CN110310322A - Method for detecting assembly surface of 10-micron-level high-precision device - Google Patents
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Abstract
The invention provides a method for detecting the assembly surface of a 10-micron-level high-precision device, which is characterized by comprising the following steps of: step 1, calculating a device surface estimation model mi(ii) a Step 2, calculating all points in the point cloud data to an estimated model miA distance d of; step 3, appointing a threshold value t, and taking a point satisfying d < t as an in-office point; step 4, for the pre-estimated model miEvaluating to obtain an evaluation score, and if the evaluation score is the highest, judging that the current estimation model m is the highestiIs the best estimation model; step 5, repeating the step 2 to the step 4, and obtaining an optimal model m when the iteration times are increased to k; step 6, dividing the local points into a plurality of point sets based on a clustering segmentation algorithm; in the step 7, the step of,and judging the concave-convex of the point set according to the position relation between the point set and the surface to obtain the maximum surface error of each surface. The invention can accurately and rapidly reflect the convex and concave conditions of the assembly surface and provides convenience for the subsequent device matching judgment work.
Description
Technical field
The invention belongs to Virtual Assembling Technology fields, and in particular to a kind of 10 micron order high precision component assembly surfaces detection
Method.
Background technique
Currently, most of virtual assembly systems are carried out according to the assembly constraint of device, the cooperation of assembly surface is had ignored
Situation causes Virtual assemble and the larger problem of practical set process variances.And it is based on physical attribute assembly system, it is moved
Mechanics Simulation needs a large amount of calculating, inefficiency.
What is extracted assembly subject surface characteristic information first has to do is exactly Segmentation of Data Set, i.e., surface point cloud with it is non-planar
Point cloud is split, then analyzes the out-of-flatness situation on each face.It currently exists exhibiting high surface detection and point cloud segmentation is calculated
Method, according to its working principle, can be divided into three classes method: clustering algorithm, region growing method and the method based on models fitting.
Clustering algorithm is the point concentrated from point cloud data, certain point is judged according to the similarity relationships between each point
Affiliated area.In clustering, reflect it generally by the otherness between intuitive image or measurement object
Between similarity.The former is to carry out Projection Character to all images, and the latter is the distance between computing object.Therefore, distance
Calculation method have very big influence for cluster segmentation, currently used several distance calculating methods have: Euclidean distance is cut
Than snow husband distance, correlation distance and mahalanobis distance.
According to the method that distance calculates, the similarity two objects can be measured.In practical applications, object
Attribute is different, and the method that distance calculates may also be different.The distance of object calculates, and has directly to the validity of clustering algorithm
It influences, so should be carefully selected when actual selection calculates the method for distance.
Algorithm of region growing and clustering algorithm have similarity, are all that it is real constantly to divide whole region from a point
Now divide.Except that clustering algorithm is usually the ownership for removing to judge certain point according to the distance of distance, region growing is calculated
Method can go to divide region according to the property of three-dimensional point cloud.Such as in general point cloud, according to the method for each point in point cloud data
Vector sum curvature value, and then go to judge which kind of the point belongs to.
Method based on models fitting can extract the shape wanted, such as straight line, plane and spherical surface from cloud.
Wherein most representative algorithm is stochastical sampling consistency (Random Sample Consensus) algorithm, which can be with
It is concentrated from one group of testing data comprising " point not in the know ", the parameter of mathematical model is estimated by way of iteration.RANSAC is calculated
The basic assumption of method is the mathematical model that all intra-office points all meet estimation, and point not in the know does not meet the mathematical model, removes this
Except point belong to noise spot.
Clustering algorithm, region growing and stochastical sampling consistency algorithm respectively have advantage and disadvantage, and application scenarios are also different.It is poly-
Class algorithm usually handles the point cloud of discretization, and discrete point cloud is sorted out, and high precision component model is after pretreatment
It is whole point cloud sector domain mostly, needs micronization processes again with clustering algorithm segmentation is subsequent.It is raw based on the region of curvature and normal
Long algorithm is suitble to distinguish the apparent region in boundary, and the algorithm needs to calculate the curvature and normal of each point, calculates the time
It is longer.Stochastical sampling consistency algorithm (RANSAC) may only estimate a model from specific set of data.Although the algorithm can be with
Robust iterative, but it is too small to work as intra-office point proportion in testing data, it is necessary to great the number of iterations, so that algorithm is estimated
Time sharply increases.Also, stochastical sampling consistency algorithm estimates model quality according to the quantity of intra-office point, often makes in this way
Obtain from highdensity cloud of model farther out.
Summary of the invention
The object of the present invention is to provide a kind of 10 micron order high precision component assembly surface detection methods, by point cloud according to spy
Fixed shape is split, and calculates the maximum height of projection of each assembly surface, is mentioned for device matching judgement in Virtual assemble
It is supported for data.
The present invention provides a kind of 10 micron order high precision component assembly surface detection methods, include the following steps:
Step 1, according to the type of device surface model, minimum sampling number n is selected, is selected at random from device point cloud data
N sampled point is taken, according to sampled point calculating device surface prediction model mi；The prediction model miType packet plane, spherical surface and
Cylindrical surface；
Step 2, judge prediction model miWhether it is consistent with ideal model, if not being consistent, calculates all the points in point cloud data
To prediction model miDistance d；
Step 3, a model bias threshold value t is specified, the point of d < t will be met as intra-office point；
Step 4, initial best model evaluation score s and best model m algorithm are set, and substantially determines the number of iterations k, it is right
Prediction model miIt is evaluated, obtains an evaluation score, if evaluation score highest, current prediction model miFor best estimate
Model updates m and s value；
Step 5, step 2 is repeated to step 4, and when the number of iterations increases to k, the quantity of intra-office point will be basically unchanged, and be obtained
To best model m；
Step 6, cluster segmentation is carried out based on play a game outer point cloud data of Euclidean clustering algorithm, point not in the know is divided into several points
Collection；
Step 7, the bumps at position where judging point set according to the positional relationship of the position of point set and face, if lug boss
Position acquires the height of protrusion, finally obtains the maximum surface error in each face.
Further, in step 2, spherical surface and cylinder model judge prediction model m according to model radius sizeiWith ideal mould
Whether type is consistent, if prediction model miModel bias threshold value is deviated more than with 1 with the ratio between ideal model radius, then mould is estimated in judgement
Type miDeviation is larger, carries out next iteration and recalculates prediction model mi。
Further, the value of the t of model bias threshold value described in step 3 is 0.05.
Further, the calculation formula of the number of iterations k described in step 4 is as follows:
Wherein, ω is to randomly select a point, which is the probability of intra-office point, and p is to obtain correct model after algorithm is run
Idealized probability, taking 0.95 or 0.99, n is prediction model miThe seed point number of selection.
Further, the step 5 includes:
Based on evaluation function g (M) to the prediction model m of each iterationiIt is evaluated:
Wherein, M represents the model to be evaluated, k0, k1, k2Be the weight of evaluation function, take 0.5,0.3 and 0.2 respectively, r and
R ' respectively indicates ideal radius value and real radius, and n and n ' respectively indicate all the points quantity and intra-office point quantity that model includes,
σ indicates that model intra-office point arrives the range averaging distance of model, and di indicates the i-th point of distance to model M in intra-office point.
Compared with prior art the beneficial effects of the present invention are:
For this method by extraction device assembly surface point cloud data, the geometry that analysis point cloud data obtains each fitting surface is special
Sign, the fitting surface that can be extracted are plane, spherical surface and cylindrical surface, can accurately and quickly reflect assembly surface protrusion and recess
Situation matches for subsequent device and determines that work provides convenience.
Detailed description of the invention
Fig. 1 is a kind of flow chart of 10 micron order high precision component assembly surface detection methods of the invention；
Fig. 2 is the algorithm flow chart of device surface modeling and segmentation of the present invention.
Specific embodiment
The present invention is described in detail for each embodiment shown in reference to the accompanying drawing, but it should be stated that, these
Embodiment is not limitation of the present invention, those of ordinary skill in the art according to these embodiments made by function, method,
Or equivalent transformation or substitution in structure, all belong to the scope of protection of the present invention within.
For the assembling model reconstructed by three-dimensional point cloud in virtual assembly environment, can assembly ware surface meeting
There is part to deform (protrusion), in the suitable situation of assembly ware size specification, these deformed regions cause assembly to fail
Position.This method main function is the highest protrusion distance found apart from assembly surface.This method is by being divided into point cloud data
Two big collections, one is intra-office point sets, and one is point sets not in the know, with T, I1,I2…InIndicate them, wherein T representative office
Outer point set, InRepresent the set put on n-th of face of device.It only needs to judge in this way flat where the midpoint point set T not in the know to them
The distance in face, so that it may judge concave-convex region and height.The main flow of this method is as shown in Figure 1.
(1) device surface models
In the method, the assembly of rigid body device is pertained only to, does not consider the factors such as elasticity, the extruding of device, the shape on surface
Shape is divided into: plane, spherical surface, cylindrical surface.The calculation method and representation on three kinds of surfaces are as follows.
For plane, three not conllinear scatterplots are assured that a plane, so in stochastical sampling consistency algorithm
During estimating areal model, it is 3 that we, which take smallest sample sub-set size n, that is, at least takes 3 points to estimate as model every time
Meter set.It is (a, b, c, d) respectively that the areal model parameter being calculated, which has 4, wherein (a, b, c) indicates the normal direction of plane
Amount, d is constant.Expression formula such as formula (1) is calculated.
Ax+by+cz+d=0； (1)
For spherical surface, in two-dimensional space, three not conllinear scatterplots can determine a standard round model, in three-dimensional space
Between in only need to find other a three coplanar points of getting along well again and can determine a spherical surface, i.e. formula (2).Spherical surface segmentation is minimum
Sample set size n is set as 4.The model parameter being calculated is (x0,y0,z0, r), wherein (x0,y0,z0) represent the centre of sphere
Three-dimensional coordinate, r represent radius of a ball value.
(x-x0)2+(y-y0)2+(z-z0)2=r2； (2)
For cylindrical surface, in order to establish cylinder surface model, 2 point p for having normal vector are needed1And p2.Firstly, enabling a=n1×
n2The direction of cylinder axis is determined, then by two o'clock in the linear projection to ax=0 plane representated by the normal vector, and with them
Intersection point be center c, radius r is set as projection plane c and p1The distance between.In the smallest sample subset for calculating cylindrical surface
Size n is set as 2.In formula (3), (x0,y0,z0) it is a bit on cylinder axis, (l, m, n) is cylinder axis direction vector
A, r are the radius of cylinder, this seven parameters determine a cylinder model.
(2) assembly ware surface segmentation
In this stage, system carries out surface segmentation to it according to the type of device, is the flatness error of next stage
Detection provides an ideal surfaced.This method divides device surface using improved RANSAC algorithm, compared to traditional
RANSAC algorithm, modified hydrothermal process increase detection module after estimating model, which estimates model and design parameter
Whether specification coincide, if estimation model and design parameter, which coincide, illustrates to estimate that model meets basic demand, can carry out subsequent
Sub-set size calculate；If misfitting expression estimates that model and design parameter difference are excessive, it is next to skip subsequent calculating progress
Secondary iteration.
Before executing algorithm, initial best model evaluation score s (being initially set to 0) and best model m algorithm are set,
And substantially determine the number of iterations k.The k of the number of iterations is calculated as shown in formula (4), and wherein ω is to randomly select a point, the point
It is the probability of intra-office point, p is to obtain the idealized probability of correct model after algorithm is run, and taking 0.95 or 0.99, n is prediction model institute
The seed point number of selection.
After obtaining the number of iterations k, assembly ware surface segmentation is directly carried out, the specific steps are as follows:
1. selecting minimum sampling number n according to required model, n sampled point being randomly selected from cloud, according to sampled point
Computation model mi；
2. whether judgment models are consistent substantially with ideal model, if not being inconsistent all the points in joint account data set arrives model
Distance d
3. specifying a threshold value t (this paper value 0.05), the point of d < t will be met as intra-office point；
4. couple model miEvaluation obtains an evaluation score, if evaluation score highest, "current" model miFor best estimate mould
Type updates m and s value；
5. repeating step 2 to step 4, when the number of iterations increases to k, the quantity of intra-office point will be basically unchanged, and be obtained most
Excellent model m.
Algorithm flow is as shown in Figure 2.
After estimating out model, first model and ideal model are compared, spherical surface and cylinder model are according to model half
Diameter size.The ratio between radius of two models deviates more than model bias threshold value with 1 and then thinks that model bias is larger, carries out next time
Iteration recalculates model, and model bias threshold value takes 0.05 herein.The prediction model to each iteration is needed to comment in algorithm
Valence, the evaluation function g (M) of model, wherein M represents the model to be evaluated, such as formula (5).
Wherein (k0,k1,k2) be evaluation function weight, this method takes 0.5,0.3 and 0.2 respectively, and r and r ' are respectively indicated
Ideal radius value and real radius, n and n ' respectively indicate all the points quantity and intra-office point quantity that model includes, σ representative model
Intra-office point to model range averaging distance, such as formula (6).diRepresent the i-th point of distance to model M in intra-office point.In office
In the case that interior accounting difference is little, according to average distance come the quality of judgment models, mould is estimated in the smaller representative of average distance
The compatible degree of type and intra-office point is higher, on the contrary then lower.
(3) outlier detection
Rigging error refers to flatness error, i.e., variation of the tested actual surface with respect to its ideal plane in the method.
In order to measure flatness error, needs again to cluster not in the know cloud, determine the positional relationship of every one kind point and ideal surface not in the know.
In the case where point cloud data is divided into intra-office point drawn game exterior point, point cloud data not in the know is classified.In order to measure assembly pair
The flatness error of elephant needs point not in the know being divided into several point sets { B1,B2,...,Bm}.Because not in the know after dividing ideal plane
Point is substantially at discrete state.
It needs to find the point got together point set i.e. not in the know after dividing ideal plane, therefore selects Euclidean clustering algorithm to inspection
Point cloud data after survey is split.Meanwhile because also unavoidably there is noise even across pretreatment in point cloud data,
After point cluster not in the know, the quantity judgement according to every a kind of point is point or noise spot not in the know.Concrete operations are as follows:
1) creation KD tree P indicates left point cloud data acquisition system；
2) an empty cluster list C is set, and the queue Q for the point for needing to check；
3) for each point pi∈ P adds piTo current queue Q；,
4) one group of p is searched in radius riNeighbours' meter of point does point set
5) forInterior every bit checks whether it is processed, if being added to current queue Q without processed；
6) when all points of queue Q have all been handled, queue Q is to cluster C for addition, and empties queue Q；
7) step 3) is repeated to 6), and until all the points are disposed, algorithm stops.
According to BmPosition and face { I1,I2,...,InPositional relationship judge the position be it is convex or recessed, if raised
Position acquires the height of protrusion, finally obtains the maximum surface error in each face.
The present invention has the following technical effect that.
(1) it is screened and is judged in advance after model pre-estimating, for the result progress close to original design model
Subsequent calculating, is otherwise sampled next time.Unnecessary consumption can be reduced in this way, improve efficiency of algorithm.
(2) evaluation function of optimization algorithm is changed to a collective model evaluation side from model intra-office point quantitative approach
Method.This method primarily focuses on the standard deviation and intra-office point quantity of model parameter, intra-office point and prediction model distance, can be improved
The precision of model pre-estimating.
It is obvious to a person skilled in the art that invention is not limited to the details of the above exemplary embodiments, Er Qie
In the case where without departing substantially from spirit or essential attributes of the invention, the present invention can be realized in other specific forms.Therefore, no matter
From the point of view of which point, the present embodiments are to be considered as illustrative and not restrictive, and the scope of the present invention is by appended power
Benefit requires rather than above description limits, it is intended that all by what is fallen within the meaning and scope of the equivalent elements of the claims
Variation is included within the present invention.
Claims (5)
1. a kind of 10 micron order high precision component assembly surface detection methods, which comprises the steps of:
Step 1, according to the type of device surface model, minimum sampling number n is selected, randomly selects n from device point cloud data
A sampled point, according to sampled point calculating device surface prediction model mi；The prediction model miType packet plane, spherical surface and circle
Cylinder；
Step 2, judge prediction model miWhether it is consistent with ideal model, if not being consistent, calculates in point cloud data all the points to estimating
Model miDistance d；
Step 3, a model bias threshold value t is specified, the point of d < t will be met as intra-office point；
Step 4, it sets initial best model evaluation score s and best model m algorithm, and substantially determines the number of iterations k, to estimating
Model miIt is evaluated, obtains an evaluation score, if evaluation score highest, current prediction model miFor best estimate model,
Update m and s value；
Step 5, step 2 is repeated to step 4, and when the number of iterations increases to k, the quantity of intra-office point will be basically unchanged, and be obtained most
Good model m；
Step 6, cluster segmentation is carried out based on play a game outer point cloud data of Euclidean clustering algorithm, point not in the know is divided into several point sets；
Step 7, the bumps at position are asked if boss where judging point set according to the positional relationship of the position of point set and face
Raised height is obtained, the maximum surface error in each face is finally obtained.
2. 10 micron order high precision component assembly surface detection method according to claim 1, which is characterized in that step 2
In, spherical surface and cylinder model judge prediction model m according to model radius sizeiWhether it is consistent with ideal model, if prediction model mi
Model bias threshold value is deviated more than with 1 with the ratio between ideal model radius, then judges prediction model miDeviation is larger, carries out next time
Iteration recalculates prediction model mi。
3. 10 micron order high precision component assembly surface detection method according to claim 2, which is characterized in that step 3
Described in model bias threshold value t value be 0.05.
4. 10 micron order high precision component assembly surface detection method according to claim 1, which is characterized in that step 4
Described in the number of iterations k calculation formula it is as follows:
Wherein, ω is to randomly select a point, which is the probability of intra-office point, and p is to obtain the reason of correct model after algorithm is run
Think probability, taking 0.95 or 0.99, n is prediction model miThe seed point number of selection.
5. 10 micron order high precision component assembly surface detection method according to claim 4, which is characterized in that the step
Rapid 5 include:
Based on evaluation function g (M) to the prediction model m of each iterationiIt is evaluated:
Wherein, M represents the model to be evaluated, k0, k1, k2It is the weight of evaluation function, takes 0.5,0.3 and 0.2 respectively, r and r ' points
Not Biao Shi ideal radius value and real radius, n and n ' respectively indicate all the points quantity and intra-office point quantity that model includes, σ table
Representation model intra-office point to model range averaging distance, di indicate intra-office point in i-th point arrive model M distance.
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