CN114066983A - Intelligent supplementary scanning method based on two-axis rotary table and computer readable storage medium - Google Patents
Intelligent supplementary scanning method based on two-axis rotary table and computer readable storage medium Download PDFInfo
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Abstract
The invention discloses an intelligent supplementary scanning method based on a two-axis turntable and a computer readable storage medium, comprising S1, calibrating parameters of a binocular structured light three-dimensional scanner; s2, performing hand-eye calibration based on the two-axis turntable to obtain a calibration result matrix of the spatial relative position relationship between the scanner and the two-axis turntable base; s3, collecting point cloud data of a three-dimensional digital model of the target object by using a calibrated scanner to obtain a point cloud model; s4, searching a point cloud model missing area in a computer UI window, aligning a window view to the missing area, acquiring a point cloud model target pose displayed in the window in real time, and controlling the rotary table to move synchronously according to the calibration result matrix and the target pose; s5, collecting single point cloud under a target pose corresponding to the point cloud of the missing area, reconstructing the single point cloud, and registering the single point cloud with the point cloud data collected in S3 to complement the point cloud data of the missing area; and S6, repeating S4-S5 until all missing region point cloud data of the point cloud model are completed, and obtaining the complete three-dimensional digital model of the target object for deviation detection.
Description
Technical Field
The invention relates to the technical field of visual 3D measurement, in particular to an intelligent supplementary scanning method based on a two-axis rotary table and a related computer readable storage medium.
Background
The optical three-dimensional measurement technology is widely applied to multiple industrial production stages such as reverse design, detection, quality control and the like. In the production and manufacturing process of products, accurate detection is a key ring in product quality control, and complex molded surface product digital detection based on a three-dimensional CAD model carries out registration and deviation analysis by measuring molded surface model data of parts of processed products and an original design CAD model to obtain a detection result for quality control.
The surface measurement of the surface structure light free-form curved surface has the advantages of rapidness, full field, non-contact, high-density point cloud, field measurement, non-cooperative target and the like, so that the surface measurement method is widely applied to the fields of reverse engineering, grinding tool design, industrial detection, quality control, cultural relic protection, medical imaging, agricultural mapping, underwater detection and the like. With the rapid development of the manufacturing industry, the production period of products is shortened, and the quality detection scheme with high precision and high efficiency can effectively deal with the challenges in the aspects of product detection and quality control brought by the industrial revolution.
By combining robot equipment and turntable equipment, a plurality of highly integrated and automated visual 3D measurement schemes are derived, wherein the robot-based automatic measurement system is used for three-dimensional measurement of parts with larger sizes and industrial levels, and the two-axis turntable system is more competent for model repair and detection in the fields of desktop level and light industry.
At present, the existing two-axis turntable measuring system usually uses a viewpoint planning or manual teaching mode to measure. Namely, the rotation angle of two shafts is input to control the two-shaft turntable to move, and a binocular structured light three-dimensional scanner is used for measuring the point cloud of a target object. The measurement method has the problem of data loss, and the existing method cannot effectively solve the problem. At present, most methods are solved by point cloud sampling and packaging into grids and grid model hole filling in a data post-processing stage. However, the three-dimensional digital model obtained by the grid hole filling technology is not a model obtained by real processing, is a model obtained by algorithm calculation and optimization, and cannot well reflect the real appearance of the model, and particularly in narrow areas such as holes and cavities, the model obtained by post-processing is more different from the real model. Therefore, in the point cloud obtaining stage, more complete model data needs to be obtained, and the influence of post-processing on the model is reduced.
Disclosure of Invention
In view of the above, the invention provides an intelligent supplementary scanning method based on a two-axis turntable, and aims to solve the technical problem that the measurement deviation is large due to the fact that data loss exists in the conventional two-axis turntable measurement system and cannot be effectively compensated in a post-processing stage.
The invention provides the following technical scheme for solving the technical problems:
an intelligent supplementary scanning method based on a two-axis rotary table comprises the following steps: s1, calibrating parameters of the binocular structured light three-dimensional scanner; s2, performing hand-eye calibration based on the two-axis turntable to obtain a calibration result matrix of the spatial relative position relationship between the binocular structured light three-dimensional scanner and the two-axis turntable base; s3, scanning a target object by using the binocular structured light three-dimensional scanner calibrated in the step S1 to acquire three-dimensional digital model point cloud data of the target object to obtain a point cloud model; s4, searching a missing area of the point cloud model in a UI window on a computer, aligning a window view to the missing area, acquiring a point cloud model target pose displayed in the UI window in real time, and controlling the two-axis rotary table to synchronously move according to the calibration result matrix and the target pose, wherein the UI refers to a user interface; s5, acquiring a single point cloud under a target pose corresponding to the point cloud of the missing area, reconstructing the single point cloud, and registering the single point cloud with the point cloud data acquired in the step S3 to complement the point cloud data of the missing area; and S6, repeating the steps S4-S5 until point cloud data of all missing areas of the point cloud model are completed to obtain a complete three-dimensional digital model of the target object, wherein the complete three-dimensional digital model is used for deviation detection.
Further, step S1 includes: and calibrating internal and external parameters of a camera of the binocular structured light three-dimensional scanner.
Further, the calibrating the internal and external parameters of the camera of the binocular structured light three-dimensional scanner in step S1 includes: s11, shooting a calibration board from different angles and positions to obtain a calibration picture, wherein the calibration board is provided with coding mark points and non-coding mark points; s12, identifying the calibration picture to obtain the two-dimensional image coordinates of the coding mark points and the two-dimensional image coordinates of the non-coding mark points; respectively carrying out three-dimensional reconstruction on the coding mark points and the non-coding mark points to obtain three-dimensional space coordinates of the coding mark points and three-dimensional space coordinates of the non-coding mark points; and S13, based on the two-dimensional image coordinates and the three-dimensional space coordinates of the coding mark points and the two-dimensional image coordinates and the three-dimensional space coordinates of the non-coding mark points, carrying out integral beam adjustment iterative optimization on the internal and external parameters of the camera, taking the minimized reprojection error of the camera as a target loss function, and adding a scale parameter for constraint to obtain the internal and external parameters of the camera.
Further, in step S2, the binocular structured light three-dimensional scanner is used as an eye, and the two-axis turntable base is used as a hand to perform the hand-eye calibration; the coordinate system of the binocular structured light three-dimensional scanner is set at the optical center of the reference camera, the coordinate system of the two-axis turntable base is set at the two-axis turntable base, and the mathematical model used for calibration is an eye-in-hand model.
Further, the performing of the hand-eye calibration based on the two-axis turntable in step S2 includes: s21, adopting a standard calibration board configured with coding mark points and non-coding mark points to calibrate; in the calibration process, the spatial relative position relationship between the binocular structured light three-dimensional scanner and the two-axis turntable base is kept unchanged, and the spatial relative position relationship between the coordinate system of the table top of the two-axis turntable and the standard calibration plate is kept unchanged, so that the standard calibration plate is fixed on the table top of the two-axis turntable and moves along with the two-axis turntable in the calibration process;
s22, controlling the rotation of two shafts of the two-shaft rotary table to drive the standard calibration plate to different spatial positions and postures, and shooting the standard calibration plate by using the binocular structured light three-dimensional scanner in the process to obtain a plurality of calibration images at different positions and postures; s23, performing binocular matching on the calibration image, performing three-dimensional reconstruction, and calculating the relative external parameters of the three-dimensional coordinate points of the reconstruction mark points and the known world coordinate system global points of the standard calibration plate; s24, regarding the two-axis rotary table as a two-axis robot through a robot DH modeling method, and establishing a conversion relation from a base coordinate system of the two-axis rotary table to a table top coordinate system of the two-axis rotary table; calculating to obtain a conversion matrix from a base coordinate system of the two-axis rotary table to a table coordinate system of the two-axis rotary table at different positions and postures of the table top of the two-axis rotary table through a Rodrigues formula and the rotation angles of the two axes of the two-axis rotary table; and S25, inputting the relative external parameters obtained in the step S23 and the conversion matrix obtained in the step S24 as data in a hand-eye calibration process, and solving by using a hand-eye calibration algorithm to obtain a calibration result matrix from the optical center of the reference camera to the two-axis turntable base coordinate system.
Further, in the process of performing the hand-eye calibration in step S2, for the calibration image captured by the binocular structured light three-dimensional scanner, the following hand-eye relationship equation is satisfied:
AX＝XB
the hand-eye relation equation shows that different A matrixes and B matrixes exist for each calibration pose X, the A matrixes represent external parameter matrixes obtained by calibrating the camera, the B matrixes represent conversion matrixes from a base coordinate system to a table-board coordinate system of the two-axis rotary table at each position, and the B matrixes are obtained through DH modeling of the two-axis rotary table.
Further, obtaining the B matrix through DH modeling of the two-axis turntable includes: setting respective coordinate systems for connecting rods of the two-axis rotary table, determining parameters of a kinematic model of the two-axis rotary table through movement of the coordinate systems, and converting the parameters of the kinematic model into a conversion matrix B from a base coordinate system of the two-axis rotary table to a table top coordinate system of the two-axis rotary table according to space three-dimensional rigid body conversion.
Further, step S3 specifically includes: s31, placing the target object in the center of the table top of the two-axis rotary table and fixing the target object, and ensuring that the relative position relationship between the target object and the two-axis rotary table is unchanged in the scanning process; s32, controlling the two-axis rotary table to move to different positions and postures, and scanning the target object by using the binocular structured light three-dimensional scanner at each position and posture to obtain multiple groups of point cloud data; and S33, splicing the multiple groups of point cloud data in a non-mark point splicing mode to obtain the three-dimensional digital model point cloud data of the target object.
Further, step S4 specifically includes: s41, setting a reference pose of the scanning point cloud under the two-axis turntable base coordinate system, and recording a reference conversion matrix from the table top coordinate system of the two-axis turntable to the two-axis turntable base coordinate system; s42, regarding the computer screen as a virtual camera, moving a mouse to rotate a view of a point cloud model in a UI window to search a missing area of the point cloud model, aligning the window view to the missing area, and recording an initial coordinate and a termination coordinate of the movement of the mouse in a two-dimensional plane; s43, converting the two-dimensional coordinates of the mouse into three-dimensional coordinates in a three-dimensional space by utilizing an OpenGL view matrix transformation relation; s44, according to the definition of the three-dimensional sphere, the three-dimensional coordinates of the initial coordinates and the ending coordinates are distributed on the spherical surface of the three-dimensional sphere; respectively constructing a first vector connecting the sphere center of the three-dimensional sphere and the initial coordinate and a second vector connecting the sphere center of the three-dimensional sphere and the termination coordinate, and obtaining a rotating shaft and a rotating angle through the first vector and the second vector operation; s45, taking the rotating shaft and the rotating angle obtained in the step S44 as input, and calculating by utilizing a Rodrigues formula to obtain a target pose estimation matrix of the missing region; s46, converting the target pose estimation matrix obtained by calculation in the step S45 into rotation angles of two axes of the two-axis rotary table by using a DH model of the two-axis rotary table; and S47, controlling the two-axis rotary table to move according to the rotation angle calculated in the step S46, wherein the pose of the target object in the optical center coordinate system of the reference camera of the binocular structured light three-dimensional scanner is consistent with the pose of the point cloud model view in the UI window, and synchronous movement of the two-axis rotary table and the point cloud model is completed.
Further, step S5 specifically includes: s51, acquiring a single point cloud of the target object in the target pose by using the binocular structured light three-dimensional scanner; and S52, calculating a transformation relation between the single point cloud and the point cloud model according to the target pose estimation matrix calculated in the step S45 and the reference transformation matrix recorded in the step S41, so as to transform the compensated and scanned single point cloud from the target pose under the optical center coordinate system of the reference camera to the reference pose under the optical center coordinate system of the reference camera, and obtaining the point cloud data after the compensation and scanning of the missing area.
Compared with the prior art, the technical scheme of the invention has the beneficial effects that: by using the binocular structured light three-dimensional scanner to scan the target object and introducing the two-axis rotary table, the pose of the binocular structured light three-dimensional scanner does not need to be adjusted manually, and the acquisition of model point clouds under different visual angles can be realized only by controlling the rotation of the two-axis rotary table through software at the PC end. On the basis, the view of the point cloud model is adjusted through a software UI window, the visual angle of a camera scanning target object is simulated, the missing area of the point cloud model can be visually checked, the two-axis rotary table is automatically controlled to move to the position corresponding to the pose when the missing area is aligned with the viewing angle, a binocular structured light three-dimensional scanner is used for collecting data, missing point cloud data is repaired in the point cloud acquisition stage, intelligent compensation scanning is achieved, interactivity, usability and real-time performance are improved, the estimation precision of the measured target pose is high, the integrity of the model can be improved in the point cloud data acquisition stage, and a higher-quality model is provided for point cloud data post-processing. In addition, a binocular structured light high-precision three-dimensional scanner can be adopted, and the device has the advantages of high imaging precision (the single scanning precision can reach 0.01mm), high point cloud acquisition speed (the single scanning speed is less than 1s), high acquisition quality, strong ambient light interference resistance and the like when a target object is scanned, and has high adaptability to black and reflective objects.
In a further technical scheme, calibration parameters of the binocular structured light three-dimensional scanner and the two-axis turntable are accurately obtained by using a binocular camera system calibration method based on photogrammetry, and the problems of non-convergence and poor robustness in the traditional calibration calculation process are solved; meanwhile, the calibration process is greatly simplified, and the reprojection error of the calibration method is within 0.05 pixel, so that the system measurement precision is met.
The present invention further provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, can implement the steps of the foregoing intelligent compensation scanning method.
Drawings
Fig. 1 is a schematic diagram of an intelligent supplementary scanning method based on a two-axis turntable in an embodiment of the present invention.
Fig. 2 is a schematic flow chart illustrating calibrating internal and external parameters of a camera of a binocular structured light three-dimensional scanner according to an embodiment of the present invention.
Fig. 3 is a schematic diagram of a method for calibrating internal and external parameters of a camera of a binocular structured light three-dimensional scanner according to an embodiment of the present invention.
Fig. 4 is a schematic diagram of a standard calibration board configured with coding mark points and non-coding mark points and a Scale for reconstruction according to an embodiment of the present invention.
Fig. 5 is a schematic diagram of a hand-eye calibration method based on a two-axis turntable in the embodiment of the invention.
Fig. 6 is a schematic diagram of a method for acquiring point cloud data of a three-dimensional digital model of a target object to be scanned in the embodiment of the present invention.
Fig. 7 is a scanning effect diagram of a pump body model point cloud including a missing region obtained by preliminary scanning in the embodiment of the present invention.
Fig. 8 is a schematic diagram of a method for acquiring a point cloud model target pose displayed in a UI window in real time and controlling synchronous motion of a two-axis turntable by a computer in the embodiment of the present invention.
Fig. 9 is a schematic diagram of an OpenGL view display matrix transformation process in the embodiment of the present invention.
Fig. 10 is a schematic diagram of a method for converting two-dimensional coordinates into three-dimensional space according to an embodiment of the present invention.
Fig. 11 is a schematic diagram of a method for collecting and completing point cloud data of a missing area according to an embodiment of the present invention.
Fig. 12 is a scanning effect diagram of the pump body model point cloud for complementing the missing area through intelligent complementing after the sweeping in the embodiment of the present invention.
Detailed Description
In order to make the technical problems, technical solutions and advantageous effects to be solved by the embodiments of the present invention more clearly apparent, the present invention is further described in detail below with reference to the accompanying drawings and the 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.
Referring to fig. 1, an embodiment of the present invention provides an intelligent supplementary scanning method based on a two-axis turntable, including the following steps S1 to S6:
s1, calibrating parameters of the binocular structured light three-dimensional scanner; the parameter calibration refers to calibrating internal and external parameters of a camera of the binocular structured light three-dimensional scanner;
s2, performing hand-eye calibration based on the two-axis turntable to obtain a calibration result matrix of the spatial relative position relationship between the binocular structured light three-dimensional scanner and the two-axis turntable base;
s3, scanning a target object by using the binocular structured light three-dimensional scanner calibrated in the step S1 to acquire three-dimensional digital model point cloud data of the target object to obtain a point cloud model;
s4, searching a missing area of the point cloud model in a UI window on a computer, aligning a window view to the missing area, acquiring a point cloud model target pose displayed in the UI window in real time, and controlling the two-axis rotary table to move synchronously according to the calibration result matrix and the target pose; wherein, the UI refers to a User Interface (User Interface);
s5, acquiring a single point cloud under a target pose corresponding to the point cloud of the missing area, reconstructing the single point cloud, and registering the single point cloud with the point cloud data acquired in the step S3 to complement the point cloud data of the missing area;
and S6, repeating the steps S4-S5 until point cloud data of all missing areas of the point cloud model are completed to obtain a complete three-dimensional digital model of the target object, wherein the complete three-dimensional digital model is used for deviation detection.
In one embodiment of the present invention, the three-dimensional digital model is a pump body CAD model, and CAD (computer aideddesign) is a CAD design. In one embodiment of the invention, by using the binocular structured light high-precision three-dimensional scanner, the imaging precision is high, the point cloud acquisition speed is high, the acquisition quality is high, the ambient light interference resistance is strong, the adaptability to black and reflective objects is high, the single-width scanning precision can reach 0.01mm, and the single-width scanning speed is less than 1 s.
Specifically, internal and external parameter calibration of a camera of the binocular structured light three-dimensional scanner refers to a process of determining internal and external orientation parameters of the camera, and the accuracy of the camera parameters directly influences the point cloud reconstruction accuracy of the three-dimensional scanner. In one embodiment of the invention, a standard calibration plate configured with coding mark points and non-coding mark points is used for calibration, and the calibration plate is shot from different angles and positions to obtain a certain number of calibration pictures; then identifying the u-v coordinates of the mark point image in the calibration picture, and performing three-dimensional reconstruction on the mark point based on the photogrammetry principle to obtain the accurate three-dimensional space coordinates of the mark point; and then, based on the u-v coordinates and the three-dimensional space coordinates of the two-dimensional images of the mark points, carrying out integral beam adjustment iterative optimization on all camera parameters in the pinhole imaging camera model to minimize the re-projection error of the camera as a target loss function, and adding scale parameters (images and real dimension scaling parameters) to carry out constraint so as to finally obtain accurate internal and external parameters of the three-dimensional scanner camera.
The calibration method does not need a high-precision calibration plate, and can accurately calculate the inside and outside orientation parameters of the camera by only taking the accurate distance between any pair of mark points on the calibration plate as a scale, thereby realizing the calibration of the camera.
As shown in fig. 2, a schematic flow chart of calibrating the internal and external parameters of the camera of the binocular structured light three-dimensional scanner in the embodiment of the present invention includes flows a 01-a 05: a01, collecting a calibration picture; a02, identifying mark points; a03, reconstructing three-dimensional coordinates of the mark points; a04, iterative optimization of beam adjustment; a05, adding proportion parameters. Fig. 3 is a schematic diagram of a method for calibrating internal and external parameters of a camera of a binocular structured light three-dimensional scanner according to an embodiment of the present invention. Referring to fig. 2 and 3, calibrating the internal and external parameters of the camera of the binocular structured light three-dimensional scanner includes the following steps S11 to S13:
s11, shooting the calibration board from different angles and positions to obtain calibration pictures; wherein the calibration plate is a standard calibration plate configured with coded marker points and non-coded marker points.
S12, identifying the calibration picture to obtain the two-dimensional image coordinates of the coding mark points and the two-dimensional image coordinates of the non-coding mark points; and respectively carrying out three-dimensional reconstruction on the coding mark points and the non-coding mark points to obtain the three-dimensional space coordinates of the coding mark points and the three-dimensional space coordinates of the non-coding mark points. Specifically, the coded mark points are three-dimensionally reconstructed to obtain accurate three-dimensional space coordinates, and then the non-coded mark points are reconstructed to obtain accurate three-dimensional space coordinates.
And S13, carrying out integral light beam adjustment and iterative optimization on the internal and external parameters of the camera based on the two-dimensional image coordinates and the three-dimensional space coordinates of the coding mark points and the two-dimensional image coordinates and the three-dimensional space coordinates of the non-coding mark points, taking the minimized reprojection error of the camera as a target loss function, and adding a scale parameter to carry out constraint to obtain the internal and external parameters of the camera. And when the optimization is carried out, parameters such as the coordinates, the external parameter matrix, the internal parameter matrix, distortion and the like are taken into consideration together, binding optimization is carried out, the reprojection error is minimized, and the optimal internal and external parameters of the camera are obtained after iteration.
Fig. 4 is a schematic diagram of a standard calibration board configured with coding mark points and non-coding mark points and a Scale for three-dimensional reconstruction according to an embodiment of the present invention.
The hand-eye calibration based on the two-axis rotary table is used for obtaining the spatial relative position relation between the binocular structured light three-dimensional scanner and the rotary table base, and is essentially the calibration of the conversion relation between two coordinate systems, and the precision of the calibration directly influences the target pose acquisition precision of intelligent supplementary scanning and the supplementary scanning point cloud splicing precision. The binocular structured light three-dimensional scanner is used as an Eye (Eye), and the coordinate system of the binocular structured light three-dimensional scanner is set at the optical center of the reference camera; the turntable base is used as a Hand (Hand), the coordinate system of the turntable base is set at the turntable base, and the mathematical model of the turntable base is an eye-outside-Hand model. It can be understood that in the intelligent compensation scanning, the computer screen is regarded as a virtual camera, the scanning target pose for acquiring the point cloud of the missing area is under the optical center coordinate system of the left camera, the target pose needs to be converted into the two-axis turntable base coordinate system through hand-eye calibration, and then the transformation matrix describing the target pose is converted into the angles of the two axes of the two-axis turntable through the two-axis turntable DH model, so that the model is driven to the position consistent with the visual angle of the computer screen.
As shown in fig. 5, the hand-eye calibration of the two-axis turntable includes the following steps S21 to S25:
s21, adopting a standard calibration board configured with coding mark points and non-coding mark points to calibrate; in the calibration process, the spatial relative position relationship between the binocular structured light three-dimensional scanner and the two-axis turntable base is kept unchanged, and the spatial relative position relationship between the coordinate system of the table top of the two-axis turntable and the standard calibration plate is kept unchanged, so that the standard calibration plate is fixed on the table top of the two-axis turntable and moves along with the two-axis turntable in the calibration process.
And S22, controlling the rotation of the two shafts of the two-shaft rotary table to drive the standard calibration plate to different spatial positions and postures, shooting the standard calibration plate by using the binocular structured light three-dimensional scanner in the process, and repeating the process to obtain a plurality of calibration images at different positions and postures. In one embodiment, 10-15 calibration images are acquired.
And S23, carrying out binocular matching on the calibration images, then carrying out three-dimensional reconstruction, and calculating the relative external parameters of the three-dimensional coordinate points of the reconstruction mark points and the known world coordinate system global points of the standard calibration plate.
S24: by a robot DH modeling method, regarding the two-axis rotary table as a two-axis robot, and establishing a conversion relation from a base coordinate system of the two-axis rotary table to a table-board coordinate system of the two-axis rotary table; and calculating to obtain a conversion matrix from the two-axis turntable base coordinate system to the two-axis turntable table surface coordinate system under different positions and postures of the two-axis turntable table surface through a Rodrigues formula and the rotation angles of the two axes of the two-axis turntable. The DH modeling method is a robot modeling method proposed by Denavit and Hartenberg and is mainly used in robot kinematics.
And S25, inputting the relative external parameters obtained in the step S23 and the conversion matrix obtained in the step S24 as data in a hand-eye calibration process, and solving by using a hand-eye calibration algorithm to obtain a calibration result matrix from the optical center of the reference camera to the two-axis turntable base coordinate system.
Specifically, since the calibration plate is fixed on the two-axis turntable table during the hand-eye calibration process, the transformation matrix between the calibration plate and the two-axis turntable table is also kept unchanged when the two-axis turntable rotates. For a binocular structured light three-dimensional scanner to capture a calibration image, the following equations can be listed:
AX＝XB (1)
the equation is a hand-eye relation equation and shows that different A matrixes and B matrixes exist for each calibration pose X, the A matrixes represent external parameter matrixes obtained by camera calibration, the B matrixes represent conversion matrixes from a base coordinate system to a table coordinate system of the two-axis turntable at each position, and the B matrixes are obtained through DH modeling of the two-axis turntable. The left side and the right side of the equation respectively represent that no matter where the two-axis turntable moves, the transformation matrix from the table top of the two-axis turntable to the calibration plate is unchanged.
Specifically, respective coordinate systems are set for connecting rods of the two-axis rotary table, parameters of a rotary table kinematic model are determined through movement of the coordinate systems, and the parameters of the kinematic model are converted into a conversion matrix from a two-axis rotary table base coordinate system to a two-axis rotary table top coordinate system according to space three-dimensional rigid body conversion.
The method comprises the following specific steps:
1. establishing a coordinate system of each part of the two-axis rotary table, wherein the establishing steps of the coordinate system are as follows:
1.1, establishing ziShaft: the coordinate system of the connecting rod i is established at the joint i, ziThe axial direction is along the axial direction of the joint i.
1.2, establish xiShaft: along ziAxis and zi+1Direction of axis common to perpendicular, pointing zi+1A shaft.
1.3, establish yiShaft: by xiAxis and ziAxis, determined with right hand rule.
2. Four parameters (theta) of the DH model can be determined according to the length of the rotary table connecting rod and each joint anglei，di，ai，αi)：
2.1, winding xiShaft rotation torsion angle alphai-1Let z after movementiAxis and zi+1The axes coincide.
2.2, along xiLength of shaft moving rod ai-1The origin of the coordinate system of the link i and the origin of the coordinate system of the link i +1 are set to zi+1Collinear in the axial direction.
2.3, along ziDistance d of shaft movementiLet x beiAxis and xi+1The axes lie in the same plane.
2.4 winding ziIncluded angle theta of shaft rotationiLet x beiAxis and xi+1The axes coincide.
According to the steps, the structural parameters of the two-axis rotary table are combined, and the corresponding DH parameters can be obtained.
3. And (3) carrying the obtained DH parameters in according to a connecting rod coordinate system transformation matrix shown in the following formula (2), and obtaining a transformation matrix from a turntable base coordinate system to a turntable table-board coordinate system through the matrix multiplication of the formula (3), wherein the matrix is a matrix B required by hand-eye calibration.
In the embodiment of the invention, the calibration processes of the step S1 and the step S2 do not need to manufacture calibration reference objects with higher cost, only a standard calibration plate with mark points is used, a certain number of calibration images of the calibration plate are collected from different angles and positions, and the rescission error is iteratively minimized through the steps of identifying the mark points, reconstructing three-dimensional coordinates of the mark points, integrally optimizing light beam adjustment, adding proportion parameters and camera internal and external parameters and the like, so that accurate binocular structured light cameras and hand-eye calibration relative position parameters are obtained, and the point cloud collection quality and the splicing precision of the complementary scanning point cloud are ensured.
After the calibration of step S1 and step S2 is completed, as shown in fig. 6, the point cloud data of the three-dimensional digital model of the target object (pump body part) is acquired. The step of acquiring the three-dimensional digital model data of the pump body part comprises the following steps of S31-S33:
s31, placing the pump body part in the center of the table top of the two-axis turntable and fixing the pump body part, so as to ensure that the relative position relationship between the pump body part and the turntable is unchanged in the scanning process;
s32, controlling the two-axis rotary table to move to different positions and postures, and scanning the pump body part by using the binocular structured light three-dimensional scanner at each position and posture to obtain multiple groups of point cloud data;
and S33, splicing the multiple groups of point cloud data in a non-mark point splicing mode to obtain three-dimensional digital model point cloud data, or point cloud models, of the pump body parts.
Fig. 7 shows scanned three-dimensional digital model point cloud data of the pump body part. The pump body point cloud model is incomplete because of the problems that the pump body model has more complex characteristics, less planning positions and matching fails due to common shielding of binocular structured light scanning, and cannot be reconstructed, and the problem can be effectively solved through the intelligent supplementary scanning method.
Fig. 8 is a schematic diagram of a method for acquiring a point cloud model target pose displayed in a UI window in real time and controlling synchronous motion of a two-axis turntable by a computer in the embodiment of the present invention. As shown in fig. 8, searching a point cloud model missing region in a UI window on a computer, aligning a window view to the missing region, acquiring a point cloud model target pose displayed in the UI window in real time, and controlling the two-axis turntable to synchronously move according to the calibration result matrix and the target pose, including the following steps S41-S47:
s41, setting a reference pose of the scanning point cloud under the base coordinate system of the two-axis turntable, and recording a reference conversion matrix from the table top coordinate system of the two-axis turntable to the base coordinate system;
s42, regarding the computer screen as a virtual camera, moving a mouse to rotate a view of a point cloud model in a UI window to search a point cloud model missing area, aligning the window view to the missing area (namely, adjusting the pose of the point cloud model in a software UI window on the computer through mouse operation, aligning the missing area to the visual angle observed by human eyes, which is equivalent to that the model is driven by the rotation of a turntable to move to the position shot by a corresponding camera), and recording the initial coordinate and the final coordinate of the movement of the mouse in a two-dimensional plane;
s43, converting the two-dimensional coordinates of the mouse into three-dimensional coordinates in a three-dimensional space by utilizing an OpenGL view matrix transformation relation;
specifically, in order to convert one coordinate system (three-dimensional space) to another coordinate system (two-dimensional screen), several conversion matrices, respectively, a model matrix, a view matrix, and a projection matrix, are required. And converting the description of a certain vertex in the three-dimensional world in the model point cloud data into a computer screen, and displaying the model point cloud data after rendering. As shown in fig. 9, the conversion process is: first, the vertex coordinates begin in local space, called local coordinates, then go through world coordinates, look at the coordinates, crop the coordinates and finally end with screen coordinates. The local coordinates are coordinates of the object relative to the local origin, and are located at the world origin after being converted into world coordinates. The coordinate observed by the angle of the camera or the observer is the observation coordinate, the observation coordinate is changed into a cutting coordinate through perspective projection, and the cutting coordinate is changed into a coordinate on the final two-dimensional screen through viewport transformation.
By utilizing the OpenGL view transformation relationship, the transformation of the two-dimensional coordinates of the mouse into three-dimensional coordinates in a three-dimensional space is essentially the inverse transformation of the OpenGL for displaying a model of a three-dimensional world on a computer. In the intelligent supplementary scanning, a reference coordinate system for describing the model point cloud is a camera coordinate system, the camera coordinate system is a world coordinate system scanned by the two-axis rotary table, namely the initial position of the coordinate system of the model point cloud in the real world is in an observation space, and a three-dimensional transformation matrix of the real world is a matrix in the observation space, so that the world coordinate and the local coordinate do not need to be inversely transformed. Therefore, as shown in fig. 10, the specific steps of converting the mouse from the two-dimensional space to the three-dimensional space are as follows:
(1) OpenGL firstly converts points on each screen through a viewport in a screen space, and maps the screen coordinates back to 2D standardized equipment coordinates through internal parameters;
(2) then, performing perspective division inverse operation on the 2D standardized equipment coordinates, namely multiplying each coordinate component by a homogeneous component, and mapping the screen coordinates back to a cutting space;
(3) and OpenGL maps the clipping coordinates in the clipping space back to the observation space through a projection matrix, and the coordinates in the observation space are the coordinates in the three-dimensional space.
S44, according to the definition of the three-dimensional sphere, the three-dimensional coordinates of the initial coordinate and the ending coordinate are distributed on the spherical surface; respectively constructing a vector connecting the sphere center of the three-dimensional sphere and the initial coordinate and a vector connecting the sphere center of the three-dimensional sphere and the final coordinate, and obtaining a rotating shaft and a rotating angle through vector operation; the method comprises the following specific steps:
(1) the vector connecting the center of the three-dimensional sphere and the initial coordinate is recorded as a vector
The vector connecting the center of the three-dimensional sphere and the termination coordinate is recorded as a vector
(2) Rotating shaft
The rotation angle θ can be calculated by the following equations (4) and (5), respectively:
and S45, taking the rotating shaft and the rotating angle obtained in the step S44 as input, and calculating by using a Rodrigues formula to obtain a target pose estimation matrix R of the missing region, wherein the target pose estimation matrix R comprises the following steps:
R＝cosθI+(1-cosθ)nnT+sinθnΛ
where I denotes the identity matrix, n denotes the unit length vector of the axis of rotation, the symbol Λ denotes the transformation of the vector into the anti-symmetric matrix, nΛAn antisymmetric matrix representing a vector n;
s46, converting the target pose estimation matrix obtained by calculation in the step S45 into rotation angles of two axes of the two-axis rotary table by using a DH model of the two-axis rotary table;
and S47, controlling the two-axis rotary table to move according to the rotation angle calculated in the step S46 through software on a computer, wherein the pose of the target object under the optical center coordinate system of the reference camera of the binocular structured light three-dimensional scanner is consistent with the pose of the point cloud model view in the UI window, and synchronous movement of the two-axis rotary table and the point cloud model is completed.
Fig. 11 is a schematic diagram of a method for collecting and completing point cloud data of a missing area according to an embodiment of the present invention. As shown in fig. 11, after the turntable synchronously moves to the target pose consistent with the view of the computer UI window, a single point cloud under the target pose is collected, and the scanning result of the point cloud missing at the position is completed by reconstructing the single point cloud and registering the single point cloud with the previously scanned point cloud model, which includes the following steps:
s51, acquiring a single point cloud of a target object in the target pose by using the binocular structured light three-dimensional scanner;
and S52, calculating a transformation relation between the single point cloud and the point cloud model according to the target pose estimation matrix calculated in the step S45 and the reference transformation matrix recorded in the step S41, so as to transform the compensated and scanned single point cloud from the target pose under the optical center coordinate system of the reference camera to the reference pose under the optical center coordinate system of the reference camera, and obtain the point cloud data after the compensation and scanning of the missing area.
And finally, repeating the steps S4-S5 until point cloud data of all missing areas are supplemented, and obtaining complete three-dimensional digital model point cloud data of the pump body part, wherein the complete three-dimensional digital model is used for deviation detection. The pump body point cloud model obtained after the intelligent compensation scanning is finished is shown in fig. 12.
The intelligent supplementary scanning method based on the two-axis rotary table provided by the embodiment of the invention has the advantages of good interactivity, usability and instantaneity, the estimation precision of the pose of the measurement target is high, the integrity of the model can be improved in the point cloud data acquisition stage, and a higher-quality model is provided for point cloud data post-processing.
Another embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, can implement the steps of the foregoing embodiment intelligent supplementary scanning method based on a two-axis turntable. A computer readable storage medium may include, among other things, a propagated data signal with readable program code embodied therein, either in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable storage medium may transmit, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied in a computer readable storage medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
The foregoing is a more detailed description of the invention in connection with specific preferred embodiments and it is not intended that the invention be limited to these specific details. For those skilled in the art to which the invention pertains, several equivalent substitutions or obvious modifications can be made without departing from the spirit of the invention, and all the properties or uses are considered to be within the scope of the invention.
Claims (11)
1. An intelligent supplementary scanning method based on a two-axis rotary table is characterized by comprising the following steps:
s1, calibrating parameters of the binocular structured light three-dimensional scanner;
s2, performing hand-eye calibration based on the two-axis turntable to obtain a calibration result matrix of the spatial relative position relationship between the binocular structured light three-dimensional scanner and the two-axis turntable base;
s3, scanning a target object by using the binocular structured light three-dimensional scanner calibrated in the step S1 to acquire three-dimensional digital model point cloud data of the target object to obtain a point cloud model;
s4, searching a missing area of the point cloud model in a UI window on a computer, aligning a window view to the missing area, acquiring a point cloud model target pose displayed in the UI window in real time, and controlling the two-axis rotary table to move synchronously according to the calibration result matrix and the target pose; wherein, the UI refers to a user interface;
s5, acquiring a single point cloud under a target pose corresponding to the point cloud of the missing area, reconstructing the single point cloud, and registering the single point cloud with the point cloud data acquired in the step S3 to complement the point cloud data of the missing area;
and S6, repeating the steps S4-S5 until point cloud data of all missing areas of the point cloud model are completed to obtain a complete three-dimensional digital model of the target object, wherein the complete three-dimensional digital model is used for deviation detection.
2. The intelligent supplementary scanning method based on the two-axis turntable as claimed in claim 1, wherein the step S1 includes: and calibrating internal and external parameters of a camera of the binocular structured light three-dimensional scanner.
3. The intelligent supplementary scanning method based on the two-axis turntable as claimed in claim 2, wherein the calibrating the internal and external parameters of the camera of the binocular structured light three-dimensional scanner in step S1 includes:
s11, shooting a calibration board from different angles and positions to obtain a calibration picture, wherein the calibration board is provided with coding mark points and non-coding mark points;
s12, identifying the calibration picture to obtain the two-dimensional image coordinates of the coding mark points and the two-dimensional image coordinates of the non-coding mark points; respectively carrying out three-dimensional reconstruction on the coding mark points and the non-coding mark points to obtain three-dimensional space coordinates of the coding mark points and three-dimensional space coordinates of the non-coding mark points;
and S13, based on the two-dimensional image coordinates and the three-dimensional space coordinates of the coding mark points and the two-dimensional image coordinates and the three-dimensional space coordinates of the non-coding mark points, carrying out integral beam adjustment iterative optimization on the internal and external parameters of the camera, taking the minimized reprojection error of the camera as a target loss function, and adding a scale parameter for constraint to obtain the internal and external parameters of the camera.
4. The intelligent supplementary scanning method based on the two-axis turntable as claimed in claim 1, wherein in step S2, the binocular structured light three-dimensional scanner is used as an eye, and the two-axis turntable base is used as a hand to perform the hand-eye calibration; the coordinate system of the binocular structured light three-dimensional scanner is set at the optical center of the reference camera, the coordinate system of the two-axis turntable base is set at the two-axis turntable base, and the mathematical model used for calibration is an eye-in-hand model.
5. The intelligent supplementary sweeping method based on the two-axis turntable as claimed in claim 4, wherein the performing of the hand-eye calibration based on the two-axis turntable in step S2 includes:
s21, adopting a standard calibration board configured with coding mark points and non-coding mark points to calibrate; in the calibration process, the spatial relative position relationship between the binocular structured light three-dimensional scanner and the two-axis turntable base is kept unchanged, and the spatial relative position relationship between the coordinate system of the table top of the two-axis turntable and the standard calibration plate is kept unchanged, so that the standard calibration plate is fixed on the table top of the two-axis turntable and moves along with the two-axis turntable in the calibration process;
s22, controlling the rotation of two shafts of the two-shaft rotary table to drive the standard calibration plate to different spatial positions and postures, and shooting the standard calibration plate by using the binocular structured light three-dimensional scanner in the process to obtain a plurality of calibration images at different positions and postures;
s23, performing binocular matching on the calibration image, performing three-dimensional reconstruction, and calculating the relative external parameters of the three-dimensional coordinate points of the reconstruction mark points and the known world coordinate system global points of the standard calibration plate;
s24, regarding the two-axis rotary table as a two-axis robot through a robot DH modeling method, and establishing a conversion relation from a base coordinate system of the two-axis rotary table to a table top coordinate system of the two-axis rotary table; calculating to obtain a conversion matrix from a base coordinate system of the two-axis rotary table to a table coordinate system of the two-axis rotary table at different positions and postures of the table top of the two-axis rotary table through a Rodrigues formula and the rotation angles of the two axes of the two-axis rotary table;
and S25, inputting the relative external parameters obtained in the step S23 and the conversion matrix obtained in the step S24 as data in a hand-eye calibration process, and solving by using a hand-eye calibration algorithm to obtain a calibration result matrix from the optical center of the reference camera to the two-axis turntable base coordinate system.
6. The intelligent supplementary scanning method based on the two-axis turntable as claimed in claim 5, wherein in the process of calibrating the hands and eyes in step S2, the following hand-eye relation equation is satisfied for the calibration images taken by the binocular structured light three-dimensional scanner:
AX＝XB
the hand-eye relation equation shows that different A matrixes and B matrixes exist for each calibration pose X, the A matrixes represent external parameter matrixes obtained by calibrating the camera, the B matrixes represent conversion matrixes from a base coordinate system to a table-board coordinate system of the two-axis rotary table at each position, and the B matrixes are obtained through DH modeling of the two-axis rotary table.
7. The intelligent complement scan method based on a two-axis turntable of claim 6, wherein obtaining the B matrix by DH modeling of the two-axis turntable comprises:
setting respective coordinate systems for connecting rods of the two-axis rotary table, determining parameters of a kinematic model of the two-axis rotary table through movement of the coordinate systems, and converting the parameters of the kinematic model into a conversion matrix B from a base coordinate system of the two-axis rotary table to a table top coordinate system of the two-axis rotary table according to space three-dimensional rigid body conversion.
8. The intelligent supplementary scanning method based on the two-axis turntable as claimed in claim 1, wherein the step S3 specifically includes:
s31, placing the target object in the center of the table top of the two-axis rotary table and fixing the target object, and ensuring that the relative position relationship between the target object and the two-axis rotary table is unchanged in the scanning process;
s32, controlling the two-axis rotary table to move to different positions and postures, and scanning the target object by using the binocular structured light three-dimensional scanner at each position and posture to obtain multiple groups of point cloud data;
and S33, splicing the multiple groups of point cloud data in a non-mark point splicing mode to obtain the three-dimensional digital model point cloud data of the target object.
9. The intelligent supplementary scanning method based on the two-axis turntable as claimed in claim 1, wherein the step S4 specifically includes:
s41, setting a reference pose of the scanning point cloud under the two-axis turntable base coordinate system, and recording a reference conversion matrix from the table top coordinate system of the two-axis turntable to the two-axis turntable base coordinate system;
s42, regarding the computer screen as a virtual camera, moving a mouse to rotate a view of a point cloud model in a UI window to search a missing area of the point cloud model, aligning the window view to the missing area, and recording an initial coordinate and a termination coordinate of the movement of the mouse in a two-dimensional plane;
s43, converting the two-dimensional coordinates of the mouse into three-dimensional coordinates in a three-dimensional space by utilizing an OpenGL view matrix transformation relation;
s44, according to the definition of the three-dimensional sphere, the three-dimensional coordinates of the initial coordinates and the ending coordinates are distributed on the spherical surface of the three-dimensional sphere; respectively constructing a first vector connecting the sphere center of the three-dimensional sphere and the initial coordinate and a second vector connecting the sphere center of the three-dimensional sphere and the termination coordinate, and obtaining a rotating shaft and a rotating angle through the first vector and the second vector operation;
s45, taking the rotating shaft and the rotating angle obtained in the step S44 as input, and calculating by utilizing a Rodrigues formula to obtain a target pose estimation matrix of the missing region;
s46, converting the target pose estimation matrix obtained by calculation in the step S45 into rotation angles of two axes of the two-axis rotary table by using a DH model of the two-axis rotary table;
and S47, controlling the two-axis rotary table to move according to the rotation angle calculated in the step S46, wherein the pose of the target object in the optical center coordinate system of the reference camera of the binocular structured light three-dimensional scanner is consistent with the pose of the point cloud model view in the UI window, and synchronous movement of the two-axis rotary table and the point cloud model is completed.
10. The intelligent supplementary scanning method based on the two-axis turntable as claimed in claim 9, wherein the step S5 specifically includes:
s51, acquiring a single point cloud of the target object in the target pose by using the binocular structured light three-dimensional scanner;
and S52, calculating a transformation relation between the single point cloud and the point cloud model according to the target pose estimation matrix calculated in the step S45 and the reference transformation matrix recorded in the step S41, so as to transform the compensated and scanned single point cloud from the target pose under the optical center coordinate system of the reference camera to the reference pose under the optical center coordinate system of the reference camera, and obtaining the point cloud data after the compensation and scanning of the missing area.
11. A computer-readable storage medium having stored thereon a computer program, characterized in that: the computer program may, when being executed by a processor, realize the steps of the intelligent replying method as claimed in any one of claims 1 to 10.
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| CN114170284A (en) * | 2022-02-09 | 2022-03-11 | 南京理工大学 | Multi-view point cloud registration method based on active landmark point projection assistance |
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