CN108981604B - Line laser-based three-dimensional full-view measurement method for precision part - Google Patents

Line laser-based three-dimensional full-view measurement method for precision part Download PDF

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CN108981604B
CN108981604B CN201810767127.4A CN201810767127A CN108981604B CN 108981604 B CN108981604 B CN 108981604B CN 201810767127 A CN201810767127 A CN 201810767127A CN 108981604 B CN108981604 B CN 108981604B
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point cloud
point
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profile
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CN108981604A (en
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宋丽梅
孙思远
郭庆华
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Qingyan Zhongdian (Tianjin) Intelligent Equipment Co.,Ltd.
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Tianjin Polytechnic University
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
    • G01B11/00—Measuring arrangements characterised by the use of optical techniques
    • G01B11/24—Measuring arrangements characterised by the use of optical techniques for measuring contours or curvatures
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
    • G01B11/00—Measuring arrangements characterised by the use of optical techniques
    • G01B11/002—Measuring arrangements characterised by the use of optical techniques for measuring two or more coordinates
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
    • G01B11/00—Measuring arrangements characterised by the use of optical techniques
    • G01B11/02—Measuring arrangements characterised by the use of optical techniques for measuring length, width or thickness
    • G01B11/03—Measuring arrangements characterised by the use of optical techniques for measuring length, width or thickness by measuring coordinates of points

Abstract

The invention belongs to the field of three-dimensional machine vision, and relates to a three-dimensional full-view measuring method of a precision part based on line laser. The method is based on a laser triangulation method, changes of reflected laser stripes are collected by a high-speed camera to reflect height changes of the surface of an object, and therefore the cross section profile of the object at the moment is collected. And then, combining translation scanning and rotation scanning modes, converting two-dimensional profile data under different viewing angles into point cloud data under the same coordinate system according to the calculated rotation translation matrix between different viewing angles, and finally performing curved surface reconstruction on the point cloud to obtain real three-dimensional full-appearance data of the measured object. Compared with other non-contact measuring methods, the three-dimensional overall measuring method designed by the invention has the advantages that the measuring mode is more flexible, the method is suitable for diversified working conditions, the measuring precision is higher, the point cloud effect is better, and the processing efficiency of a computer can be effectively improved.

Description

Line laser-based three-dimensional full-view measurement method for precision part
Technical Field
The invention relates to a three-dimensional overall measurement method of a precision part based on laser scanning, in particular to a three-dimensional overall measurement method capable of converting laser profile information into actual three-dimensional point cloud.
Background
With the gradual maturity of photoelectric sensor devices and computer technologies, the optical three-dimensional measurement method is widely applied to a plurality of fields such as industrial detection, reverse engineering, medical scanning, cultural relic protection, military science and technology, and has the advantages of fast forming and high precision in the detection of free-form surfaces. Optical three-dimensional measurement techniques can be classified into laser triangulation, time-of-flight, binocular vision, and spectral confocal, depending on the imaging mode. For precise part details and complex working condition environments, a laser triangulation method is often adopted for measurement. Because each sampling data of the line laser is the cross section outline of the object, the line laser is generally synchronized with a motion device in the three-dimensional reconstruction process, then a world coordinate system is determined, the outline data is converted into point cloud data, and finally the point clouds are registered according to the calculated rotation translation matrix and are fused into a complete three-dimensional full-view model of the object. Due to the surface material characteristics of the measured object, the profile data has peaks and dead angles, so that the point cloud processing process becomes complicated, and the three-dimensional reconstruction effect is directly influenced. In order to solve the problem of three-dimensional overall measurement of the precision part, the invention designs a three-dimensional overall measurement method for the precision part based on the measurement principle of a laser triangulation method.
Disclosure of Invention
The invention designs a line laser-based three-dimensional overall measurement method for precision parts, which can be applied to high-precision three-dimensional measurement and overcomes the defect of poor quality of traditional optical modeling point clouds.
The hardware system of the three-dimensional overall measurement method comprises the following steps:
the line laser profile scanner is used for acquiring the profile of the part;
a computer for precision control, contour acquisition and data processing;
the grating ruler and the encoder are used for synchronously triggering the acquisition signals;
a multi-axis scanning platform for fixing the scanner probe and motion control;
a standard gauge block and a calibration ball for calibration;
the invention designs a three-dimensional full-view measuring method of a precision part based on line laser, which is characterized by comprising the following steps:
step 1: starting a line laser sensor for collecting the outline of the precision part and the multi-axis scanning platform for motion control; acquiring the measured object once by using the line laser sensor to obtain the section profile P of the measured object; carrying out translation scanning on the measured object, namely acquiring the profile for multiple times along one axis to obtain batch processing profile data B;
step 2: performing inclination correction on the section profile P in the step 1 by using a least square method; let the measured cross-sectional profile be P (x)i，yi) i is 1, 2 …, n, and the compensated cross-sectional profile P' (x) is obtained according to equation (1)i，yi-axi-b)；
Figure BSA0000166996900000011
And step 3: respectively calibrating the x, y and z axial directions of a sensor coordinate system by utilizing a step gauge block, and setting a step gauge block standard wheelProfile is S, and profile measurement error e is obtained according to formula (2)pObtaining minimum values under different compensation proportions c;
epmin ∑ (cP' -S) formula (2)
And 4, step 4: obtaining calibrated profile data cP' through the steps, determining a world coordinate system and a unit of the calibrated profile data according to the coordinate system of the line laser sensor in the step 3, converting the calibrated profile data into three-dimensional point cloud data, fixing an x-axis coordinate corresponding to each point, determining a z-axis coordinate by the size of the step gauge block in the step 3, and determining a y-axis coordinate by the acquisition frequency and the step length of the line laser sensor in the step 1;
and 5: carrying out bilateral filtering on the three-dimensional point cloud according to a formula (3) to remove point cloud noise, and calculating to obtain d as an adjustment distance of a point to be processed in the normal vector direction of the point to be processed; calculating the required weight W according to equation (4)cAnd WsLet P be a point in P, N (P) represent the k neighborhood of point P, | P-PiI denotes the slave point piThe length of the modulus of the vector to point p,
Figure BSA0000166996900000021
representing the normal vector at the point p,
Figure BSA0000166996900000022
representing a vector
Figure BSA0000166996900000023
And from point piThe inner product of the vectors to point p;
Figure BSA0000166996900000024
Figure BSA0000166996900000025
step 6: calibrating a rotating shaft of the multi-shaft scanning platform by using the standard sphere; with a radius of RBThe calibration ball is placed on a rotary table of the scanning platform, and the calibration ball passes through the step 1The translation scanning method of the method obtains point cloud data of a part of spherical surface to obtain the spherical coordinates of the spherical surface
Figure BSA0000166996900000026
Constructing a nonlinear equation system according to the formula (5);
Figure BSA0000166996900000027
and 7: solving the sphere center coordinate O of the spherical surface in the step 6n(xn，yn，zn) Driving the rotary table in the step 6 to rotate, respectively measuring the three-dimensional data of the spherical surface in the step 6 at N (N is more than or equal to 4) positions, obtaining N groups of spherical center coordinate data by using the step 6, and constructing N groups of linear equation sets by using the spherical center coordinates according to the formula (6);
Axn+Byn+Czn+ D is 0 formula (6)
And 8: solving the linear equation set in the step 7, and fitting the spherical center O in the step 6nEquation P of the plane of the rotation trajectoryBCalculating P according to equation (7)BThe normal vector u of (a); the coordinates O of the sphere center in the step 6nSubstituting the formula (8) to obtain the intersection point O of the rotating shaft of the sphere center in the step 6 and the plane where the sphere center is located in the step 6n′(xn′，yn′，zn′)；
Figure BSA0000166996900000028
Figure BSA0000166996900000031
And step 9: let RuFor the rotation matrix of the point cloud to be converted, T (x)n′，yn′，zn') is a translation matrix of the point cloud to be converted, and the rotation angle is theta; combining u and O calculated in step 8nCalculating the coordinates of each point cloud under different viewing angles in a corresponding world coordinate system by using the formulas (9) and (10);
Figure BSA0000166996900000032
Figure BSA0000166996900000033
Figure BSA0000166996900000034
step 10: converting the point clouds of all parts in the step 9 into the same coordinate system according to the scanning sequence of the rotating shaft to finish rough registration of the point clouds, and then performing fine registration on the point clouds after the rough registration by searching an iterative closest point; let two point clouds to be registered be Pi，QiDefining an objective function epsilon (a) according to formula (11), and calculating the rotational-translational matrix R of step 9u，T(xn′，yn′，zn') obtaining a rigid transformation matrix R, T meeting the threshold value by adopting a traversal search method;
Figure BSA0000166996900000035
wherein Φ P is PiMiddle point piCorresponding points of the fitted surface are located;
step 11: for the point cloud data P to be transformed in the step 10iSubstituting the rigid body transformation matrix and the rotational translation matrix R, T in the step 10 into a formula (12) to obtain the point cloud Q after registration in the step 10i；
Qi＝RPi+ T formula (12)
Step 12: repeating the step 10 and the step 11 to finish the fine registration of all the visual angle point clouds to be converted to obtain the three-dimensional full-looking point cloud data of the measured object;
step 13: down-sampling the point cloud by establishing a grid, simplifying the point cloud data in the step 12, and performing point cloud simplification on the point cloud data obtained in the step 12 according to a formula (13), whereinD is the side length of the voxel grid, α is a scale factor, N is the number of point clouds of the point cloud obtained in step 12, (D)x，Dy，Dz) The maximum (x) of the point cloud data obtained in the step 12 in the directions of the three coordinate axes of x, y and z is shownmax，ymax，zmax) Minimum (x)min，ymin，zmin) Coordinate values and are obtained according to the formula (14);
Figure BSA0000166996900000041
Figure BSA0000166996900000042
step 14: converting the simplified point cloud data in the step 13 into polygons for surface reconstruction by a method for constructing a neural network, and constructing a network kernel function according to a formula (15)
Figure BSA0000166996900000045
And the radial range of action sigma and the centre of action P are determinedi(ii) a Let omegaiInterpolating the point cloud P simplified in the step 13 according to a formula (16) for the network weight to be trained to obtain a full-looking high-precision three-dimensional reconstruction f (P) of the measured object;
Figure BSA0000166996900000043
Figure BSA0000166996900000044
and finishing the operation of the measured three-dimensional overall information. The flow chart of the three-dimensional overall measurement method related to the patent of the invention is shown in figure 1. And f (P) is the three-dimensional overall information of the object to be measured.
The invention has the beneficial effects that: the three-dimensional point cloud overall measurement method introduced by the invention can solve the problems of large point cloud noise, low matching precision and the like of small complex workpieces, can realize high-precision three-dimensional overall measurement of complete point cloud data of the small workpieces without spraying a developer, and avoids the defects in the traditional structured light three-dimensional measurement method.
Drawings
FIG. 1: a flow chart of a line laser three-dimensional overall measurement method;
FIG. 2: measuring block ladder three-dimensional point cloud data;
FIG. 3: a schematic diagram of a rotating shaft calibration method;
FIG. 4: the three-dimensional overall effect graph of the workpiece is measured by the method.
Detailed Description
The laser triangulation method is to project a laser beam to the surface of an object to be measured by using a laser, and calculate the cross-sectional shape of the object according to the change of laser light bars acquired by a camera.
According to determination, the width (x axial direction) of the acquired stripe is 16mm, the batch processing length (y axial direction) is 100mm, the height difference range (z axial direction) is 16mm, and the highest acquisition frequency is 500Hz when the stripe is shot by a line laser sensor in an experimental scene.
The gauge block ladder is formed by combining standard gauge blocks with the thicknesses of 1.08mm, 1.5mm and 2mm into a ladder, and the scanned point cloud data of the gauge block ladder is shown in figure 2. The edge data is removed to reduce the error, and the calibrated tilt compensation line is y equal to 1.167 multiplied by 10-5x +1.078, and the compensation ratio c is 0.993.
The radius of a calibration ball used for calibrating the rotating shaft is RBThe method comprises the steps of rotating a 10mm ceramic ball clockwise for 60 degrees each time, measuring for 6 times in total, obtaining point cloud data of 6 spheres, and calculating coordinates of 6 sphere centers
Figure BSA0000166996900000051
And calculating the normal vector u of the rotating shaft and the equation P of the rotating planeBFig. 3 is a schematic diagram of a method for calibrating a rotating shaft.
And (3) converting the 6 parts of point clouds into a rotation and translation matrix according to theta (i is i multiplied by 60) and (i is 1, 2, … 6) for coordinate transformation according to a rotating shaft calibration result and a fine registration method.
And (3) performing curved surface reconstruction on the point cloud data through constructing a neural network to finally obtain complete three-dimensional overall data of the workpiece, wherein an effect diagram of three-dimensional overall measurement of the workpiece is shown in fig. 4.
The invention designs a three-dimensional full-view measuring method of a precision part based on line laser, which is characterized by comprising the following steps:
step 1: starting a line laser sensor for collecting the outline of the precision part and the multi-axis scanning platform for motion control; acquiring the measured object once by using the line laser sensor to obtain the section profile P of the measured object; carrying out translation scanning on the measured object, namely acquiring the profile for multiple times along one axis to obtain batch processing profile data B;
step 2: performing inclination correction on the section profile P in the step 1 by using a least square method; let the measured cross-sectional profile be P (x)i，yi) The compensated cross-sectional profile P' (x) is obtained as follows, i being 1, 2 …, ni，yi-axi-b)；
Figure BSA0000166996900000052
And step 3: respectively calibrating the x, y and z axial directions of a sensor coordinate system by utilizing a step gauge block, setting the standard profile of the step gauge block as S, and enabling the profile measurement error e according to the following formulapObtaining minimum values under different compensation proportions c;
ep＝min∑(cP′-S)
and 4, step 4: obtaining calibrated profile data cP' through the steps, determining a world coordinate system and a unit of the calibrated profile data according to the coordinate system of the line laser sensor in the step 3, converting the calibrated profile data into three-dimensional point cloud data, fixing an x-axis coordinate corresponding to each point, determining a z-axis coordinate by the size of the step gauge block in the step 3, and determining a y-axis coordinate by the acquisition frequency and the step length of the line laser sensor in the step 1;
and 5: carrying out bilateral filtering on the three-dimensional point cloud according to the following formula to remove point cloud noise, and calculating to obtain d as an adjustment distance of a point to be processed in the normal vector direction of the point to be processed; according to the following formulaCalculating the required weight WcAnd WsLet P be a point in P, N (P) represent the k neighborhood of point P, | P-PiI denotes the slave point piThe length of the modulus of the vector to point p,
Figure BSA0000166996900000056
representing the normal vector at the point p,
Figure BSA0000166996900000053
representing a vector
Figure BSA0000166996900000054
And from point piThe inner product of the vectors to point p;
Figure BSA0000166996900000055
Figure BSA0000166996900000061
step 6: calibrating a rotating shaft of the multi-shaft scanning platform by using the standard sphere; with a radius of RBThe calibration ball is placed on a rotary table of the scanning platform, and the point cloud data of part of the spherical surface is obtained by the translation scanning method of the method in the step 1 to obtain the spherical coordinates of the spherical surface
Figure BSA0000166996900000062
Constructing a nonlinear equation system according to the following formula;
Figure BSA0000166996900000063
and 7: solving the sphere center coordinate O of the spherical surface in the step 6n(xn，yn，zn) Driving the rotary table in the step 6 to rotate, respectively measuring the three-dimensional data of the spherical surface in the step 6 at N (N is more than or equal to 4) positions, obtaining N groups of spherical center coordinate data by using the step 6, and constructing N groups of linear equation sets by using the spherical center coordinates according to the following formula;
Axn+Byn+Czn+D＝0
and 8: solving the linear equation set in the step 7, and fitting the spherical center O in the step 6nEquation P of the plane of the rotation trajectoryBP is calculated as followsBThe normal vector u of (a); the coordinates O of the sphere center in the step 6nSubstituting the formula to obtain the intersection point O of the rotating shaft of the sphere center in the step 6 and the plane where the sphere center is located in the step 6n′(xn′，yn′，zn′)；
Figure BSA0000166996900000064
Figure BSA0000166996900000065
And step 9: let RuFor the rotation matrix of the point cloud to be converted, T (x)n′，yn′，zn') is a translation matrix of the point cloud to be converted, and the rotation angle is theta; combining u and O calculated in step 8nCalculating the coordinates of each point cloud under different viewing angles in a corresponding world coordinate system by using the following formula;
Figure BSA0000166996900000066
Figure BSA0000166996900000071
Figure BSA0000166996900000072
step 10: converting the point clouds of all parts in the step 9 into the same coordinate system according to the scanning sequence of the rotating shaft to finish rough registration of the point clouds, and then performing fine registration on the point clouds after the rough registration by searching an iterative closest point; let two point clouds to be registered be Pi，QiDefining an objective function ε (a) according to the following formula, and calculating in step 9The rotational translation matrix Ru，T(xn′，yn′，zn') obtaining a rigid transformation matrix R, T meeting the threshold value by adopting a traversal search method;
Figure BSA0000166996900000073
wherein Φ P is PiMiddle point piCorresponding points of the fitted surface are located;
step 11: for the point cloud data P to be transformed in the step 10iSubstituting the rigid body transformation matrix and the rotational translation matrix R, T in the step 10 into the following formula to obtain the point cloud Q after registration in the step 10i；
Qi＝RPi+T
Step 12: repeating the step 10 and the step 11 to finish the fine registration of all the visual angle point clouds to be converted to obtain the three-dimensional full-looking point cloud data of the measured object;
step 13, down-sampling the point cloud by establishing a grid, simplifying the point cloud data in the step 12, and performing point cloud simplification on the point cloud data obtained in the step 12 according to the following formula, wherein D is the side length of a voxel grid, α is a scale factor, N is the point cloud number of the point cloud obtained in the step 12, (D)x，Dy，Dz) The maximum (x) of the point cloud data obtained in the step 12 in the directions of the three coordinate axes of x, y and z is shownmax，ymax，zmax) Minimum (x)min，ymin，zmin) Coordinate values and are obtained according to the following formula;
Figure BSA0000166996900000074
Figure BSA0000166996900000075
step 14: converting the simplified point cloud data in the step 13 into polygons for surface reconstruction by a method for constructing a neural network, and constructing a network kernel function according to the following formula
Figure BSA0000166996900000076
And the radial range of action sigma and the centre of action P are determinedi(ii) a Let omegaiInterpolating the point cloud P simplified in the step 13 according to the following formula for the network weight to be trained to obtain the full-appearance high-precision three-dimensional reconstruction f (P) of the measured object;
Figure BSA0000166996900000081
Figure BSA0000166996900000082
the flow chart of the three-dimensional overall measurement method related to the patent of the invention is shown in figure 1.
The biggest difference between the method and the existing three-dimensional reconstruction method is as follows: the existing three-dimensional reconstruction method has the problems of insufficient reconstruction precision of small workpieces and higher requirements on surface materials, thereby causing point cloud loss, high point cloud noise, low splicing precision and the like, and the problems can be avoided only by spraying a developer. The three-dimensional overall measurement method designed by the invention has the advantages that the extracted contour and the fused point cloud not only contain more detailed information, but also contain complete global information of the whole object through two calibration methods, so that the problems existing in the existing method are fundamentally solved. Therefore, the method designed by the invention can solve the problem of high-precision three-dimensional full-view measurement of small workpieces.
In summary, the three-dimensional overall measurement method of the present invention has the following advantages:
(1) the correction method for extracting the contour is more accurate, so that the three-dimensional full-view measurement method designed by the invention is more in line with the contour size of an actual model.
(2) Due to the double registration method of the rotation axis calibration and the closest point iteration, the three-dimensional reconstruction method designed by the invention can realize high-precision point cloud registration.
(3) The measurement problem of objects with different surface materials is solved through bilateral filtering and point cloud simplification, coloring materials such as a spraying developer and the like are not needed, the measurement process is green and environment-friendly, and the material consumption cost in measurement is saved.
The invention and its embodiments have been described above schematically, without limitation, and the figures shown in the drawings represent only one embodiment of the invention. Therefore, if persons skilled in the art should be informed by the teachings of the present invention, other similar components or other arrangements of components may be adopted without departing from the spirit of the present invention, and technical solutions and embodiments similar to the technical solutions may be creatively designed without departing from the scope of the present invention.

Claims (1)

1. A three-dimensional full-view measuring method of a precision part based on line laser is characterized by comprising the following steps:
step 1: starting a line laser sensor for collecting the outline of the precision part and a multi-axis scanning platform for motion control; acquiring the measured object once by using the line laser sensor to obtain the section profile P of the measured object; carrying out translation scanning on the measured object, namely acquiring the profile for multiple times along one axis to obtain batch processing profile data B;
step 2: the section profile P described in step 1 is subjected to tilt correction using the least squares method: let the measured cross-sectional profile be P (x)i，yi) i is 0, 1, 2 …, n, and the compensated cross-sectional profile P' (x) is obtained according to equation (1)i，yi-axi-b)；
Figure FSB0000185648810000011
And step 3: respectively calibrating the x, y and z axial directions of a sensor coordinate system by utilizing a step gauge block, setting the standard profile of the step gauge block as S, and enabling the profile measurement error e according to a formula (2)pObtaining minimum values under different compensation proportions c;
epmin ∑ (cP' -S) formula (2)
And 4, step 4: obtaining calibrated profile data cP' through the steps 1 to 3, determining a world coordinate system and a unit of the calibrated profile data according to the coordinate system of the line laser sensor in the step 3, converting the calibrated profile data into three-dimensional point cloud data, fixing an x-axis coordinate corresponding to each point, determining a z-axis coordinate by the size of the step gauge block in the step 3, and determining a y-axis coordinate by the acquisition frequency and the step length of the line laser sensor in the step 1;
and 5: carrying out bilateral filtering on the three-dimensional point cloud according to a formula (3) to remove point cloud noise, and calculating to obtain d as an adjustment distance of a point to be processed in the normal vector direction of the point to be processed; calculating the required weight W according to equation (4)cAnd WsLet P be a point in P, N (P) represent the k neighborhood of point P, | P-PiI denotes the slave point piThe length of the modulus of the vector to point p,
Figure FSB0000185648810000018
representing the normal vector at the point p,
Figure FSB0000185648810000012
representing a vector
Figure FSB0000185648810000013
And from point piThe inner product of the vectors to point p;
Figure FSB0000185648810000014
Figure FSB0000185648810000015
step 6: calibrating a rotating shaft of the multi-shaft scanning platform by using a standard sphere; with a radius of RBThe calibration ball is placed on a rotary table of the scanning platform, and the point cloud data of part of the spherical surface is obtained by the translation scanning method in the step 1 to obtain the spherical coordinates of the spherical surface
Figure FSB0000185648810000016
Constructing a nonlinear equation system according to the formula (5);
Figure FSB0000185648810000017
and 7: solving the sphere center coordinate O of the spherical surface in the step 6n(xn，yn，zn) Driving the rotary table in the step 6 to rotate, respectively measuring the three-dimensional data of the spherical surface in the step 6 at N (N is more than or equal to 4) positions, obtaining N groups of spherical center coordinate data by using the step 6, and constructing N groups of linear equation sets by using the spherical center coordinates according to the formula (6);
Axn+Byn+Czn+ D is 0 formula (6)
And 8: solving the linear equation set in the step 7, and fitting the spherical center O in the step 7nEquation P of the plane of the rotation trajectoryBCalculating P according to equation (7)BThe normal vector u of (a); the coordinates O of the sphere center in the step 7nSubstituting the formula (8) to obtain the intersection point O of the rotating shaft of the sphere center in the step 7 and the plane where the sphere center is located in the step 7n′(xn′，yn′，zn′)；
Figure FSB0000185648810000021
Figure FSB0000185648810000022
And step 9: let RuFor the rotation matrix of the point cloud to be converted, T (x)n′，yn′，zn') is a translation matrix of the point cloud to be converted, and the rotation angle is theta; combining u and O calculated in step 8nCalculating the coordinates of each point cloud under different viewing angles in a corresponding world coordinate system by using the formulas (9) and (10);
Figure FSB0000185648810000023
Figure FSB0000185648810000024
step 10: converting the point clouds to be converted in the step 9 into the same coordinate system according to the scanning sequence of the rotating shaft, completing the coarse registration of the point clouds, and then performing fine registration on the point clouds after the coarse registration by searching an iterative closest point; let two point clouds to be registered be Pi，QiDefining an objective function epsilon (a) according to formula (11), and calculating the rotation matrix R of step 9u，T(xn′，yn′，zn') obtaining rigid transformation matrixes R and T meeting the threshold value by adopting a traversal search method;
Figure FSB0000185648810000025
wherein Φ P is PiMiddle point piCorresponding points of the fitted surface are located;
step 11: for the point cloud data P to be transformed in the step 10iSubstituting the rigid transformation matrix R and T in the step 10 into a formula (12) to obtain the point cloud Q after registration in the step 10i；
Qi＝RPi+ T formula (12)
Step 12: repeating the step 10 and the step 11 to finish the fine registration of all the visual angle point clouds to be converted to obtain the three-dimensional full-looking point cloud data of the measured object;
step 13, down-sampling the point cloud by establishing a grid, simplifying the point cloud data in the step 12, and performing point cloud simplification on the point cloud data obtained in the step 12 according to a formula (13), wherein D is the side length of a voxel grid, α is a scale factor, N is the point cloud number of the point cloud obtained in the step 12, (D)x，Dy，Dz) The maximum (x) of the point cloud data obtained in the step 12 in the directions of the three coordinate axes of x, y and z is shownmax，ymax，zmax) Minimum (x)min，ymin，zmin) Coordinate values based onFormula (14) is obtained;
Figure FSB0000185648810000031
Figure FSB0000185648810000032
step 14: converting the simplified point cloud data in the step 13 into polygons for surface reconstruction by a method for constructing a neural network, and constructing a network kernel function according to a formula (15)
Figure FSB0000185648810000033
And the radial range of action sigma and the centre of action P are determinedi(ii) a Let omegaiInterpolating the point cloud P simplified in the step 13 according to a formula (16) for the network weight to be trained to obtain a full-looking high-precision three-dimensional reconstruction f (P) of the measured object;
Figure FSB0000185648810000034
Figure FSB0000185648810000035
and finishing the operation of the measured three-dimensional overall information.
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