CN113781305A - Point cloud fusion method of double-monocular three-dimensional imaging system - Google Patents
Point cloud fusion method of double-monocular three-dimensional imaging system Download PDFInfo
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Abstract
The invention discloses a point cloud fusion method of a double-monocular three-dimensional imaging system, which comprises a structured light projection module and image acquisition modules distributed on two sides of the structured light projection module, wherein the point cloud fusion method of the monocular systems on the left side and the right side comprises a calibration stage and a reconstruction stage: determining the one-to-one corresponding relation of two monocular point clouds by utilizing the coding information in a calibration stage; in the reconstruction stage, the SVD is used for decomposing blocks by blocks to determine rotational translation transformation, and after the point cloud splicing operation is completed through the rotational translation transformation, the point clouds in the overlapped area are subjected to average fusion processing to obtain a group of point clouds with good integrity. The method can realize the splicing of the left and right monocular point clouds through one-time data acquisition, saves the complicated operation of multiple acquisition, does not need to calculate the point cloud characteristics or the 2D characteristics to determine the matching relation of the two groups of point clouds, can save a large amount of calculation time, and obviously improves the integrity of the three-dimensional imaging data.
Description
Technical Field
The invention belongs to the technical field of three-dimensional imaging, and particularly relates to a point cloud fusion method of a double-monocular three-dimensional imaging system.
Background
The three-dimensional imaging technology based on the structured light method has the advantages of high measurement precision, non-contact, short imaging time and the like, and is widely applied to the fields of industrial detection, reverse engineering, virtual reality, dental diagnosis and treatment, cultural relic protection and the like. Generally, the three-dimensional imaging system can be divided into a monocular structured light system and a binocular structured light system according to the number of image acquisition modules in the three-dimensional imaging system. The binocular structured light system determines the pixel matching relation of the two cameras according to the coding information of the structured light, and obtains a disparity map, and point cloud loss is easily caused when shielding and near-end overexposure conditions exist; and the double-monocular three-dimensional imaging system, namely the left and right image acquisition modules and the structured light projection module form a monocular system respectively, and a union set of two groups of monocular point clouds can be obtained through one-time shooting, so that the integrity of the point clouds is effectively improved.
Aiming at the problem of point cloud loss, the traditional method is that the same three-dimensional scanner changes the position, multiple times of shooting are carried out to obtain multiple groups of point clouds, and finally, the multiple groups of point clouds are spliced into a complete group of point clouds. The patent technology combines a point cloud ICP (inductively coupled plasma) splicing method/mark point splicing method and a texture matching (SIFT) splicing method together, can effectively make up for the defects of a single splicing method, but can cause the increase of algorithm complexity and the increase of calculation time.
The Shenzhen Lingyun video science and technology Limited liability company provides a point cloud splicing method in the invention patent application with the publication number of CN110120013A, the method combines 2D characteristics and 3D point cloud characteristics to determine matching point pairs, nearest point iterative calculation is not needed, the complex process in point cloud registration is avoided, but the method is not suitable for workpieces with a large number of planes in an industrial scene, and especially the unique 2D and 3D characteristics are few.
Disclosure of Invention
In view of the above, the invention provides a point cloud fusion method for a dual-monocular three-dimensional imaging system, which does not need additional operation, and can accurately realize the point cloud splicing operation of a left monocular system and a right monocular system only by one structured light projection and image process, thereby significantly improving the data integrity of three-dimensional imaging.
A point cloud fusion method of a double-monocular three-dimensional imaging system is disclosed, wherein the imaging system is composed of a structured light projection module and image acquisition modules distributed on the left side and the right side of the structured light projection module, the left image acquisition module and the structured light projection module form a left monocular system, the right image acquisition module and the structured light projection module form a right monocular system, and the method is used for point cloud fusion of the left monocular system and the right monocular system and specifically comprises the following steps:
(1) the structured light projection template for calibration is designed and consists of stripe patterns which are vertical to each other in the horizontal and vertical directions;
(2) projecting the structured light projection template to a white board by using a structured light projection module, synchronously acquiring template images by using image acquisition modules on two sides, and determining the one-to-one correspondence relationship between image pixels acquired by a left monocular system and a right monocular system by using the uniqueness of codes;
(3) according to the triangulation principle, three-dimensional point cloud data of an image acquired by a left monocular system, namely left point cloud, is reconstructed, and three-dimensional point cloud data of an image acquired by a right monocular system, namely right point cloud, is reconstructed;
(4) determining a transformation matrix from a right monocular system coordinate system to a left monocular system coordinate system block by utilizing the one-to-one correspondence between the pixels of the images acquired by the left monocular system and the right monocular system, and transforming the right point cloud into the left monocular system coordinate system;
(5) and (4) merging the overlapped parts of the right point cloud and the left point cloud which are transformed into the coordinate system of the left monocular system into a block of point cloud through average operation.
Preferably, the fringe patterns in two directions in the structured light projection template are generated by encoding through a gray code and a four-step phase shift method.
Further, in the step (2), a one-to-one correspondence relationship between the pixels of the image collected by the left monocular system and the pixels of the image collected by the right monocular system is determined, and the specific process is as follows: for any pixel point in the acquired image, calculating a phase value of the point through decoding; for the phase value of any pixel point in the image collected by the left monocular system, the pixel point corresponding to the same phase in the image collected by the right monocular system is searched, and the one-to-one corresponding relation between the pixels of the image collected by the left monocular system and the pixels of the image collected by the right monocular system can be determined.
Further, the specific implementation process of the step (3) is as follows: firstly, obtaining internal parameters and external parameters of a left image acquisition module and internal parameters and external parameters of a structured light projection module through system calibration, calculating a projection matrix between the left image acquisition module and the structured light projection module based on the information, and then calculating three-dimensional point cloud data of any pixel point in an image acquired by a left monocular system according to the phase value of the pixel point and the projection matrix; and similarly, the three-dimensional point cloud data of each pixel point in the image acquired by the right monocular system can also be calculated.
Further, the specific implementation process of the step (4) is as follows:
4.1 dividing the right point cloud into a plurality of blocks according to the size of NxN, wherein each block of point cloud consists of three-dimensional point cloud data of all points in the block, and N is a self-set positive integer;
4.2 for the ith Right Point cloud Block
According to the one-to-one correspondence relationship between the pixels of the images collected by the left monocular system and the right monocular system, the matched left point cloud block can be determined
i and j are [0, NxN-1 ]]A natural number within the range;
4.3 calculating the centroids of the right point clouds
And the center of mass of the left point cloud
4.4 calculation matrix
Performing singular value decomposition on H to obtain H ═ U Σ VTWherein U andv is a unitary matrix obtained by decomposition, sigma is a singular value diagonal matrix,Trepresenting a transpose;
4.5 calculate the rotation matrix from the right monocular system coordinate system to the left monocular system coordinate system, R ═ VUTAnd a translation matrix
Further the right point cloud block
Transforming the coordinate system into a left monocular system coordinate system;
4.6 traversing all the right point cloud blocks in sequence according to the steps 4.2-4.5, and transforming the whole right point cloud into a left monocular system coordinate system.
Further, the specific implementation process of the step (5) is as follows: for any point p in the right point cloud transformed to the left monocular system coordinate systemrzl＝(Xrzl,Yrzl,Zrzl) Calculating the pixel coordinate (u) of the point in the coordinate system of the left monocular system by projective transformationrzl,vrzl) The specific calculation expression is as follows:
wherein: kleftIs an internal reference, X, of the left image acquisition modulerzl、Yrzl、ZrzlThe components of the three-dimensional point cloud data of the point in the direction of X, Y, Z;
then, the coordinates (u) in the left point cloud are locatedrzl,vrzl) Three-dimensional point cloud data of the upper corresponding point and (X)rzl,Yrzl,Zrzl) After averaging, the point cloud data is used as new point cloud data after the point fusion.
Based on the technical scheme, the invention has the following beneficial technical effects:
1. according to the invention, through one-side data acquisition, the point clouds of the left monocular system and the right monocular system can be spliced, and the data integrity is obviously improved compared with that of a binocular imaging system.
2. The method can obviously improve the distortion resistance of the three-dimensional imaging system, and the point cloud splicing precision is high when the point cloud error caused by lens distortion is large.
Drawings
Fig. 1 is a schematic structural diagram of a double-monocular three-dimensional imaging system.
FIG. 2 is a schematic flow chart of the point cloud fusion method of the double monocular system of the present invention.
Fig. 3 is a schematic diagram illustrating a principle of analyzing a phase value of a structured light projection module.
Fig. 4 is a schematic diagram showing comparison of imaging results of a conventional binocular structured light imaging system and the binocular monocular three-dimensional imaging system of the present invention.
Detailed Description
In order to more specifically describe the present invention, the following detailed description is provided for the technical solution of the present invention with reference to the accompanying drawings and the specific embodiments.
As shown in fig. 1, the dual-monocular system of the present invention is composed of two industrial cameras and one DLP projector, wherein the left camera and the DLP projector constitute a left monocular system, and the right camera and the DLP projector constitute a right monocular system. As shown in fig. 2, the point cloud fusion method for the left monocular system and the right monocular system of the three-dimensional imaging system includes the following steps:
(1) in the calibration stage, a template map projected by the DLP projector is designed, as shown in fig. 3, the template map is composed of horizontal and vertical stripe maps, and the encoding mode of the stripe map in each direction adopts gray code and a four-step phase shift method.
The left and right cameras shoot the deformed fringe image modulated by the scene, as shown in fig. 3, the corresponding decoding algorithm specifically comprises the following steps:
1.1 binarizing black and white gray code images by a threshold value method, then combining a pair of 01 sequences of each pixel point according to a projection sequence, and determining the value (k) of each pixel point in the horizontal and vertical directions according to the corresponding relation between the gray code and the binary systemu,kv)。
1.2 the horizontal and vertical phase values of the pixel points can be obtained according to the following formula
Wherein InIs the nth phase shift diagram.
1.3 the phase value at any point (u, v) can be expressed as:
by the method, the image sequences shot by the left camera and the right camera can be decoded into phase values in the horizontal direction and the vertical direction, namely (phi)leftu,φleftv) And (phi)rightu,φrightv)。
1.4 by means of traversal, for each point (u) of the right camera systemlm,vlm) Finding equal phase corresponding pixel coordinates (u) in the left camera systemrn,vrn) This allows a one-to-one correspondence between pixel coordinates in the left and right camera systems to be determined.
(2) In the reconstruction stage, the specific implementation process is as follows:
firstly, three-dimensional point cloud data p of each pixel coordinate of a left monocular system is calculated through a monocular structured light reconstruction modell＝(Xleft,Yleft,Zleft) (ii) a Similarly, three-dimensional point cloud data p of each pixel coordinate of the right monocular system is calculatedr＝(Xright,Yright,Zright) The method comprises the following specific steps:
2.1 internal reference K of left Camera obtained by System calibrationleftGinseng (radix Ginseng) RleftAnd TleftInternal reference K of DLP optical machineprojGinseng (radix Ginseng) RprojAnd TprojCalculating projection matrixes of the left camera and the DLP projector according to the following formulas:
2.2 left Camera any Pixel position (u)l,vl) Corresponding to an absolute phase of phileftuThen three-dimensional point cloud data p of the pointl＝(Xleft,Yleft,Zleft) The calculation formula of (a) is as follows:
2.3 similarly, the three-dimensional point cloud data p of each pixel coordinate can be calculatedr＝(Xright,Yright,Zright)。
Then, determining the transformation relation from the right point cloud to the left point cloud coordinate system block by block, and the specific steps are as follows:
2.4 dividing the right point cloud into several blocks according to N × N size, wherein the ith block of point cloud data can be expressed as
2.5 according to the pixel matching relation between the left camera and the right camera obtained in the calibration stage, determining a left point cloud pair matched with the ith right point cloud and expressing the left point cloud pair as
2.6 calculating the centroid of the cloud block of the right point
Corresponding left point cloud centroid
Order to
Performing singular value decomposition on H to obtain H ═ U ∑ VTThen, thenTransformation from right point cloud to rotation matrix R ═ VU for left camera systemTTranslation matrix
And transforming the ith block of right point cloud to a left camera system through matrix operation.
2.7 according to steps 2.5-2.6, all right point cloud blocks are transformed to the left camera system in sequence, denoted as pr2l＝(Xr2l,Yr2l,Zr2l)。
And finally, fusing the point cloud transformed to the left coordinate system and the point cloud obtained by the left monocular system, and specifically comprising the following steps:
2.8 transformation to the right Point cloud p of the left Camera Systemr2lThe pixel coordinate (u) of the point in the left camera system is calculated by projective transformationr2l,vr2l) The projective transformation is:
2.9 will also be located at (u)r2l,vr2l) Left point cloud data (X)left,Yleft,Zleft) And (X)r2l,Yr2l,Zr2l) And obtaining new point cloud data after fusion by averaging.
FIG. 4 shows the results obtained using conventional binocular structured light imaging and the inventive bi-monocular fusion imaging method, on the same hardware platform; in the traditional binocular structured light imaging system, as only the public part in the left and right image acquisition systems has three-dimensional point cloud data, large-area data loss (black shadow) occurs, and the fusion method can obtain more complete three-dimensional data, thereby greatly reducing the data loss area.
The embodiments described above are presented to enable a person having ordinary skill in the art to make and use the invention. It will be readily apparent to those skilled in the art that various modifications to the above-described embodiments may be made, and the generic principles defined herein may be applied to other embodiments without the use of the inventive faculty. Therefore, the present invention is not limited to the above embodiments, and those skilled in the art should make improvements and modifications to the present invention based on the disclosure of the present invention within the protection scope of the present invention.
Claims (6)
1. A point cloud fusion method of a double-monocular three-dimensional imaging system is disclosed, wherein the imaging system is composed of a structured light projection module and image acquisition modules distributed on the left side and the right side of the structured light projection module, the left image acquisition module and the structured light projection module form a left monocular system, the right image acquisition module and the structured light projection module form a right monocular system, and the method is used for point cloud fusion of the left monocular system and the right monocular system and specifically comprises the following steps:
(1) the structured light projection template for calibration is designed and consists of stripe patterns which are vertical to each other in the horizontal and vertical directions;
(2) projecting the structured light projection template to a white board by using a structured light projection module, synchronously acquiring template images by using image acquisition modules on two sides, and determining the one-to-one correspondence relationship between image pixels acquired by a left monocular system and a right monocular system by using the uniqueness of codes;
(3) according to the triangulation principle, three-dimensional point cloud data of an image acquired by a left monocular system, namely left point cloud, is reconstructed, and three-dimensional point cloud data of an image acquired by a right monocular system, namely right point cloud, is reconstructed;
(4) determining a transformation matrix from a right monocular system coordinate system to a left monocular system coordinate system block by utilizing the one-to-one correspondence between the pixels of the images acquired by the left monocular system and the right monocular system, and transforming the right point cloud into the left monocular system coordinate system;
(5) and (4) merging the overlapped parts of the right point cloud and the left point cloud which are transformed into the coordinate system of the left monocular system into a block of point cloud through average operation.
2. The point cloud fusion method of claim 1, wherein: and the fringe patterns in two directions in the structured light projection template are generated by encoding through a Gray code and a four-step phase shift method.
3. The point cloud fusion method of claim 1, wherein: in the step (2), a one-to-one correspondence relationship between the pixels of the image collected by the left monocular system and the pixels of the image collected by the right monocular system is determined, and the specific process is as follows: for any pixel point in the acquired image, calculating a phase value of the point through decoding; for the phase value of any pixel point in the image collected by the left monocular system, the pixel point corresponding to the same phase in the image collected by the right monocular system is searched, and the one-to-one corresponding relation between the pixels of the image collected by the left monocular system and the pixels of the image collected by the right monocular system can be determined.
4. The point cloud fusion method of claim 1, wherein: firstly, obtaining internal parameters and external parameters of a left image acquisition module and internal parameters and external parameters of a structured light projection module through system calibration, calculating a projection matrix between the left image acquisition module and the structured light projection module based on the information, and then calculating three-dimensional point cloud data of any pixel point in an image acquired by a left monocular system according to the phase value of the pixel point and the projection matrix; and similarly, the three-dimensional point cloud data of each pixel point in the image acquired by the right monocular system can also be calculated.
5. The point cloud fusion method of claim 1, wherein: the specific implementation process of the step (4) is as follows:
4.1 dividing the right point cloud into a plurality of blocks according to the size of NxN, wherein each block of point cloud consists of three-dimensional point cloud data of all points in the block, and N is a self-set positive integer;
4.2 for the ith Right Point cloud Block
According to the one-to-one correspondence relationship between the pixels of the images collected by the left monocular system and the right monocular system, the matched left point cloud block can be determined
i and j are [0, NxN-1 ]]A natural number within the range;
4.3 calculating the centroids of the right point clouds
And the center of mass of the left point cloud
4.4 calculation matrix
Performing singular value decomposition on H to obtain H ═ U Σ VTWherein U and V are unitary matrixes obtained by decomposition, and Sigma is a singular value diagonal matrix,Trepresenting a transpose;
4.5 calculate the rotation matrix from the right monocular system coordinate system to the left monocular system coordinate system, R ═ VUTAnd a translation matrix
Further the right point cloud block
Transforming the coordinate system into a left monocular system coordinate system;
4.6 traversing all the right point cloud blocks in sequence according to the steps 4.2-4.5, and transforming the whole right point cloud into a left monocular system coordinate system.
6. The point cloud fusion method of claim 1, wherein: the specific implementation process of the step (5) is as follows: for any point p in the right point cloud transformed to the left monocular system coordinate systemrzl＝(Xrzl,Yrzl,Zrzl) Calculating the pixel coordinate (u) of the point in the coordinate system of the left monocular system by projective transformationrzl,vrzl) The specific calculation expression is as follows:
wherein: kleftIs an internal reference, X, of the left image acquisition modulerzl、Yrzl、ZrzlThe components of the three-dimensional point cloud data of the point in the direction of X, Y, Z;
then, the coordinates (u) in the left point cloud are locatedrzl,vrzl) Three-dimensional point cloud data of the upper corresponding point and (X)rzl,Yrzl,Zrzl) After averaging, the point cloud data is used as new point cloud data after the point fusion.
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110288699A (en) * | 2019-06-26 | 2019-09-27 | 电子科技大学 | A kind of three-dimensional rebuilding method based on structure light |
| CN110852979A (en) * | 2019-11-12 | 2020-02-28 | 广东省智能机器人研究院 | Point cloud registration and fusion method based on phase information matching |
| CN111536904A (en) * | 2020-05-27 | 2020-08-14 | 深圳市华汉伟业科技有限公司 | Three-dimensional measurement method and system based on structural illumination and storage medium |
| CN113012277A (en) * | 2021-02-03 | 2021-06-22 | 中国地质大学（武汉） | DLP (digital light processing) -surface-based structured light multi-camera reconstruction method |
-
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Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110288699A (en) * | 2019-06-26 | 2019-09-27 | 电子科技大学 | A kind of three-dimensional rebuilding method based on structure light |
| CN110852979A (en) * | 2019-11-12 | 2020-02-28 | 广东省智能机器人研究院 | Point cloud registration and fusion method based on phase information matching |
| CN111536904A (en) * | 2020-05-27 | 2020-08-14 | 深圳市华汉伟业科技有限公司 | Three-dimensional measurement method and system based on structural illumination and storage medium |
| CN113012277A (en) * | 2021-02-03 | 2021-06-22 | 中国地质大学（武汉） | DLP (digital light processing) -surface-based structured light multi-camera reconstruction method |
Non-Patent Citations (2)
| Title |
|---|
| 吴二星: "基于结构光视觉的手机外壳三维测量系统研究", 《中国优秀硕士学位论文全文数据库基础科学辑》 * |
| 王玉奥: "基于融合改进特征匹配和增强ICP的三维点云重建", 《中国优秀硕士学位论文全文数据库信息科技辑》 * |
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Application publication date: 20211210 |