CN112002013A - Three-dimensional overlapping type modeling method - Google Patents
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Abstract
The invention discloses a three-dimensional overlapped modeling method.A movement system controls a line laser sensor to acquire height information of accessories in a bow-shaped path; generating laser array point cloud data measured each time according to the moving speed and distance of the moving system and the parameters of the line laser sensor; splicing single-side point clouds of the accessories through the overlapping characteristics of the overlapped point cloud data areas; sequentially completing point cloud data acquisition of the remaining curved surfaces; optimizing an ICP (inductively coupled plasma) splicing algorithm according to the distance weight to perform three-dimensional splicing of point clouds; and (4) simplifying and triangularizing the point cloud model of the accessory. The laser point cloud data of the invention has very high information integrity and overlapping rate ROAAre positively correlated; the distance weight optimization ICP (inductively coupled plasma) splicing algorithm effectively reduces the scale of point cloud registration, and greatly reduces the time for three-dimensional point cloud splicing; the laser measurement has strong stability in industrial environment, and the model accurately restores the space torsion characteristics of the fitting.
Description
Technical Field
The invention belongs to the field of damage repair of aerospace accessories, and particularly relates to a three-dimensional overlapping type modeling method.
Background
Most parts of aerospace devices are prone to damage such as fracture, scribing and deformation when working in severe environments with high temperature and high pressure for a long time. The three-dimensional digital modeling of the damaged part is always a key step of material increase repair, and the precision of the model directly influences the quality of cladding manufacture. In the prior art, the measurement of the model is roughly divided into a contact type and a non-contact type, and comprises the following steps: according to a traditional contact type three-coordinate measuring method, a measuring pin needs to be frequently replaced in the measuring process, the working efficiency is low, and secondary damage is easy to generate; the non-contact speckle pattern modeling method based on the stereoscopic vision system aims at smooth metal surface objects such as engine blades and the like, and has the advantages of serious light reflection linearity and low precision; the non-contact grating projection three-dimensional reconstruction method based on the structured light has the advantages that the stability is greatly interfered by ambient light, and a disordered point cloud splicing method is complicated; the non-contact line laser-based modeling method has good environmental adaptability, but the measurement precision is greatly reduced along with the increase of the measurement range, and the redundant data volume of the array point cloud is large.
In the above problem, the precision of the digital model of the aerospace accessory and the efficiency of the three-dimensional digital point cloud based splicing processing can be greatly influenced.
The traditional line laser modeling method is mainly realized by adopting a multi-axis motion system and a line laser sensor, and specifically, a measured object is placed on a horizontal table, and a driving system carries laser to carry out point cloud collection on each surface of the object. And then, converting the height information of each surface into array point cloud data for splicing. The point cloud splicing mainly adopts an Iterative Closest Point (ICP) algorithm, the method utilizes geometric information among point sets to carry out multiple iterations to obtain the optimal solution of a rigid body transformation matrix, but the requirement on the initial position of a point cloud block is high, and the calculated amount in the iteration is large.
Disclosure of Invention
In order to solve the above problems, the present invention provides a three-dimensional overlapped modeling method, which has a very high integrity of laser point cloud data and an overlapping rate ROAAre positively correlated; the efficiency of generating three-dimensional point cloud data is high, the spatial distribution of the laser array point cloud is uniform, and the processing is convenient; the distance weight optimization ICP (inductively coupled plasma) splicing algorithm effectively reduces the scale of point cloud registration, and greatly reduces the time for three-dimensional point cloud splicing; the laser measurement has strong stability in industrial environment, and the model is accurately reducedSpatial torsion features of the fitting.
In order to achieve the technical purpose and achieve the technical effect, the invention is realized by the following technical scheme:
a three-dimensional overlapped modeling method comprises the following steps:
step 1) dividing a plurality of connected regions clockwise according to the surface shape characteristics of aerospace accessories;
step 2), designing a light path of the line laser sensor according to the Schlemm's law and a direct laser triangulation method, and fixing the light path on a multi-axis motion system after calibration;
step 3), the accessory is placed on a single-shaft rotating platform, and the axis of the accessory is superposed with the zero position surface of the linear laser sensor;
step 4) the motion system controls the line laser sensor to collect height information of the accessory in a bow-shaped path, wherein each measurement range is A multiplied by B, each translation distance is D, and the overlapping rate of each sampling range
Step 5) generating laser array point cloud data (x) measured each time according to the moving speed and distance of the moving system and the line laser sensor parametersij,yj,hij) Wherein i and j respectively represent the serial number of the point cloud block and the frame serial number in the current point cloud block;
step 6) splicing single-side point clouds of the accessories through the overlapping features of the overlapped point cloud data areas;
step 7) rotating the rotating platform to sequentially complete point cloud data acquisition of the remaining curved surfaces;
step 8) optimizing an ICP (inductively coupled plasma) splicing algorithm according to the distance weight to perform three-dimensional splicing of point clouds;
and 9) simplifying and triangulating the point cloud model of the accessory to generate a three-dimensional digital model of the accessory.
Further, the technical steps of the distance weight optimization ICP splicing algorithm are as follows:
step 81) setting the source point cloud set as P ═ Pi∈R3,i＝1,2,…,NpThe set of clouds of target points is Q ═ Qj∈R3,j＝1,2,…,NqAnd f, taking elements in the set as three-dimensional coordinate vectors of points, and taking an optimal target model of ICP (inductively coupled plasma) based on Euclidean distance residual errors as
Where R and T are rotation and translation matrices in rigid body transformation, qkAnd pkA group of matching point pairs;
step 82) setting target precision and maximum iteration number eta;
step 83) establishing an initial corresponding relationship, traversing the point cloud P according to dk＝min{||qk-pk||2Searching for p in a point cloud QkCorresponding point q of (2)kForming a point pair;
step 84) distributing weight values for each group of point pairs according to the idea of global point pair distance normalization, giving a threshold value mu, and rejecting the weight values wkPoints less than or equal to mu are recorded as point cloud set
wkCan be expressed as
Step 85) establishing a new point pair according to the distance nearest relationship;
step 86) solving rigid body transformation method according to singular value decomposition, namely point cloud P′mTo R of point cloud Qm、TmSolving the matrix, wherein m is the current iteration number;
step 87) calculating the transformed point cloud pl ′m+1＝Rm×pl ′m+Tm；
Step 88) determining the current error
Step 89) Judgment em+1If yes, return to step 84), if not, or m +1 > η is satisfied, then the iteration is ended.
Further, in step 4), R is preferredOAAt 50%, half of the repeated area exists in the two adjacent groups of data blocks, and the integrity of the data is optimal.
Further, the model of the line laser sensor is LJ-V7000 of Kenzhen.
Further, the connected regions in step 1) are preferably four blocks.
Further, the computer system of the distance weight optimization ICP stitching algorithm is configured as follows: w89n84ows10, memories 168782, 83PU 897-.
The invention has the beneficial effects that:
the laser point cloud data of the invention has very high information integrity and overlapping rate ROAAre positively correlated; the efficiency of generating three-dimensional point cloud data is high, the spatial distribution of the laser array point cloud is uniform, and the processing is convenient; the distance weight optimization ICP (inductively coupled plasma) splicing algorithm effectively reduces the scale of point cloud registration, and greatly reduces the time for three-dimensional point cloud splicing; the laser measurement has strong stability in industrial environment, and the model accurately restores the space torsion characteristics of the fitting.
The foregoing description is only an overview of the technical solutions of the present invention, and in order to make the technical solutions of the present invention more clearly understood and to implement them in accordance with the contents of the description, the following detailed description is given with reference to the preferred embodiments of the present invention and the accompanying drawings. The detailed description of the present invention is given in detail by the following examples and the accompanying drawings.
Drawings
FIG. 1 is a schematic view of a laser overlap scanning process for curved surfaces of a leaf basin according to the present invention;
FIG. 2 shows different overlap ratios ROACollecting a leaf basin curved surface point cloud picture;
FIG. 3 shows the overlap ratio ROAObtaining 50% collected leaf basin curved surface point cloud pictures;
FIG. 4 shows the overlap ratio ROAThe front edge curved surface point cloud picture is acquired in 50 percent;
FIG. 5 shows the overlapping ratio ROAA 50% collected leaf back curved surface point cloud picture;
FIG. 6 shows the overlapping ratio ROAObtaining a 50% collected point cloud picture of the rear edge curved surface;
FIG. 7 is a processing flow chart of the distance weight optimization ICP splicing algorithm of the invention;
FIG. 8 is a comparison graph of the distance weight optimized ICP stitching algorithm of the present invention and the conventional ICP algorithm;
FIG. 9 is a digital model of an aircraft engine blade three-dimensionally spliced using a distance weight optimization ICP splicing algorithm;
FIG. 10 is a perspective view of a spliced aircraft engine blade.
Detailed Description
The present invention will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.
Referring to fig. 1, a three-dimensional overlapped modeling method includes the following steps:
step 1) selecting an aircraft engine blade, and dividing the aircraft engine blade into four connected areas, namely a basin curved surface, a front edge curved surface, a back curved surface and a rear edge curved surface;
step 2) the line laser sensor adopts LJ-V7000 of Kenzhi, the light path of the line laser sensor is designed according to the Schlemm's law and the direct laser triangulation method, and the line laser sensor is fixed on the multi-axis motion system after being calibrated;
step 3), placing the blade on a single-shaft rotating platform, wherein the axis of the blade is superposed with the zero position surface of the linear laser sensor;
and 4) controlling a line laser sensor by a motion system to acquire height information of the curved surface of the leaf basin by a bow-shaped path, wherein each measurement range is A multiplied by B, each translation distance is D, and the overlapping rate of each sampling range
The specific scanning step is as follows:
step 41) a laser measuring head of the line laser sensor sweeps horizontally along the positive direction of the Y axis to finish sampling within the range of planar size A multiplied by B, such as the first step in FIG. 1;
step 42) translating the laser measuring head by a distance D along the positive direction of the X axis, adjusting the position, and reserving a line laser overlapping area, such as the second step in the figure 1;
step 43) the laser measuring head performs horizontal scanning sampling along the negative direction of the Y axis, records the sampling data with the size of A multiplied by B, and has a reverse order relation with the step 41), as shown in the third step in FIG. 1;
step 44), moving the laser measuring head by a distance D along the positive direction of the X axis to ensure that the range of the overlapped area is consistent in the scanning process, such as the fourth step in the figure 1;
repeating the steps 41) to 44) to finish the measurement of the curved surface range of the whole leaf basin;
step 5) establishing a XYH three-dimensional coordinate system of laser point cloud data by a vertical plane (reference surface S), wherein the point cloud coordinate of each area is represented by (x)ij,yj,hij) Representing, wherein i and j respectively represent the serial number of the point cloud block and the frame serial number in the current point cloud block;
referring to FIG. 2, the overlap ratio R is selectedOA＝0、R OA20% and ROAIn 40% of the three cases, the results of the laser overlap scan are compared, where the three-dimensional view corresponds to the two-dimensional view, and it can be seen that when R is usedOAWhen the point cloud blocks are equal to 0%, no overlapping area exists between the point cloud blocks, and the edge information is obviously lost; when R isOAWhen the point cloud block is 20%, partially lost data of the point cloud block is repaired by an overlapping region; when R isOAAnd when the point cloud is equal to 40%, a complete leaf basin curved surface point cloud model can be obtained through data filling of the overlapped area.
Step 6), splicing single-side point clouds of the accessories through the overlapping features of the overlapped point cloud data areas;
step 7) rotating the rotary platform to sequentially complete the point cloud data acquisition of the front edge curved surface, the leaf back curved surface and the rear edge curved surface, as shown in the figure 3-6;
step 8) carrying out three-dimensional splicing on the point clouds according to the distance weight optimization ICP splicing algorithm, wherein the distance weight optimization ICP splicing algorithm comprises the following technical steps:
referring to fig. 7, step 81) sets the source point cloud set to P ═ Pi∈R3,i＝1,2,…,NpThe set of clouds of target points is Q ═ Qj∈R3,j＝1,2,…,Nq}, the elements in the set are three-dimensional coordinates of pointsVector, then ICP based on the optimized target model of the Euclidean distance residual error is
Where R and T are rotation and translation matrices in rigid body transformation, qkAnd pkA group of matching point pairs;
step 82) setting target precision and maximum iteration number eta;
step 83) establishing an initial corresponding relationship, traversing the point cloud P according to dk＝min{||qk-pk||2Searching for p in a point cloud QkCorresponding point q of (2)kForming a point pair;
step 84) distributing weight values for each group of point pairs according to the idea of global point pair distance normalization, giving a threshold value mu, and rejecting the weight values wkPoints less than or equal to mu are recorded as point cloud set
wkCan be expressed as
Step 85) establishing a new point pair according to the distance nearest relationship;
step 86) solving rigid body transformation method according to singular value decomposition, namely point cloud P′mTo R of point cloud Qm、TmSolving the matrix, wherein m is the current iteration number;
step 87) calculating the transformed point cloud pl ′m+1＝Rm×pl ′m+Tm；
Step 88) determining the current error
Step 89) judgment of em+1If yes, returning to step 84), if not, or if m +1 > eta is satisfied, ending the iteration;
referring to fig. 9-10, step 9) performs simplification and triangular meshing processing on the point cloud model of the accessory to generate a three-dimensional digital model of the accessory.
The embodiment selects the overlapping rate as R OA50%, the computer system of the distance weight optimization ICP splicing algorithm is configured as follows: w89n84ows10, memories 168782, 83PU 897-:
step 811) reading the laser point cloud data of the leaf basin curved surface, the front edge curved surface, the leaf back curved surface and the back edge curved surface by a program, sequentially traversing to generate point coordinates xyz, selecting the point cloud of the leaf basin curved surface as a reference, and calculating an initial position matrix R according to the rotating position relation during scanning other leaf surfaces0And T0；
Step 821) determining an ICP algorithm optimization objective function and setting the iteration precision to be 5 mu m and the maximum iteration time eta to be 50 times;
step 831) initializing a point cloud relation pair through matrix calculation;
step 841) traversing and calculating Euclidean distances between all point cloud pairs to give weights, and simplifying the cloud scale of source points by a set threshold value of 5 mm;
step 851) calculating a new point cloud relation pair;
step 861) solving a new position matrix RmAnd Tm；
Step 871) calculating a new point cloud pl ′m+1＝Rm×pl ′m+Tm；
Step 881) to find the current error
Step 891) judgment of em+1If yes, go back to step 841), if not, or if m +1 > η, then the iteration is over.
Referring to fig. 8, the e value of the conventional ICP algorithm reaches 0.004794mm after 10 iterations, while the distance weight optimization ICP stitching algorithm reaches 0.00494mm after 7 iterations, and on the premise that the target error is 5 μm, the optimized ICP algorithm reduces the iteration number by about 30% and has higher registration efficiency.
The above is only a preferred embodiment of the invention and is not intended to limit the invention, and it will be apparent to those skilled in the art that various modifications and variations can be made in the invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (6)
1. A three-dimensional overlapped modeling method is characterized by comprising the following steps:
step 1) dividing a plurality of connected regions clockwise according to the surface shape characteristics of aerospace accessories;
step 2), designing a light path of the line laser sensor according to the Schlemm's law and a direct laser triangulation method, and fixing the light path on a multi-axis motion system after calibration;
step 3), the accessory is placed on a single-shaft rotating platform, and the axis of the accessory is superposed with the zero position surface of the linear laser sensor;
step 4) the motion system controls the line laser sensor to collect height information of the accessory in a bow-shaped path, wherein each measurement range is A multiplied by B, each translation distance is D, and the overlapping rate of each sampling range
Step 5) generating laser array point cloud data (x) measured each time according to the moving speed and distance of the moving system and the line laser sensor parametersij,yj,hij) Wherein i and j respectively represent the serial number of the point cloud block and the frame serial number in the current point cloud block;
step 6) splicing single-side point clouds of the accessories through the overlapping features of the overlapped point cloud data areas;
step 7) rotating the rotating platform to sequentially complete point cloud data acquisition of the remaining curved surfaces;
step 8) optimizing an ICP (inductively coupled plasma) splicing algorithm according to the distance weight to perform three-dimensional splicing of point clouds;
and 9) simplifying and triangulating the point cloud model of the accessory to generate a three-dimensional digital model of the accessory.
2. The three-dimensional overlapped modeling method of claim 1, wherein: the technical steps of the distance weight optimization ICP splicing algorithm are as follows:
step 81) setting the source point cloud set as P ═ Pi∈R3,i＝1,2,…,NpThe set of clouds of target points is Q ═ Qj∈R3,j＝1,2,…,NqAnd f, taking elements in the set as three-dimensional coordinate vectors of points, and taking an optimal target model of ICP (inductively coupled plasma) based on Euclidean distance residual errors as
Where R and T are rotation and translation matrices in rigid body transformation, qkAnd pkA group of matching point pairs;
step 82) setting target precision and maximum iteration number eta;
step 83) establishing an initial corresponding relationship, traversing the point cloud P according to dk＝min{||qk-pk||2Searching for p in a point cloud QkCorresponding point q of (2)kForming a point pair;
step 84) distributing weight values for each group of point pairs according to the idea of global point pair distance normalization, giving a threshold value mu, and rejecting the weight values wkPoints less than or equal to mu are recorded as point cloud set
wkCan be expressed as
Step 85) establishing a new point pair according to the distance nearest relationship;
step 86) solving method for rigid body transformation according to singular value decomposition, and carrying out point cloud P'mTo R of point cloud Qm、TmSolving the matrix, wherein m is the current iteration number;
step 87) calculating the transformed point cloud pl′m+1＝Rm×pl′m+Tm；
Step 88) determining the current error
Step 89) judgment of em+1If yes, return to step 84), if not, or m +1 > η is satisfied, then the iteration is ended.
3. The three-dimensional overlapped modeling method of claim 2, wherein: in step 4), R is preferredOAAt 50%, half of the repeated area exists in the two adjacent groups of data blocks, and the integrity of the data is optimal.
4. The three-dimensional overlapped modeling method of claim 3, wherein: the model of the line laser sensor is LJ-V7000 of Kenzhi.
5. The three-dimensional overlapped modeling method of claim 4, wherein: the contiguous areas in step 1) are preferably four.
6. The three-dimensional overlapped modeling method of claim 5, wherein: the computer system configuration of the distance weight optimization ICP splicing algorithm is as follows: w89n84ows10, memories 168782, 83PU 897-.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112880588A (en) * | 2021-01-29 | 2021-06-01 | 广州信邦智能装备股份有限公司 | Blade three-dimensional data acquisition method based on combination of segmented scanning and splicing fusion |
| CN113962945A (en) * | 2021-10-09 | 2022-01-21 | 厦门大学 | Low-repeatability line laser point cloud data splicing method |
| CN116222579A (en) * | 2023-05-05 | 2023-06-06 | 西安麦莎科技有限公司 | Unmanned aerial vehicle inspection method and system based on building construction |
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2020
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112880588A (en) * | 2021-01-29 | 2021-06-01 | 广州信邦智能装备股份有限公司 | Blade three-dimensional data acquisition method based on combination of segmented scanning and splicing fusion |
| CN113962945A (en) * | 2021-10-09 | 2022-01-21 | 厦门大学 | Low-repeatability line laser point cloud data splicing method |
| CN113962945B (en) * | 2021-10-09 | 2024-06-07 | 厦门大学 | Low-repeatability line laser point cloud data splicing method |
| CN116222579A (en) * | 2023-05-05 | 2023-06-06 | 西安麦莎科技有限公司 | Unmanned aerial vehicle inspection method and system based on building construction |
| CN116222579B (en) * | 2023-05-05 | 2023-07-18 | 西安麦莎科技有限公司 | Unmanned aerial vehicle inspection method and system based on building construction |
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Application publication date: 20201127 |