculates adaptive threshold: setting adaptive threshold dadptive, traverse matching double points collection { Ci, ifThen retain the point pair, otherwise it is assumed that the point is to being extraneous data point pair；Reject extraneous data point pair and to
Survey workpiece calibration point cloud PdataIn corresponding points pi, update and obtain matching double points collection { Ci' and measurement pointcloud Pdata′；
(3c) removes error hiding: according toNumerical values recited is to { Ci' in matching double points are ascending is ranked up, so
The matching double points of certain percentage constitute { C before taking afterwardsi" for solving rigid transformation matrix；
(3d) calculates rigid transformation matrix: in { Ci" in calculate measurement pointcloud and the corresponding one group of Euclidean of CAD model point cloud
Transformation matrix；
(3e) converts workpiece for measurement measurement pointcloud coordinate: according to European transformation matrix in step (3d) to the measurement pointcloud
In each point piCorresponding conversion coordinate is calculated by matrix multiplication, spin matrix R and translation matrix T in data set；
Whether the rigid transformation matrix sought in (3f) judgment step (3d) meets the condition of convergence being arranged in step (2b),
If meeting condition, registration process terminates；Next iteration is carried out in step (3a) if not satisfied, then jumping back to.
Further, the rotation condition of convergence passes through the rotation for judging workpiece for measurement measurement pointcloud in the secondary iterative process
Whether the cosine value cos θ of angle is greater than given threshold rotating value e to obtain, if cos θ > e, for rotation convergence；If cos θ≤
E, then it is not converged to rotate.
Further, the translation condition of convergence is by judging the translation distance D of measurement pointcloud in the secondary iterative process
It is no to be less than given translation threshold value t to obtain, if D < t, for translation convergence；It is not converged to translate if D >=t.
Further, adaptive threshold in the step (3b)WhereinFor { CiIn matching
The average value of point centering distance between two points, σ are { CiIn in matching double points the distance between two o'clock standard deviation, S is to miss
Poor scale factor.
Further, the method for rigid transformation matrix is calculated in the step (3d) are as follows:
α, β, γ all very littles are enabled first and close to 0, by formula
It is transformed to formula
Then according to formulaWith
It calculates to solve by the method for singular value decomposition and rotates around x axis angle [alpha], angle beta is rotated around y-axis, being revolved around z-axis
Gyration γ, size t is translated along x-axisx, along y-axis translate size ty, along z-axis translate size tz, and then pass throughWithObtain spin matrix R and translation matrix T；Wherein, NCIt is
Matching double points collection { CiIn matching double points number, niIt is qiNormal vector, α, β, γ respectively indicate workpiece for measurement measurement pointcloud around
The rotation angle of x, y, z axis, Rx(α) expression rotates around x axis spin matrix corresponding to α angle, Ry(β) is indicated around y-axis rotation β
Spin matrix corresponding to angle, Rz(γ) indicates to rotate spin matrix corresponding to γ angle, r around z-axisijRepresent matrix i-th
Row, jth column element, r11=cos γ cos β, r12=-sin γ cos α+cos γ sin β sin α, r13=sin γ sin α+cos γ
sinβcosα、r21=sin γ cos β, r22=cos γ cos α+sin γ sin β sin α, r23=-cos γ sin α+sin γ sin β
cosα、r31=-sin β, r32=cos β sin α, r33=cos β cos α.
Compared with prior art, the beneficial effects of the present invention are:
(1) the point cloud Precision Registration of adaptive threshold of the invention is based on workpiece for measurement measurement pointcloud and CAD model
Point cloud computing goes out adaptive threshold, and the influence of extraneous data is rejected by adaptive threshold, carries out rigid change to measurement pointcloud
It changes and reaches accuracy registration purpose.This method effectively solves the three-dimensional point cloud and industrial design that automatic detection obtains in industrial production
CAD model accuracy registration as caused by extraneous background data failure the problem of.
(2) the point cloud Precision Registration of adaptive threshold of the invention be due to can effectively distinguish extraneous background data, by
Calculation amount during gradually reducing, therefore improve registration efficiency.
(3) surface error measurement optimization is preferably put using to cloud noise and external acnode interference free performance in the present invention
Method establishes corresponding mathematical model, converts Linear least squares minimization problem for non-linear least square problem, by solving line
Property equation group obtains European transformation matrix, and faster, result of producing effects is more preferable for the algorithm the convergence speed.
(4) the method for the invention robustness with higher in the case where industrial automation detects environment.
Detailed description of the invention
Fig. 1 is the point cloud Precision Registration flow chart of the invention based on adaptive threshold.
Fig. 2 is workpiece for measurement measurement pointcloud iteration rigid transformation flow chart.
Specific embodiment
It shows that example illustrates certain embodiments of the present invention, and should not be construed as limiting model of the invention
It encloses.Present disclosure can be improved from material, method and reaction condition simultaneously, all these improvement should all
It falls within spirit and scope of the invention.
As shown in Figure 1, the point cloud Precision Registration of the adaptive threshold of the present invention preferred embodiment, is directed to
Measurement pointcloud model can be the point cloud model being measured for any components, product or device, below to this
The method for registering of preferred embodiment is illustratively illustrated.
1, workpiece for measurement measurement pointcloud and CAD model point cloud are obtained
Body surface and CAD model are scanned with different view with spatial digitizer respectively first, obtain workpiece for measurement measurement
Point cloud and CAD model three-dimensional point cloud, take workpiece for measurement measurement pointcloud and CAD model point cloud respectively as target point set P and source point
Collect Q.
2, point cloud pretreatment
(2a) establishes kd-tree: establishing kd-tree respectively to workpiece calibration point cloud to be measured and CAD model point cloud, and is based on
Kd-tree seeks K Neighbor Points of each point in workpiece for measurement measurement pointcloud and CAD model point cloud.
Wherein, the construction step of cloud kd-tree in midpoint of the present invention is as follows:
1) variance of all the points in x, y, z dimension in workpiece for measurement measurement pointcloud and CAD model point cloud is sought respectively；
2) the maximum dimension of variance is set as the domain split；
3) all the points are ranked up by the value in the domain split, take median point as root node；
4) point that the value in the domain split is less than to the value in the domain median point split is assigned to left subspace, otherwise is assigned to right son
Space；
5) 1) left subspace and right subspace are respectively repeated steps to 4), until only remaining a data point.
The condition of convergence is arranged in (2b)
Method cardinal principle proposed by the present invention is to be equal to 1992 using Besl in " IEEE Transactions on
Pattern analysis and machine intelligence " in propose iteration closest approach (Iterative
Closest Point, ICP) algorithm to measurement pointcloud carry out rigid transformation reach accuracy registration purpose, it is therefore desirable to iteration mistake
The condition of convergence is arranged in journey.Using the rotation condition of convergence and the translation condition of convergence in this method, when algorithm meets two conditions simultaneously
When be determined as with quasi-convergence, and limit algorithm maximum number of iterations prevents algorithm from falling into endless loop.
The rotation condition of convergence passes through the cosine value for judging the rotation angle of workpiece for measurement measurement pointcloud in the secondary iterative process
Whether cos θ is greater than given threshold rotating value e to obtain, if cos θ > e, for rotation convergence；If cos θ≤e, to rotate not
Convergence.The translation condition of convergence is by judging whether the translation distance D of measurement pointcloud in the secondary iterative process is less than given translation
Threshold value t is obtained, if D < t, for translation convergence；It is not converged to translate if D >=t.
Spin matrix is indicated with R in each iterative process:
α, β, γ respectively indicate rotation angle of the workpiece for measurement measurement pointcloud around x, y, z axis, R in formulax(α) is indicated around x-axis
Spin matrix corresponding to rotation alpha angle, Ry(β) indicates the spin matrix corresponding to the y-axis rotation β angle, Rz(γ) indicate around
Z-axis rotates spin matrix corresponding to γ angle, rijRepresent the i-th row of matrix, jth column element.
According to the value of the available cos θ of the angle of spin matrix-axis representation, the mathematical formulae of specific implementation is as follows:
The translation condition of convergence, which passes through, judges whether the translation distance D of measurement pointcloud in the secondary iterative process is less than given put down
Threshold value t is moved to obtain, is to translate convergence, it is otherwise not converged.WhereintxTo translate size, t along x-axisy
To translate size, t along y-axiszTo translate size along z-axis.
3, rigid transformation is carried out to workpiece calibration point cloud iteration to be measured
The step is mainly by calculating arest neighbors matching double points, calculating adaptive threshold, removal error hiding, calculating rigid transformation
Matrix converts measurement pointcloud coordinate and determines whether to continue six sub-steps of iteration composition, shown in the following attached drawing 2 of detailed process.
(3a) calculates arest neighbors matching double points
To workpiece calibration point cloud P to be measureddataIn point piUsing the kd-tree constructed in step (2a) in CAD model point cloud
PmodelIt is middle to search corresponding closest approach qi, constitute one group of match point CiTo and the distance between retain two o'clockTraverse work to be measured
Each point in part measurement pointcloud, to obtain a matching double points collection { Ci}。
(3b) calculates adaptive threshold
Adaptive threshold is setWhereinFor { CiIn distance between two points in matching double points
Average value, σ are { CiIn in matching double points the distance between two o'clock standard deviation, s is the error scale factor；Traverse match point
To collection { Ci, ifThen retain the point pair, otherwise it is assumed that the point is to being extraneous data point pair；Reject unrelated number
Strong point pair and workpiece for measurement measurement pointcloud PdataIn corresponding points pi, update and obtain matching double points collection { Ci' and measurement pointcloud
Pdata′。
Adaptive threshold in the present embodiment makes nothing for effectively distinguishing to workpiece data and extraneous background data
It closes background data and is not involved in calculating during accuracy registration, to improve registration result accuracy rate.
(3c) removes error hiding
During calculating matching double points, unique judgment criteria is Euclidean distance, can inevitably there is the matching of mistake
Point pair, it is therefore desirable to matching double points be screened, the method choice matching double points of percentage are used in the present invention.According toNumerical values recited is to { Ci' in matching double points are ascending is ranked up, the matching double points of certain percentage are constituted before then taking
{Ci" for solving rigid transformation matrix；
(3d) calculates rigid transformation matrix
Surface error measurement optimization side is preferably put using to cloud noise and external acnode interference free performance in the present invention
Method establishes corresponding mathematical model, then obtains rigid transformation matrix, including spin matrix R by solving corresponding objective function
With translation matrix T two parts, the mathematical formulae of specific implementation is as follows:
Wherein, NCIt is matching double points collection { CiIn matching double points number, niIt is qiNormal vector.It is used in solution procedure
The linear solution method that Low was proposed in " Chapel Hill, University of North Carolina " in 2004,
Since spatial position is relatively between workpiece for measurement measurement pointcloud and MODEL C AD, it is possible to think α, β, γ all very littles and
Close to 0, to there is sin θ ≈ θ, cos θ ≈ 1, T=(t is enabledx, ty, tz)T, carrying out derivation can be by objective function approximation
Conversion is as follows:
Then according to formulaWith
It calculates to solve by the method for singular value decomposition and rotates around x axis angle [alpha], angle beta is rotated around y-axis, rotating angle around z-axis
γ, size t is translated along x-axisx, along y-axis translate size ty, along z-axis translate size tz, and then pass throughWithObtain spin matrix R and translation matrix T；Wherein, α, β,
γ respectively indicates rotation angle of the workpiece for measurement measurement pointcloud around x, y, z axis, Rx(α) expression rotates around x axis corresponding to α angle
Spin matrix, Ry(β) indicates the spin matrix corresponding to the y-axis rotation β angle, Rz(γ) indicate rotate around z-axis γ angle it is right
The spin matrix answered, rijRepresent the i-th row of matrix, jth column element, r11=cos γ cos β, r12=-sin γ cos α+cos γ sin
βsinα、r13=sin γ sin α+cos γ sin β cos α, r21=sin γ cos β, r22=cos γ cos α+sin γ sin β sin α,
r23=-cos γ sin α+sin γ sin β cos α, r31=-sin β, r32=cos β sin α, r33=cos β cos α.
(3e) converts workpiece for measurement measurement pointcloud coordinate
It is right using rigid body translation formula P1=R*P+T according to the spin matrix R and translation matrix T sought in step (3d)
Each point p in the measurement pointcloudiCorresponding conversion coordinate is calculated in data set.
Whether the rigid transformation matrix sought in (3f) judgment step (3d) meets the condition of convergence being arranged in step (2b),
If meeting condition, registration process terminates；Next iteration is carried out in step (3a) if not satisfied, then jumping back to.
The point cloud Precision Registration of adaptive threshold of the invention is based on workpiece for measurement measurement pointcloud and CAD model point
Cloud computing goes out adaptive threshold, and the influence of extraneous data is rejected by adaptive threshold, carries out rigid transformation to measurement pointcloud
Reach accuracy registration purpose.This method effectively solves the three-dimensional point cloud that automatic detection in industrial production obtains and industrial design
The problem of CAD model accuracy registration as caused by extraneous background data fails.Simultaneously as extraneous background number can be distinguished effectively
According to, calculation amount during having gradually decreased, therefore improve registration efficiency
The foregoing is only a preferred embodiment of the present invention, but scope of protection of the present invention is not limited thereto,
Anyone skilled in the art in the technical scope disclosed by the present invention, according to the technique and scheme of the present invention and its
Inventive concept is subject to equivalent substitution or change, should be covered by the protection scope of the present invention.
Claims (5)
1. a kind of point cloud Precision Registration based on adaptive threshold, which comprises the following steps:
(1) workpiece for measurement measurement pointcloud and CAD model point cloud are obtained；
(2) point cloud pretreatment:
(2a) establishes kd-tree: establishing kd-tree respectively to workpiece calibration point cloud to be measured and CAD model point cloud, and is based on kd-
Tree seeks K Neighbor Points of each point in workpiece for measurement measurement pointcloud and CAD model point cloud；
The condition of convergence is arranged in (2b): the condition of convergence of iterative process is arranged, the condition of convergence includes rotation condition of convergence peace
Move the condition of convergence；
(3) rigid transformation is carried out to workpiece calibration point cloud iteration to be measured:
(3a) calculates arest neighbors matching double points: to workpiece calibration point cloud P to be measureddataIn point piUse what is constructed in step (2a)
Kd-tree is in CAD model point cloud PmodelIt is middle to search corresponding closest approach qi, constitute one group of match point CiTo and retain between two o'clock
DistanceEach point in workpiece for measurement measurement pointcloud is traversed, to obtain a matching double points collection { Ci}；
(3b) calculates adaptive threshold: setting adaptive threshold dadptive, traverse matching double points collection { Ci, ifThen retain the point pair, otherwise it is assumed that the point is to being extraneous data point pair；Reject extraneous data point pair and to
Survey workpiece calibration point cloud PdataIn corresponding points pi, update and obtain matching double points collection { Ci' and measurement pointcloud Pdata′；
(3c) removes error hiding: according toNumerical values recited is to { Ci' in matching double points are ascending is ranked up, then take
The matching double points of preceding certain percentage constitute { Ci" for solving rigid transformation matrix；
(3d) calculates rigid transformation matrix: in { Ci" in calculate measurement pointcloud and the corresponding one group of euclidean transformation of CAD model point cloud
Matrix；
(3e) converts workpiece for measurement measurement pointcloud coordinate: according to European transformation matrix in step (3d) to every in the measurement pointcloud
A point piCorresponding conversion coordinate is calculated by matrix multiplication, spin matrix R and translation matrix T in data set；
Whether the rigid transformation matrix sought in (3f) judgment step (3d) meets the condition of convergence being arranged in step (2b), if full
Sufficient condition, then registration process terminates；Next iteration is carried out in step (3a) if not satisfied, then jumping back to.
2. a kind of point cloud Precision Registration based on adaptive threshold according to claim 1, which is characterized in that described
Rotation the condition of convergence pass through judge the rotation angle of workpiece for measurement measurement pointcloud in the secondary iterative process cosine value cos θ whether
It is obtained greater than given threshold rotating value e, if cos θ > e, for rotation convergence；It is not converged to rotate if cos θ≤e.
3. a kind of point cloud Precision Registration based on adaptive threshold according to claim 1, which is characterized in that described
The translation condition of convergence is by judging whether the translation distance D of measurement pointcloud in the secondary iterative process is less than given translation threshold value t
It obtains, if D < t, for translation convergence；It is not converged to translate if D >=t.
4. a kind of point cloud Precision Registration based on adaptive threshold according to claim 1, which is characterized in that described
Adaptive thresholding times in step (3b)WhereinFor { CiIn in matching double points distance between two points it is flat
Mean value, σ are { CiIn in matching double points the distance between two o'clock standard deviation, s is the error scale factor.
5. a kind of point cloud Precision Registration based on adaptive threshold according to claim 1, which is characterized in that described
The method of rigid transformation matrix is calculated in step (3d) are as follows:
α, β, γ all very littles are enabled first and close to 0, by formula
It is transformed to formula
Then according to formulaWithIt calculates logical
The method for crossing singular value decomposition solves and rotates around x axis angle [alpha], angle beta is rotated around y-axis, angle γ is rotated around z-axis, is flat along x-axis
Move size tx, along y-axis translate size ty, along z-axis translate size tz, and then pass through
WithObtain spin matrix R and translation matrix T；Wherein, NCIt is matching double points collection { CiIn matching double points number, ni
It is qiNormal vector, α, β, γ respectively indicate rotation angle of the workpiece for measurement measurement pointcloud around x, y, z axis, Rx(α) is indicated around x-axis
Spin matrix corresponding to rotation alpha angle, Ry(β) indicates the spin matrix corresponding to the y-axis rotation β angle, Rz(γ) indicate around
Z-axis rotates spin matrix corresponding to γ angle, rijRepresent the i-th row of matrix, jth column element, r11=cos γ cos β, r12=-
sinγcosα+cosγsinβsinα、r13=sin γ sin α+cos γ sin β cos α, r21=sin γ cos β, r22=cos γ
cosα+sinγsinβsinα、r23=-cos γ sin α+sin γ sin β cos α, r31=-sin β, r32=cos β sin α, r33=
cosβcosα。
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Application publication date: 20181211 |