CN104484508A - Optimizing method for noncontact three-dimensional matching detection of complex curved-surface part - Google Patents
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Abstract
The invention discloses an optimizing method for noncontact three-dimensional matching detection of a complex curved-surface part. The optimizing method comprises the steps of sampling a CAD model and a scanning model of the complex curved-surface part to be detected; aligning the CAD model and the scanning model of the complex curved-surface part in gravity; calculating corresponding point pairs between the CAD model and the scanning model of the complex curved-surface part; performing the suckoo search to obtain a transformation matrix; updating the scanning model of the complex curved-surface part according to the transformation matrix; calculating matching error between the scanning model and the CAD model of the complex curved-surface part; then iteratively updating. With the adoption of the optimizing method, the problem in the prior art that the calculation convergence speed is low and local optimization is easily caused can be effectively solved; the optimizing method is particularly applicable to high-precision quality detection of large complex curved-surface parts such as blades of aircraft engines.
Description
Technical Field
The invention belongs to the technical field of part processing and detection, and particularly relates to a non-contact three-dimensional matching detection optimization method for a complex curved surface part.
Background
At present, two types of methods are mainly used for detecting complex curved surface parts with higher precision: contact measurement and non-contact measurement. The contact measurement adopts the traditional three-coordinate measuring machine to sample the parts point by point, the measurement precision is higher, but the method has low measurement efficiency and has special requirements on the material of the measured parts. With the continuous development of computer vision and pattern recognition technology, the non-contact measurement technology is more and more widely applied in the field of complex curved surface part detection with higher measurement speed and precision.
The non-contact measurement utilizes an optical scanner to obtain scanning point sets with different angles, then the point sets are spliced to obtain a complete part scanning point set, and error information of the part is obtained through matching analysis of the scanning point set and a CAD model point set. In a standard detection link, machining errors are generally mainly considered, but the influence of measurement errors on a detection result is ignored; in fact, if the error of the matching itself does not reach the ideal order of magnitude, a wrong measurement result is obtained, and particularly, the matching error is reduced to the minimum in the matching detection process of the high-precision complex curved surface part.
The most widely applied matching detection technology in the prior art is the ICP (iterative closed Point) algorithm proposed by Besl et al in 1992, the famous GeomagicStudio software of Geomagic corporation in the United states and the Scanalyze software developed by Stanford university, which are based on the ICP algorithm to realize the point set matching. However, the ICP algorithm requires the initial positions of two point clouds when performing matching, that is, the two point sets need to be roughly matched before the algorithm is performed, and the algorithm obtains a rigid transformation matrix by singular value decomposition or a quaternion method, which is easy to fall into local optimization, and cannot guarantee to obtain a matched optimal solution for some complex models. CN101847262A discloses a rapid three-dimensional point cloud searching and matching method, wherein the matching process comprises low-precision searching and high-precision searching, and the complexity of the algorithm is correspondingly increased; CN102034104A discloses a method for detecting a feature line based on random sampling consistency in a three-dimensional point cloud, which has a key step of obtaining a feature line of the point cloud, but has a large error for some parts with unobvious sharp features or smooth curvature changes.
Disclosure of Invention
Aiming at the defects or improvement requirements of the prior art, the invention provides a non-contact three-dimensional matching detection optimization method for a complex curved surface part, wherein the three-dimensional matching detection process is designed by adopting an optimized cuckoo algorithm and the key process flow is improved by combining the structural characteristics of the complex curved surface part, and tests show that the method can effectively solve the problems of low convergence rate and easy falling into local optimization in the prior art, and is particularly suitable for high-precision quality detection of large complex curved surface parts such as aeroengine blades.
In order to achieve the above object, according to one aspect of the present invention, there is provided a method for optimizing non-contact three-dimensional matching detection of a complex curved surface part, the method comprising the steps of:
(a) performing primary scanning on a complex curved surface part to be detected to obtain a plurality of three-dimensional measuring points, and then respectively performing secondary sampling on the three-dimensional measuring points and corresponding discrete points of a CAD model to simplify the three-dimensional measuring points and obtain two pieces of point clouds serving as matching comparison objects; in addition, the point cloud corresponding to the CAD model is set asThe measurement point cloud of the corresponding part is set asWherein j is 1q，i＝1,...,np，nq、npRespectively representing the total number of points of the point cloud Q and the point cloud P;
(b) the respective barycenters of the two point clouds Q, P are calculated based on the following formulas (I) and (II)Andthen, two pieces of point clouds are moved to the same XYZ coordinate system, and the respective centers of gravity of the point clouds coincide with the origin of coordinates, whereinEach point in the two-piece point cloud Q, P is represented separately, and each point is represented in the form of three-dimensional coordinate values:
(c) Keeping the point cloud Q fixed, sequentially traversing each point in the point cloud P, searching the point with the closest relative distance in the point cloud Q as a corresponding point, and further forming a point pair, namely obtaining the point pairAndcorresponding;
(d) based on a cuckoo algorithm, randomly generating 6 variables by performing angle rotation on the point cloud P around the XYZ three axes and distance translation along the XYZ three axes in the XYZ coordinate system, and then coding the variables to serve as individuals to form a cuckoo population; then, when iterative calculation is performed, the individual is decoded to generate a rotation matrixAnd translation matrixTwo-part conversion matrixWherein the rotation matrixA rotation matrix and a translation matrix, each representing 3 rows by 3 columns obtained by performing angular rotation of the point cloud P along the xyz three axesRepresents a translation vector of 3 rows × 1 column obtained by performing distance translation of the point cloud P along the xyz three axes, respectively, and then performs updating of the point cloud P according to the following functional expression (three):
(e) Calculating a matching error value ER between the point cloud P and the point cloud Q based on the following formula (IV):
Wherein,representing the neutralization point in the point cloud QThe corresponding point with the nearest distance is represented in the form of three-dimensional coordinate values; | X | | denotes taking the absolute value of X; when the calculated matching error value is larger than a preset matching threshold value, performing iterative updating on the point cloud P based on the function formula in the step (d) until the calculated matching error value is smaller than or equal to the matching threshold value, and obtaining an optimal conversion matrix under the current correspondence;
(f) updating the point cloud P based on the current optimal conversion matrix obtained in the step (e), then calculating a matching error value between the updated point cloud P and the point cloud Q based on the formula (IV), returning to the step (c) to recalculate the corresponding point pair of the point cloud Q and the point cloud P when the calculated matching error value is larger than the matching threshold value, and continuing to circulate until the calculated matching error value is smaller than or equal to the matching threshold value, thereby completing the whole three-dimensional matching detection optimization process.
As a further preference, in step (a), the subsampling is performed using approximate model theory.
As a further preference, in step (c), a K-D tree method is preferably employed to speed up the finding of the point pairs between the two pieces of point cloud Q, P.
As a further preference, the complex curved surface part is preferably an aircraft engine blade.
According to another aspect of the invention, another non-contact three-dimensional matching detection optimization method for a complex curved surface part is provided, which is characterized by comprising the following steps:
(i) performing primary scanning on a complex curved surface part to be detected to obtain a plurality of three-dimensional measuring points, and then respectively performing secondary sampling on the three-dimensional measuring points and corresponding discrete points of a CAD model to simplify the three-dimensional measuring points and the corresponding discrete points of the CAD model, thereby obtaining two pieces of point clouds serving as matching comparison objects; in addition, the point cloud corresponding to the CAD model is set asThe measurement point cloud corresponding to the complex curved surface part is set asWherein j is 1q，i＝1,...,np，nq、npRespectively representing the total number of points of the point cloud Q and the point cloud P;
(ii) the respective barycenters of the two point clouds Q, P are calculated based on the following formulas (I) and (II)Andthen, two pieces of point clouds are moved to the same XYZ coordinate system, and the respective centers of gravity of the point clouds coincide with the origin of coordinates, whereinEach point in the two-piece point cloud Q, P is represented separately, and each point is represented in the form of three-dimensional coordinate values:
(iii) Keeping the point cloud Q fixed and traversing the point clouds P in sequenceFinding a point with the closest relative distance in the point cloud Q as a corresponding point, and further forming a point pair, namely, the point pair is formedAndcorresponding;
(iv) based on a cuckoo algorithm, randomly generating 6 variables by performing angle rotation on the point cloud P around the XYZ three axes and distance translation along the XYZ three axes in the XYZ coordinate system, and then coding the variables to serve as individuals to form a cuckoo population; then, when iterative calculation is performed, the individual is decoded to generate a rotation matrixAnd translation matrixTwo-part conversion matrixWherein the rotation matrixA rotation matrix and a translation matrix, each representing 3 rows by 3 columns obtained by performing angular rotation of the point cloud P along the XYZ three axes of the XYZ coordinate systemTranslation vectors of 3 rows × 1 columns obtained by performing distance translation of the point cloud P along the XYZ three axes of the XYZ coordinate system, respectively, and then, updating the point cloud P is performed in accordance with the following functional expression (three):
(v) Calculating a matching error value ER between the point cloud P and the point cloud Q based on the following formula (IV):
Wherein,representing the neutralization point in the point cloud QThe corresponding point with the nearest distance is represented in the form of three-dimensional coordinate values; | X | | denotes taking the absolute value of X; and when the calculated matching error value is greater than a preset matching threshold value, performing iterative update on the point cloud P based on the functional formula in the step (iv), returning to the step (iii) to recalculate the corresponding point pair of the point cloud Q and the point cloud P, and then continuing to circulate until the calculated matching error value is less than or equal to the matching threshold value, thereby completing the whole three-dimensional matching detection optimization process.
Generally, compared with the prior art, the above technical solution conceived by the present invention mainly has the following technical advantages:
1. the optimized cuckoo algorithm is adopted to design the three-dimensional matching detection process of the complex curved surface part, so that the characteristics of high convergence speed, good robustness and difficulty in falling into local optimum can be fully utilized, and tests show that a high-precision and high-efficiency three-dimensional matching result can be obtained;
2. particularly, the specific process flow is designed with the angle rotation variable and the distance translation variable as individuals to form a population, and meanwhile, a specific optimization strategy is designed, so that the structural characteristics of the complex curved surface part can be correspondingly attached, the initial position of the point cloud is not required, the rough matching operation is not required before the algorithm is executed, and the precision and the efficiency of the matching detection can be further improved;
3. because the two point sets are subjected to secondary sampling by using an approximate model theory before matching operation, the complexity of the model can be reduced and the precision of a matching result can be ensured; in addition, the calculation amount of the translation matrix can be effectively reduced by executing the gravity center alignment operation of the two point sets before the matching operation;
4. the invention particularly provides an optimization strategy with an inner-layer circulation structure and an outer-layer circulation structure, wherein the inner-layer circulation can obtain an optimal conversion matrix under the current corresponding condition, after the inner-layer circulation is finished, the outer-layer circulation gives a new point pair corresponding condition, the inner-layer circulation is repeatedly executed until the outer-layer circulation meets the termination condition, tests show that the optimization strategy can obviously achieve the purpose of minimizing the difference of point cloud point pairs on the spatial position, and correspondingly, the result precision of three-dimensional matching detection is further improved.
Drawings
FIG. 1 is a basic flow diagram of a non-contact three-dimensional match detection method contemplated by the present invention;
FIG. 2 is a flow chart of detection optimization according to a first preferred embodiment of the present invention;
FIG. 3 is a flow chart of detection optimization according to a second preferred embodiment of the present invention;
FIG. 4 is a diagram of a quadratic sampling for exemplary display of initial positions of two sets of points;
FIG. 5 is a schematic diagram for exemplary display of two-slice point set center-of-gravity alignment;
FIG. 6 is a diagram showing the matching result obtained according to the detection optimization procedure shown in FIG. 2;
fig. 7 is a diagram for showing a matching result obtained according to the detection optimization procedure shown in fig. 3.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. In addition, the technical features involved in the embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.
In the prior art, geometric measurement of a workpiece is often performed under the condition that a CAD model is known, actual element information of the workpiece is obtained through a multi-sensor measurement system, a required geometric profile is extracted through data processing, and finally, error judgment is completed through matching detection with the CAD model. The matching detection is to find an optimal rigid body transformation between two image point sets, so that each point in one image point set is matched with a corresponding point in the other point set, and the influence of measurement errors on the part detection result is correspondingly reduced.
Therefore, the method has good global optimization capability by considering that the cuckoo algorithm searches a solution space based on the Levy flight principle aiming at the problem of three-dimensional matching detection of workpieces, particularly complex curved surface parts. Starting from the geometric shape of the point set, the optimal rotation and translation matrix between the two point sets is searched through the optimized cuckoo algorithm iteration, and accordingly the method can achieve the effects of high convergence speed, good robustness, suitability for three-dimensional matching detection of high-precision complex curved surface parts and the like.
Fig. 1 is a basic flow chart of a non-contact three-dimensional matching detection method according to the present invention, and we will specifically describe below a model of a certain type of aircraft engine blade as an example.
Firstly, performing first scanning on an aircraft engine blade model to be detected to obtain a plurality of three-dimensional measurement points, and in order to simplify analysis, performing secondary sampling on a scanning model by using an approximate model theory to simplify the analysis, for example, taking 1000 points for analyzing two models (of course, it is also feasible that the number of analysis points is not equal in practical situations) so as to obtain two pieces of point clouds serving as matching comparison objects; in addition, the point cloud corresponding to the CAD model may be set toThe point cloud corresponding to the complex curved surface part is set asWherein j is equal to i, and the simplified point set is shown in fig. 4, in which the real point set is a CAD model, and the hollow circle point set is a scanning model of a complex curved surface part.
Then, the gravity center of each of the two pieces of point cloud Q, P can be calculated based on the following formulas (one) and (two)Andthen, two pieces of point clouds are moved to the same XYZ coordinate system, and the respective centers of gravity of the point clouds coincide with the origin of coordinates, whereinEach point in the two-piece point cloud Q, P is represented separately, and each point is represented in the form of three-dimensional coordinate values:
The two point clouds with their centers of gravity aligned are shown in fig. 5.
Then, keeping the point cloud Q fixed, sequentially traversing each point in the point cloud P, searching the point with the closest relative distance in the point cloud Q as a corresponding point, and further forming a point pair, namely obtaining the point pairAndcorresponding;
then, based on a cuckoo algorithm, randomly generating three variables rotating around an XYZ coordinate axis and three variables translating around the XYZ coordinate axis in the XYZ coordinate system, and coding the six variables to serve as individuals to form a cuckoo population; in other words, a variable is randomly obtained by performing angle rotation and distance translation on the point cloud P, and the 6 variables are encoded to form individuals of the cuckoo population; then, when iterative calculation is performed, decoding is performed on the individual to generate a rotation matrixAnd translation matrixTwo-part conversion matrixWherein the rotation matrixA rotation matrix of 3 × 3 and a translation matrix representing the rotation matrix obtained by performing angular rotation of the point cloud P along the XYZ three axes of the XYZ coordinate systemFor example, to detect the matching accuracy of the present invention, the scanning model of the complex curved surface part may be sequentially rotated by 20 degrees around the XYZ coordinate axis, sequentially translated by 100 mm, and then matched with the CAD model. Wherein, all the points in the point cloud P are updated, and the expression (III) represents the updated expression form of the point cloud P:
The above expression represents that the complex curved surface part scanning model is updated by using the transformation matrix obtained by the cuckoo algorithm.
In the invention, the method of accelerating the corresponding point pair between two point sets by adopting a K-D tree method is preferably adoptedFinding, assuming calculated point cloud QAnd in the point cloud PCorrespondingly, the matching error ER can be calculated by using the following formula (four):
Wherein,in representing CAD models and in scan modelsThe point is closest to the corresponding point, and each point is represented in the form of three-dimensional coordinate values,respectively representing a 3 × 3 rotation matrix and a 3 × 1 translation vector obtained by respectively performing angular rotation and distance translation on the point cloud P along XYZ three axes of the XYZ coordinate system, | X | | | representing taking an absolute value of X; the loop termination condition of the present invention may be set until the calculated matching error is less than or equal to the preset matching threshold, or may be set when the number of iterations is equal to the specified number of loops.
Fig. 2 and 3 show the optimization strategies according to two preferred embodiments of the present invention, respectively.
For the optimization strategy shown in fig. 2, an inner-layer loop structure and an outer-layer loop structure are provided in an iteration process, the purpose of the inner-layer loop is to obtain an optimal conversion matrix under the current corresponding condition, after the inner-layer loop is finished, the outer-layer loop gives a new point pair corresponding condition, and the inner-layer loop is repeatedly executed until the outer-layer loop meets the termination condition, specifically: when the calculated matching error value is larger than a preset matching threshold value, performing iterative updating on the population based on the optimization function model until the calculated matching error value is smaller than or equal to the matching threshold value, and obtaining a current corresponding optimal conversion matrix; and then, updating the point cloud P based on the obtained current optimal conversion matrix, then calculating a matching error value between the updated point cloud P and the point cloud Q based on a matching error calculation formula, returning to the point pair searching step to recalculate the corresponding relation of the two point clouds when the calculated matching error value is greater than the matching threshold value, and continuing to perform external circulation until the calculated matching error value is less than or equal to the matching threshold value, thereby completing the whole three-dimensional matching detection optimization process.
For the optimization strategy shown in fig. 3, only one layer of loop structure exists in the iteration process, specifically: initializing the cuckoo population, calculating a matching error value ER between the point cloud P and the point cloud Q based on a matching error calculation formula, when the calculated matching error value is larger than a preset matching threshold value, performing iterative updating on the population based on the optimization function model, and directly returning to the step of searching the point pairs for circulation until the calculated matching error value is smaller than or equal to the matching threshold value, thereby completing the whole three-dimensional matching detection optimization process.
The matching results based on the two optimization strategies are respectively shown in fig. 6 and 7, wherein a CAD model is represented by a real point set, a scan model is represented by a hollow dot set, and the higher the contact ratio of each point in the two point sets is, the better the matching effect is. As can be seen from the matching results of FIGS. 6 and 7, the two optimization strategies provided by the invention can both obtain a matching result with higher precision, and can reduce the influence of measurement errors on the detection result of the part to the maximum extent; particularly, the optimization strategy of the inner layer and the outer layer can obviously minimize the difference of the point cloud point pairs on the space position, and correspondingly, the result precision of the three-dimensional matching detection is further improved.
It will be understood by those skilled in the art that the foregoing is only a preferred embodiment of the present invention, and is not intended to limit the invention, and that any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (5)
1. A non-contact three-dimensional matching detection optimization method for a complex curved surface part is characterized by comprising the following steps:
(a) performing primary scanning on a complex curved surface part to be detected to obtain a plurality of three-dimensional measuring points, and then respectively performing secondary sampling on the three-dimensional measuring points and corresponding discrete points of a CAD model to simplify the three-dimensional measuring points and the corresponding discrete points of the CAD model, thereby obtaining two pieces of point clouds serving as matching comparison objects; in addition, the point cloud corresponding to the CAD model is set asThe measurement point cloud corresponding to the complex curved surface part is set asWherein j is 1q，i＝1,...,np，nq、npRespectively representing the total number of points of the point cloud Q and the point cloud P;
(b) the respective barycenters of the two point clouds Q, P are calculated based on the following formulas (I) and (II)Andthen, two pieces of point clouds are moved to the same XYZ coordinate system, and the respective centers of gravity of the point clouds coincide with the origin of coordinates, whereinEach point in the two-piece point cloud Q, P is represented separately, and each point is represented in the form of three-dimensional coordinate values:
(c) Keeping the point cloud Q fixed, sequentially traversing each point in the point cloud P, searching the point with the closest relative distance in the point cloud Q as a corresponding point, and further forming a point pair, namely obtaining the point pairAndcorresponding;
(d) based on a cuckoo algorithm, randomly generating 6 variables by performing angle rotation on the point cloud P around the XYZ three axes and distance translation along the XYZ three axes in the XYZ coordinate system, and then coding the variables to serve as individuals to form a cuckoo population; then, when iterative calculation is performed, the individual is decoded to generate a rotation matrixAnd translation matrixTwo-part conversion matrixWherein the rotation matrixRepresenting a rotation of 3 rows by 3 columns obtained by angular rotation of the point cloud P along the xyz three axes, respectivelyMatrix, translation matrixRepresents a translation vector of 3 rows × 1 column obtained by performing distance translation of the point cloud P along the xyz three axes, respectively, and then performs updating of the point cloud P according to the following functional expression (three):
(e) Calculating a matching error value ER between the point cloud P and the point cloud Q based on the following formula (IV):
Wherein,representing the neutralization point in the point cloud QThe corresponding point with the nearest distance is represented in the form of three-dimensional coordinate values; iix iii represents taking the absolute value of X; when the calculated matching error value is larger than a preset matching threshold value, performing iterative updating on the point cloud P based on the function formula in the step (d) until the calculated matching error value is smaller than or equal to the matching threshold value, and obtaining an optimal conversion matrix under the current correspondence;
(f) updating the point cloud P based on the current optimal conversion matrix obtained in the step (e), then calculating a matching error value between the updated point cloud P and the point cloud Q based on the formula (IV), returning to the step (c) to recalculate the corresponding point pair of the point cloud Q and the point cloud P when the calculated matching error value is larger than the matching threshold value, and continuing to circulate until the calculated matching error value is smaller than or equal to the matching threshold value, thereby completing the whole three-dimensional matching detection optimization process.
2. The three-dimensional matching detection optimization method of claim 1, wherein in step (a), said sub-sampling is performed using an approximate model theory.
3. The method for optimizing three-dimensional matching detection according to claim 1 or 2, wherein in step (c), the search for the point pair between the two point clouds Q, P is accelerated by using a K-D tree method.
4. The three-dimensional matching detection optimization method according to any one of claims 1 to 3, wherein the complex curved surface part is preferably an aircraft engine blade.
5. A non-contact three-dimensional matching detection optimization method for a complex curved surface part is characterized by comprising the following steps:
(i) performing primary scanning on a complex curved surface part to be detected to obtain a plurality of three-dimensional measuring points, and then respectively performing secondary sampling on the three-dimensional measuring points and corresponding discrete points of a CAD model to simplify the three-dimensional measuring points and the corresponding discrete points of the CAD model, thereby obtaining two pieces of point clouds serving as matching comparison objects; in addition, the point cloud corresponding to the CAD model is set asThe measurement point cloud corresponding to the complex curved surface part is set asWherein j is 1q，i＝1,...,np，nq、npRespectively representing the total number of points of the point cloud Q and the point cloud P;
(ii) the respective barycenters of the two point clouds Q, P are calculated based on the following formulas (I) and (II)Andthen, two pieces of point clouds are moved to the same XYZ coordinate system, and the respective centers of gravity of the point clouds coincide with the origin of coordinates, whereinEach point in the two-piece point cloud Q, P is represented separately, and each point is represented in the form of three-dimensional coordinate values:
(iii) Keeping the point cloud Q fixed, sequentially traversing each point in the point cloud P, searching the point with the closest relative distance in the point cloud Q as a corresponding point, and further forming a point pair, namely obtaining the point pairAndcorresponding;
(iv) based on a cuckoo algorithm, randomly generating 6 variables by performing angle rotation on the point cloud P around the XYZ three axes and distance translation along the XYZ three axes in the XYZ coordinate system, and then coding the variables to serve as individuals to form a cuckoo population; then, when iterative calculation is performed, the individual is decoded to generate a rotation matrixAnd translation matrixTwo-part conversion matrixWherein the rotation matrixA rotation matrix and a translation matrix, each representing 3 rows by 3 columns obtained by performing angular rotation of the point cloud P along the XYZ three axes of the XYZ coordinate systemTranslation vectors of 3 rows × 1 columns obtained by performing distance translation of the point cloud P along the XYZ three axes of the XYZ coordinate system, respectively, and then, updating the point cloud P is performed in accordance with the following functional expression (three):
(v) Calculating a matching error value ER between the point cloud P and the point cloud Q based on the following formula (IV):
Wherein,representing the neutralization point in the point cloud QThe closest corresponding point, and each point is seated in three dimensionsThe form of the scalar value is expressed; iix iii represents taking the absolute value of X; and when the calculated matching error value is greater than a preset matching threshold value, performing iterative update on the point cloud P based on the functional formula in the step (iv), returning to the step (iii) to recalculate the corresponding point pair of the point cloud Q and the point cloud P, and then continuing to circulate until the calculated matching error value is less than or equal to the matching threshold value, thereby completing the whole three-dimensional matching detection optimization process.
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