CN113432550A - Large-size part three-dimensional measurement splicing method based on phase matching - Google Patents
Large-size part three-dimensional measurement splicing method based on phase matching Download PDFInfo
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- G01B11/25—Measuring arrangements characterised by the use of optical techniques for measuring contours or curvatures by projecting a pattern, e.g. one or more lines, moiré fringes on the object
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Abstract
A large-size part three-dimensional measurement splicing method based on phase matching is used for splicing three-dimensional data of a global measurement system and a local measurement system in large-size part three-dimensional measurement. The method is based on a grating phase principle, takes sinusoidal fringe phase information projected by a local measurement system as matching characteristics, finds image matching points of a global measurement system and the local measurement system, and then reconstructs three-dimensional corresponding points of a global measurement coordinate system and the local measurement coordinate system according to calibration parameters of the global measurement system and the local measurement system. And designing an optimization objective function, taking the image matching points, the three-dimensional corresponding points, the internal and external parameters of the global measurement system and the local measurement system and the transformation matrixes of the two measurement coordinate systems as input, and obtaining the transformation matrix between the global measurement coordinate system and the local measurement coordinate system through iterative optimization according to the principle of minimum reprojection error, so as to realize the three-dimensional data splicing of the global measurement system and the local measurement system. The invention solves the problem of three-dimensional data splicing of a global measurement system and a local measurement system in three-dimensional measurement of large-size parts, does not need to stick mark points or auxiliary positioning devices on a measured object, and meets the requirement of high-efficiency and high-precision splicing of rapid measurement of the large-size parts.
Description
Technical Field
The invention relates to a large-size part three-dimensional measurement splicing method based on phase matching, which can be used for splicing data between a global measurement system and a local measurement system in large-size part three-dimensional measurement. The invention belongs to the field of machine vision.
Background
The optical three-dimensional scanning device based on the grating phase method is widely applied to the acquisition of three-dimensional data on the surface of a part, namely, the three-dimensional reconstruction is carried out on the outer contour of the part by using methods such as stereoscopic vision, projection grating and the like. In the field of manufacturing of high-end equipment such as ships, aviation, automobiles and the like, a core structure is mostly large-size parts, three-dimensional shape data needs to be obtained through a measurement technology and fed back to a processing link for allowance analysis and process optimization, and the processing quality of the large-size parts is guaranteed. The large-size part has a complex structure and more shielded parts, and a global measurement system and a local measurement system are required to perform measurement simultaneously during measurement so as to obtain complete three-dimensional data. The rapid high-precision splicing of the three-dimensional data of the global measurement system and the local measurement system is the key for ensuring the measurement precision and efficiency. Therefore, in the three-dimensional measurement of large-size parts, a high-precision global measurement system and a local three-dimensional data splicing method which do not need marking points and auxiliary positioning devices are designed, and the method has important application value.
Disclosure of Invention
In order to solve the problem of splicing global measurement system and local three-dimensional data without marking points and auxiliary positioning devices, the invention designs a global measurement system and a local measurement system three-dimensional data splicing method based on phase matching search by utilizing phase information provided by sine stripes. And when the local measurement system is used for measuring, the horizontal and vertical sine stripes are projected to the surface of the measured part, and the global measurement system is used for shooting the sine stripes at the same time. And establishing a one-to-one corresponding relation between the camera pixel and the image surface pixel of the digital projector according to the phase values of the horizontal and vertical stripes as matching features. According to the principle of closest distance, searching image matching points between the global measurement system and the local measurement system on the image surface of the digital projector, and then removing error matching by using epipolar constraint to realize phase matching. And respectively carrying out three-dimensional reconstruction by combining internal and external parameters of the global measurement system and the local measurement system to obtain three-dimensional corresponding points between the two measurement coordinate systems, and calculating initial coordinate transformation values between the two coordinate systems. And finally, designing and optimizing an objective function according to a principle of minimum reprojection error, and obtaining a coordinate transformation matrix between the final global measurement system and the final local measurement system through iterative solution to complete three-dimensional data splicing of the two measurement systems.
The technical problem to be solved by the invention is as follows: the rapid high-precision splicing of the global measurement system and the local measurement system in the three-dimensional measurement of the large-size part is realized without pasting mark points on the measured object or using an auxiliary positioning device.
The technical solution of the invention is as follows: a large-size part three-dimensional measurement splicing method based on phase matching is characterized by comprising the following steps:
(1) and the horizontal and vertical sinusoidal stripes projected when the local measurement system performs measurement are simultaneously collected by the global measurement system.
(2) Through phase solution and phase expansion, each point on the images of the global measurement system and the local measurement system is uniquely identified by a phase characteristic value corresponding to a horizontal stripe and a vertical stripe, and a corresponding relation with a pixel on an image surface of the digital projector is established. And searching and obtaining image matching points between the global measurement system and the local measurement system according to a distance nearest principle.
(3) Epipolar constraints between two cameras of the global measurement system and epipolar constraints between two cameras of the local measurement system can remove false image matching points between the global measurement system and the local measurement system. The remaining image matching points can be combined with the internal and external parameters of the global measurement system and the local measurement system to reconstruct a three-dimensional corresponding point between the two measurement systems, and the initial value of a coordinate transformation matrix between the two measurement coordinate systems is calculated.
(4) And designing and optimizing an objective function according to a principle of minimum reprojection error, and iteratively solving a coordinate transformation matrix between the global measurement system and the local measurement system by taking the image matching points, the three-dimensional corresponding points, the internal and external parameters of the global measurement system and the local measurement system and the initial values of the transformation matrices of the two measurement coordinate systems as input.
Compared with the prior art, the invention has the advantages that:
(1) the invention uses the horizontal and vertical sine stripes projected during the measurement of the local measurement system to realize the three-dimensional data splicing of the global measurement system and the local measurement system, does not paste mark points on a measured object, does not use an additional auxiliary positioning device to position the local measurement system, and meets the requirement of rapid measurement.
(2) Polar constraint is used to eliminate error matching, and a transformation matrix between two measurement system coordinate systems is iteratively solved by using an optimized objective function designed according to the principle of minimum reprojection error, so that the splicing precision is improved.
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FIG. 1 is a flow chart of the method of the present invention.
Fig. 2 is a schematic diagram of the global measurement system and the local measurement system when they are spliced. FIG. 1 is a schematic view of a part to be measured. And 2, a local measurement system comprising 2 cameras and 1 digital projector. And 3, a global measurement system comprising 2 cameras and 1 digital projector.
FIG. 3 is a schematic diagram of the present invention showing four cameras of the global measurement system and the local measurement system shooting simultaneously. In the figure, 1 is a left camera image plane of a local measurement system, 2 is a right camera image plane of the local measurement system, 3 is a left camera image plane of a global measurement system, 4 is a right camera image plane of the global measurement system, and 5 is a schematic diagram of a part to be measured.
Fig. 4 is a schematic diagram of searching for a matching point on an image plane of a digital projector according to the present invention. In the figure, 1 is a left camera image plane of the local measurement system, 2 is a right camera image plane of the local measurement system, 3 is a left camera image plane of the global measurement system, 4 is a right camera image plane of the global measurement system, and 5 is a digital projector image plane.
Detailed Description
For a better understanding of the present invention, the technical solutions of the present invention will be described in detail below with reference to the accompanying drawings and examples.
Referring to the attached figure 1, a large-size part three-dimensional measurement splicing method based on phase matching comprises the following steps:
1. the global measurement system and the local measurement system are both based on the principle of a grating phase method and are composed of 2 cameras and 1 digital projector, the global measurement system is fixed relative to the measured object, and the local measurement system can move around the measured object. The local measurement system projects sine stripes to the surface of the measured part, and the global measurement system simultaneously shoots the sine stripes projected by the local measurement system.
2. And performing phase calculation on the fringe patterns shot by the four cameras of the global measurement system and the local measurement system. The phase solution adopts a four-step phase shift method, and the corresponding solution formula is
Wherein g isi(x, y) is a fringe image, Φ (x, y) is a phase principal value to be solved, and i is the number of phase shifts.
By projecting horizontal and vertical sinusoidal stripes with similar multi-stage periods, the stripes with one period covering the whole field of view are obtained according to the heterodyne principle, and the phase expansion can be carried out by utilizing the inverse relation between the phase value and the period of the stripes and one of the original projected stripes under the condition of the same zero phase. So far, each pixel on the camera can use the phase phi corresponding to the horizontal stripexPhase phi corresponding to (x, y) and vertical stripey(x, y) a unique identifier. The correspondence between the camera pixels and the digital projector pixels can be calculated by the following formula
Wherein xp，ypFor digital projector pixel coordinates, phix，ΦyThe phase principal value corresponding to the horizontal and vertical stripes of the camera pixel (x, y) is λ, which is the stripe period.
3. Referring to fig. 4, after the image points of the four cameras of the global measurement system and the local measurement system in step 2 establish a corresponding relationship with the image plane of the digital projector, image matching points of the four cameras are searched on the image plane of the digital projector. If the distance between the two points is smaller than a set threshold value, namely a closest point principle, on the digital projector, the two points are considered as matching points, and accordingly image matching points among the four cameras are found. Meanwhile, polar constraint is respectively satisfied between two cameras of the global measurement system and between cameras of the local measurement system, and mismatching points can be eliminated. And then combining the three-dimensional corresponding points at the positions of the internal parameter reconstruction and the external parameter reconstruction of the global measurement system and the local measurement system to calculate the initial value of the coordinate transformation matrix between the two measurement coordinate systems.
Where R, T are initial values of coordinate transformation matrix between two measurement coordinate systems, PGi、PLiIs a three-dimensional corresponding point between the global measurement system and the local measurement system.
4. According to the minimum principle of the reprojection error, using the internal and external parameters of the image matching point, the three-dimensional corresponding point, the global measurement system and the local measurement system and the initial values of the transformation matrixes of the two measurement coordinate systems in 3 as input, designing an optimization objective function, and iteratively solving the transformation matrix between the global measurement coordinate system and the local measurement coordinate system, wherein the optimization objective function is as follows:
in the formula, R and T are parameters to be solved and the initial value is the calculation result in 3,
intrinsic parameters, R, for global and local measurement system camerasG、TGAs an external parameter of the global measurement system, RL、TLFor local measurement of external parameters of the system, PLiThree-dimensional points, p, reconstructed for local measurement systems1i、p2i、p3i、p4iAnd matching points of the corresponding images between the global measurement system and the local measurement system. And performing iterative computation on R and T to transform the point cloud of the local measurement system, thereby realizing the three-dimensional point cloud data splicing of the global measurement system and the local measurement system.
Claims (5)
1. A large-size part three-dimensional measurement splicing method based on phase matching is characterized by comprising the following steps:
(1) the global measurement system and the local measurement system are both based on the principle of a grating phase method, the local measurement system projects sine stripes to the surface of the part to be measured, and the global measurement system simultaneously shoots the sine stripes projected by the local measurement system;
(2) respectively obtaining a phase characteristic value of each pixel of the images of the global measurement system and the local measurement system through a phase calculation and phase expansion algorithm, and searching corresponding image matching points between the global measurement system and the local measurement system according to the phase characteristic values;
(3) reconstructing three-dimensional corresponding points of two measurement coordinate systems by combining corresponding image matching points between the global measurement system and the local measurement system with calibration parameters of the global measurement system and the local measurement system, and calculating an initial value of a coordinate transformation matrix between the two measurement coordinate systems;
(4) and designing an optimized objective function according to a principle of minimum reprojection error, and obtaining a final transformation matrix between the two coordinate systems through iterative optimization, so that the point cloud of the local measurement system is spliced to the coordinate system of the global measurement system.
2. The three-dimensional measurement splicing method for the large-size parts based on the phase matching as claimed in claim 1, wherein the three-dimensional measurement splicing method comprises the following steps: in the step (1), the global measurement system is fixed and unchanged relative to the measured object, and the local measurement system can move around the measured object; and when the local measurement system is used for measuring, transverse and longitudinal sine stripes are projected to the surface of the measured object, and meanwhile, the global measurement system also shoots the sine stripes, namely, the global measurement system and the local measurement system shoot to obtain stripe images.
3. The three-dimensional measurement splicing method for the large-size parts based on the phase matching as claimed in claim 1, wherein the three-dimensional measurement splicing method comprises the following steps: in the step (2), the horizontal and vertical sinusoidal stripe images obtained by the global measurement system and the local measurement system are applied with a phase calculation and phase unwrapping algorithm, and each image point can be uniquely identified by a phase characteristic value corresponding to the horizontal and vertical stripe. The fringe image is projected by a digital projector, and each pixel on an image plane coordinate system of the digital projector has a unique phase characteristic value corresponding to the horizontal and vertical fringes. According to the principle of a grating phase method, pixel points of the projector image surface corresponding to the pixel points on the camera image surface are found through the phase characteristic values corresponding to the horizontal and vertical stripes. And each image point of the global measurement system and the local measurement system can find a corresponding pixel on the image surface of the projector, and image matching points between the global measurement system and the local measurement system are searched on the image surface of the projector according to the principle of closest distance.
4. The three-dimensional measurement splicing method for the large-size parts based on the phase matching as claimed in claim 1, wherein the three-dimensional measurement splicing method comprises the following steps: and (3) removing the error matching in the image matching points searched in the step (2) by using epipolar constraint, then respectively performing three-dimensional reconstruction by combining internal parameters and external parameters of the global measurement system and the local measurement system to obtain three-dimensional corresponding points between the two measurement coordinate systems, and calculating an initial value of a transformation matrix between the two measurement coordinate systems.
5. The three-dimensional measurement splicing method for the large-size parts based on the phase matching as claimed in claim 1, wherein the three-dimensional measurement splicing method comprises the following steps: and (4) designing an optimization objective function by taking the internal and external parameters of the image matching point, the three-dimensional corresponding point, the global measurement system and the local measurement system and the initial values of the transformation matrixes of the two measurement coordinate systems in the step (3) as input, and iteratively calculating the transformation matrix between the global measurement coordinate system and the local measurement coordinate system according to the minimum principle of reprojection errors.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114372916A (en) * | 2021-12-31 | 2022-04-19 | 易思维（杭州）科技有限公司 | Automatic point cloud splicing method |
| CN114372916B (en) * | 2021-12-31 | 2024-05-31 | 易思维(杭州)科技股份有限公司 | Automatic point cloud splicing method |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102445164A (en) * | 2011-10-12 | 2012-05-09 | 北京航空航天大学 | Three-dimensional shape vision measuring method and system for large component surface |
| CN102721375A (en) * | 2012-06-20 | 2012-10-10 | 北京航空航天大学 | Multi-reflection inhibiting method in in-situ measurement of high reflection metal structural component |
| CN102721376A (en) * | 2012-06-20 | 2012-10-10 | 北京航空航天大学 | Calibrating method of large-field three-dimensional visual sensor |
| CN105716542A (en) * | 2016-04-07 | 2016-06-29 | 大连理工大学 | Method for three-dimensional data registration based on flexible feature points |
| WO2017125507A1 (en) * | 2016-01-19 | 2017-07-27 | Zivid Labs As | Imaging unit and system for obtaining a three-dimensional image |
| CN109230580A (en) * | 2018-10-11 | 2019-01-18 | 西安中科光电精密工程有限公司 | It is a kind of based on the mixed unstacking robot system and method for putting material information acquisition |
| CN111242990A (en) * | 2020-01-06 | 2020-06-05 | 西南电子技术研究所（中国电子科技集团公司第十研究所） | 360-degree three-dimensional reconstruction optimization method based on continuous phase dense matching |
| CN111637850A (en) * | 2020-05-29 | 2020-09-08 | 南京航空航天大学 | Self-splicing surface point cloud measuring method without active visual marker |
| CN112504161A (en) * | 2020-11-26 | 2021-03-16 | 北京航空航天大学 | Engine blade combined type measuring system with measurement precision and efficiency taken into consideration |
-
2021
- 2021-06-22 CN CN202110692207.XA patent/CN113432550B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102445164A (en) * | 2011-10-12 | 2012-05-09 | 北京航空航天大学 | Three-dimensional shape vision measuring method and system for large component surface |
| CN102721375A (en) * | 2012-06-20 | 2012-10-10 | 北京航空航天大学 | Multi-reflection inhibiting method in in-situ measurement of high reflection metal structural component |
| CN102721376A (en) * | 2012-06-20 | 2012-10-10 | 北京航空航天大学 | Calibrating method of large-field three-dimensional visual sensor |
| WO2017125507A1 (en) * | 2016-01-19 | 2017-07-27 | Zivid Labs As | Imaging unit and system for obtaining a three-dimensional image |
| CN105716542A (en) * | 2016-04-07 | 2016-06-29 | 大连理工大学 | Method for three-dimensional data registration based on flexible feature points |
| CN109230580A (en) * | 2018-10-11 | 2019-01-18 | 西安中科光电精密工程有限公司 | It is a kind of based on the mixed unstacking robot system and method for putting material information acquisition |
| CN111242990A (en) * | 2020-01-06 | 2020-06-05 | 西南电子技术研究所（中国电子科技集团公司第十研究所） | 360-degree three-dimensional reconstruction optimization method based on continuous phase dense matching |
| CN111637850A (en) * | 2020-05-29 | 2020-09-08 | 南京航空航天大学 | Self-splicing surface point cloud measuring method without active visual marker |
| CN112504161A (en) * | 2020-11-26 | 2021-03-16 | 北京航空航天大学 | Engine blade combined type measuring system with measurement precision and efficiency taken into consideration |
Non-Patent Citations (5)
| Title |
|---|
| 伏燕军等: "基于单目结构光的大物体三维测量关键方法的研究", 《应用光学》 * |
| 刘建伟等: "大尺寸工业视觉测量系统", 《光学精密工程》 * |
| 杨凌辉等: "融合光电扫描定位的大构件整体形貌测量方法", 《红外与激光工程》 * |
| 王鹏等: "基于柔性靶标定位实现图像拼接的多相机三维测量系统", 《光学学报》 * |
| 邓文怡等: "一种基于视觉测量的大型自由曲面拼接方法", 《北京信息科技大学学报(自然科学版)》 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114372916A (en) * | 2021-12-31 | 2022-04-19 | 易思维（杭州）科技有限公司 | Automatic point cloud splicing method |
| CN114372916B (en) * | 2021-12-31 | 2024-05-31 | 易思维(杭州)科技股份有限公司 | Automatic point cloud splicing method |
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