WO2023060717A1 - 一种物体表面高精度定位方法及系统 - Google Patents

一种物体表面高精度定位方法及系统 Download PDF

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WO2023060717A1
WO2023060717A1 PCT/CN2021/133252 CN2021133252W WO2023060717A1 WO 2023060717 A1 WO2023060717 A1 WO 2023060717A1 CN 2021133252 W CN2021133252 W CN 2021133252W WO 2023060717 A1 WO2023060717 A1 WO 2023060717A1
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image
mark
real
tested
marks
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PCT/CN2021/133252
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French (fr)
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杨夏
覃军友
郭贵松
张小虎
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中山大学
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Priority to JP2023524731A priority Critical patent/JP2023549658A/ja
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/70—Determining position or orientation of objects or cameras
    • G06T7/73—Determining position or orientation of objects or cameras using feature-based methods
    • 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
    • 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
    • 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
    • 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
    • G01B11/26—Measuring arrangements characterised by the use of optical techniques for measuring angles or tapers; for testing the alignment of axes
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
    • G06F18/213—Feature extraction, e.g. by transforming the feature space; Summarisation; Mappings, e.g. subspace methods
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
    • G06F18/213—Feature extraction, e.g. by transforming the feature space; Summarisation; Mappings, e.g. subspace methods
    • G06F18/2135—Feature extraction, e.g. by transforming the feature space; Summarisation; Mappings, e.g. subspace methods based on approximation criteria, e.g. principal component analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T3/00—Geometric image transformations in the plane of the image
    • G06T3/40—Scaling of whole images or parts thereof, e.g. expanding or contracting
    • G06T3/4038—Image mosaicing, e.g. composing plane images from plane sub-images
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/40—Analysis of texture
    • G06T7/41—Analysis of texture based on statistical description of texture
    • G06T7/46—Analysis of texture based on statistical description of texture using random fields
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20212—Image combination
    • G06T2207/20221—Image fusion; Image merging

Definitions

  • the invention relates to the field of measurement technology, in particular to a high-precision positioning method and system for an object surface.
  • High-precision positioning of object surfaces is an important content in the field of measurement.
  • the displacement or rotation of the object can be directly measured with high precision, and then high-precision displacement or rotation angle data can be provided for precision instruments, precision equipment or high-precision weapons and equipment.
  • This kind of technology can be widely used in the fields of robots, aerospace equipment, photoelectric theodolite, radar, CNC machine tools and various industrial automation equipment, and is of great significance to industries such as national defense, aerospace, precision manufacturing, and automation industries.
  • Grating measurement technology is a technology that uses Moiré fringes for precise measurement.
  • the measurement principle is: when two gratings overlap, moiré fringes will be generated, and the moiré fringes can amplify the relative small displacement of the two gratings, and the high-precision positioning of the grating can be realized by measuring the relevant information of the moiré fringes, thus Realize high-precision measurement of displacement or angle.
  • the grating measurement technology has the advantages of small size, high precision, and strong anti-interference ability, it has very high requirements on materials and processes, and it is very difficult to manufacture and process;
  • the measurement technology will still be restricted by many factors, and the actual measurement accuracy will be greatly affected, such as the influence of the optical part of the encoder such as the motherboard error and the engraving error of the code disc, the influence of the mechanical parts such as bearings and structures, the influence of the light emission source and the The influence of the electrical part such as the receiving unit and the influence of the signal delay in the use of the encoder, etc.
  • the object of the present invention is to provide a high-precision positioning method and system for the surface of an object.
  • This algorithm is a semi-supervised learning method, which can take pictures of the built-in features or texture features of the surface of the object through a single camera, and realize two-dimensional translation and three-dimensional translation of the object. High-precision measurement of parameters such as translation and two-dimensional rotation angle.
  • the first technical solution adopted in the present invention is: a method for high-precision positioning on the surface of an object, comprising the following steps:
  • the markers produced need to meet the following settings: 1. Ensure that after imaging the surface of the object, the background images of different markers in the image have a certain degree of discrimination, so that the markers can be detected and identified from the image quickly and reliably; 2. Markers need to be on the surface of the object Reach a certain distribution density to ensure that there is at least one complete mark in the imaging field of view when the camera shoots any part of the object surface to be tested.
  • the position of the object surface corresponding to the current real-time image to be measured is calculated.
  • the step of making marks with a preset distribution density on the surface of the object to obtain the marked object specifically includes:
  • the mark of the preset style is engraved on the surface by the mark making method, and the object with the mark is obtained;
  • the marks of the preset style include line segments, points, circles, squares, crosses and their combined shapes;
  • the marking method comprises laser engraving, printing and etching
  • the engraving interval of the marks is less than half of the image frame.
  • the step of photographing the marked object and numbering the marks in the image to obtain a surface image with numbered marks specifically includes:
  • the surface image is processed by threshold segmentation, and all marked target centers in the surface image are detected;
  • the principal component analysis method is used to establish the corresponding relationship between the marked feature image and the marked number, reduce the marked feature image to low dimension, and take the first 10 principal components of the principal component matrix as a one-dimensional vector representing the mark, and determine the unique number;
  • a surface image with numbered markers is obtained.
  • the step of obtaining the relative position information of each mark on the surface of the object according to the image with the numbered mark and establishing the corresponding relationship between the mark number and the mark position information specifically includes:
  • Image stitching is performed on images with numbered marks by an image stitching method to obtain a complete surface image
  • Identify and locate the marks on the reference image and determine the corresponding relationship between each mark and the surface coordinates of the object according to the relationship between the pixel coordinates of the reference image and the actual object surface coordinates.
  • the step of obtaining the real-time image to be tested and detecting the mark in the real-time image to be tested to obtain the mark to be tested specifically includes:
  • the marker detection is performed on the real-time image to be tested, and the marker number corresponding to the marker in the real-time image to be tested is confirmed based on vector comparison.
  • s is the Z coordinate of the center point in the camera coordinate system
  • (u, v) is the center image coordinate
  • (f x , f y ) is the equivalent focal length of the camera
  • (c x , c y ) is the image Principal point
  • (x w ,y w ,z w ) represents the surface of the object
  • the step of calculating the object surface position corresponding to the current real-time image to be tested according to the position of the marker to be tested in the real-time image to be tested and the position information of the object surface corresponding to the marker specifically includes:
  • the object surface position corresponding to the current real-time image to be tested is obtained;
  • the object surface position corresponding to the current real-time image to be measured includes one-dimensional coordinates, two-dimensional coordinates, three-dimensional coordinates, one-dimensional corners and two-dimensional corners.
  • the step of calculating the object surface position corresponding to the current real-time image to be tested according to the position of the marker to be tested in the real-time image to be tested and the position information of the object surface corresponding to the marker specifically includes:
  • ⁇ is the rotation angle of the rotating body to be measured
  • y 0 is the origin of the circumference of the outer surface of the rotation
  • y c is the center coordinate point of the marking of the outer surface of the rotation
  • L is the length of the circumference of the outer surface of the rotation.
  • the coordinates of the center of the image are (u, v) and the world coordinates on the surface of the sphere to be measured are marked as (x w , y w , z w ), ⁇ is the longitude of the sphere to be measured, and ⁇ is the latitude of the sphere to be measured .
  • the second technical solution adopted in the present invention is: a high-precision positioning system on the surface of an object, comprising:
  • a mark making module which is used to make marks with a preset distribution density on the surface of the object to obtain a marked object
  • the numbering module is used to shoot the marked object, and number the marks in the image to obtain the image with the numbered mark;
  • the marker position relationship module is used to obtain the relative position information of each marker on the surface of the object according to the image with the numbered marker and establish the corresponding relationship between the marker number and the marker position information;
  • the image-to-be-tested module is used to acquire the real-time image to be tested and detect the marks in the real-time image to be tested to obtain the mark to be tested;
  • the physical quantity settlement module is used to calculate the position of the object surface corresponding to the current real-time image to be measured according to the position of the mark to be measured in the real-time image to be measured and the position information of the object surface corresponding to the mark.
  • the invention realizes high-precision measurement of parameters such as two-dimensional translation, three-dimensional translation, and two-dimensional rotation angle of the object by photographing the built-in features or the fabricated texture features of the object surface by a single camera.
  • the actual position of each mark relative to the object to be measured can be obtained with high precision, and then the surface of the object can be positioned and measured according to the actual position of the mark (rather than the theoretical position), thus avoiding the possibility of marking Factors such as processing errors and installation errors affect the measurement accuracy; and the present invention does not need to make and install information rings, high-precision processing and coding marks, and the method is simpler and more economical.
  • Fig. 1 is a flow chart of the steps of a method for high-precision positioning of an object surface in the present invention
  • Fig. 2 is a structural block diagram of an object surface high-precision positioning system of the present invention
  • Fig. 3 is a schematic diagram of camera imaging according to a specific embodiment of the present invention.
  • Fig. 4 is a schematic diagram of making a mark according to a specific embodiment of the present invention.
  • Fig. 5 is a schematic diagram of naming and numbering of a specific embodiment of the present invention.
  • Fig. 6 is a schematic diagram of physical quantity calculation according to a specific embodiment of the present invention.
  • the present invention provides a kind of object surface high precision positioning method, and this method comprises the following steps:
  • the coordinates of each mark on the surface of the object can be directly measured using a three-coordinate measuring instrument or stereo vision measurement technology, and the corresponding relationship between each mark and the coordinates of the object surface can be determined.
  • the step of S2, photographing the marked object, and numbering the marks in the image to obtain an image with numbered marks specifically includes:
  • the image is binarized and segmented by thresholding:
  • f(x, y) is the gray value of the image
  • x, y are the pixel coordinates
  • Th is the threshold. Then perform area growth on the pixels with a gray value of 255, and confirm the target according to the area of the target. If the number of points connected to the current pixel with a pixel value of 255 is less than the threshold Ts, the current point is not the target; on the contrary, if If the number of points with a pixel value of 255 is greater than the threshold Ts, the current point is confirmed to be the target:
  • the center of mass is obtained by locating the center of mass of the confirmed target, and the center of mass is the coordinate center of each point whose pixel value is 255:
  • n is the number of points with a pixel value of 255 in the current target
  • ( xi , y i ) are the image coordinates of each point.
  • an image with a size of 200 ⁇ 200 pixels is intercepted as the feature image of the marker, and different markers can be distinguished through the feature image.
  • the principal component analysis method is used to establish the corresponding relationship between the marker feature image and the marker number, and the dimension of the marker feature image with a size of 200 ⁇ 200 pixels is reduced into a one-dimensional vector represented by 10 components, and the one-dimensional vector is used as the displacement number.
  • the step S24 specifically includes:
  • the step of acquiring the relative position information of each marker on the surface of the object according to the image with numbered markers and establishing the corresponding relationship between marker numbers and marker position information specifically includes:
  • the relationship between the image plane points and the two-dimensional points on the local area of the object surface can be expressed as:
  • a, b, c, d, e, f are six parameters describing the deformation between two planes, (u, v) are image pixel coordinates, and (x, y) are coordinates on the approximate plane of the frustum of a cone.
  • the deformation parameters can be calculated by combining the control points (u i , v i ) on the surface of the object with the corresponding points (xi , y i ) in the image as follows:
  • the v direction of the reference graph corresponds to the height direction of the rotating body, and the u direction of the reference graph corresponds to the rotation angle of the rotating body.
  • ,v) and the coordinates ( ⁇ ,h) of the side surface of the cylinder are as follows:
  • a and b are scale parameters, which can be obtained through camera calibration, or can be obtained by calculating the following equations by combining the control points ( ⁇ i , h i ) on the object surface and the corresponding points (u i , v i ) in the image:
  • Identify and locate each marker on the reference image and determine the corresponding relationship between each marker and the object surface coordinates according to the relationship between the reference image pixel coordinates and the actual object surface coordinates.
  • the step of obtaining the real-time image to be tested and detecting the mark in the real-time image to be tested to obtain the mark to be tested specifically includes:
  • the precise position on the surface of the frustum of the cone corresponding to each mark center point is measured, such as two-dimensional coordinates or three-dimensional coordinates.
  • the two-dimensional coordinates (x c , y c ) and three-dimensional coordinates (x w , y w , z w ) of the mark center in the circular frustum coordinate system can be directly measured by two-dimensional or three-dimensional coordinate measuring instruments; three-dimensional coordinates can also be used
  • the scanner is used to scan the three-dimensional point cloud of the marker point and other parts on the surface of the circular platform, so as to obtain the three-dimensional coordinates (x w , y w , z w ) of the marker point relative to the coordinate system of the circular platform.
  • the position of the surface of the circular platform corresponding to the center point of the mark can be determined according to the imaging parameters: if the image coordinates of the center of the mark are (u, v), then the transformation formula between it and the world coordinates (x w , y w , z w ) is as follows :
  • the first matrix on the right is the camera intrinsic parameter matrix
  • the second matrix is the camera extrinsic parameter matrix.
  • the internal parameter matrix and external parameter matrix of the camera can be obtained by camera calibration methods such as Zhang Zhengyou calibration method. Use (x w , y w , z w ) in the above formula to represent the three-dimensional point coordinates corresponding to the surface of the cone and the center of the logo.
  • s is the Z coordinate of the center point in the camera coordinate system
  • (u,v) is the center image coordinate
  • (f x , f y ) is the equivalent focal length of the camera
  • (c x , cy ) is the principal point of the image
  • (x w ,y w ,z w ) means that the surface of the object corresponds to the center of the mark
  • real-time imaging is performed on the surface of the circular platform, mark detection and positioning are performed on the real-time image (the detection process is the same as S3), the mark number corresponding to the current mark is confirmed, and the corresponding coordinates of the real-time image center on the reference image are calculated.
  • the detection process is the same as S3
  • the mark number corresponding to the current mark is confirmed
  • the corresponding coordinates of the real-time image center on the reference image are calculated.
  • Marker detection and confirmation According to the PCA dimensionality reduction criterion, the dimensionality of the currently marked feature image is reduced to obtain a one-dimensional vector with 10 components, and the vector is compared with all the marked one-dimensional vectors in the reference image, and the similarity in the reference image The mark number with the highest degree is used as the current mark number.
  • the similarity C of two vectors ⁇ a i ⁇ , ⁇ b i ⁇ is expressed as:
  • width and height are the pixel width and height of the real-time image respectively.
  • the step of calculating the object surface position corresponding to the current real-time image to be tested according to the position of the marker to be tested in the real-time image to be tested and the position information of the object surface corresponding to the marker which Specifically include this step, which specifically includes:
  • the object surface point corresponding to the center of the real-time image can be determined according to the correspondence between the pixel coordinates of the reference image and the object surface parameters.
  • the two-dimensional coordinates (x c , y c ) of the object surface corresponding to the real-time image center (u c , v c ) can be expressed as:
  • parameters such as the displacement of the corresponding point in the center of the real-time image can be directly calculated.
  • the v direction of the reference map corresponds to the height direction of the rotary axis of the circular table
  • the u direction of the reference map corresponds to the rotation angle of the circular table
  • the object surface point ( ⁇ c , h c ) corresponding to the center (u c , v c ) of the real-time map ) Expressed as:
  • the object surface position corresponding to the current real-time image to be measured includes one-dimensional coordinates, two-dimensional coordinates, three-dimensional coordinates, one-dimensional corners and two-dimensional corners.
  • the corresponding relationship between the image position and the one-dimensional rotation angle of the object Let the circumference of the frustum be L, the axis direction of the frustum be the x direction, the circumference direction of the frustum be the y direction, let the pixel coordinates of the center of the image be (u, v), and the corresponding object The two-dimensional coordinates of the surface are (x c , y c ), then the corresponding rotation angle of this point is:
  • ⁇ is the rotation angle of the rotating body to be measured
  • y 0 is the origin of the circumference of the outer surface of the rotation
  • y c is the center coordinate point of the marking of the outer surface of the rotation
  • L is the length of the circumference of the outer surface of the rotation.
  • the corresponding relationship between the image position and the two-dimensional rotation angle (latitude and longitude) of the spherical surface the coordinates of the center of the image are (u, v) and the corresponding world coordinates on the spherical surface are (x w , y w , z w ), assuming that the world coordinate system is based on the center of the sphere is the origin, then the latitude and longitude are:
  • the coordinates of the center of the image are (u, v) and the world coordinates on the surface of the sphere to be measured are marked as (x w , y w , z w ), ⁇ is the longitude of the sphere to be measured, and ⁇ is the latitude of the sphere to be measured .
  • One-dimensional angle change The one-dimensional angle change of the object corresponding to the image center during two real-time imaging is calculated as follows:
  • Two-dimensional angle change The two-dimensional angle change of the sphere surface corresponding to the image center during two real-time imaging (assuming the origin is at the center of the sphere and the radius of the sphere is r) is calculated as follows:
  • the measurement is based on the theoretical position of the scale and the mark, and the measurement basis of the present invention is the real position of the mark on the surface of the object to be measured.
  • the present invention measures the random texture information and marks after production, obtains the actual position of each mark relative to the object to be measured with high precision, and then locates and measures the surface of the object according to the actual position of the mark (rather than the theoretical position) , so as to avoid the influence of factors such as marking processing errors and installation errors on the measurement accuracy.
  • This measurement not only has high precision and good reliability, but also greatly reduces the requirements for processing technology and device materials.
  • the present invention can directly make random speckle information and marks (spraying, pasting) on the surface of objects, without making and installing information rings, high-precision processing and making coding marks, and the method is simpler economy.
  • c) Compared with the information ring, it can only be measured for the cylinder, and the rotation angle is measured by measuring the one-dimensional displacement of the side of the cylinder.
  • the invention can be directly applied to the surface displacement measurement and rotation angle measurement of a circular platform (both sides and upper and lower bottom surfaces), cones, disks and any irregular rotating bodies.
  • the present invention proposes a method for directly detecting and identifying marks for image positioning, without traversing and searching all pixels of the reference image during image positioning, and has the advantages of high execution efficiency, good real-time performance, simplicity and reliability.
  • the present invention adopts simple patterns without code (such as dots, lines, crosshairs, etc.), and can also be compatible with code marks, so that not only the mark
  • the processing and production are simpler and more convenient, completely get rid of the high-precision processing requirements, and can also make the imaging range of the camera smaller, thereby obtaining higher measurement accuracy.
  • the present invention only needs to use a single-camera to realize the identification and positioning of the marking points on the surface of the object, making the whole measurement process simpler and more efficient.
  • the present invention can also be used for the measurement of parameters such as two-dimensional translation, three-dimensional translation, and two-dimensional rotation angle.
  • a high-precision positioning system on the surface of an object includes:
  • a mark making module which is used to make marks with a preset distribution density on the surface of the object to obtain a marked object
  • the numbering module is used to shoot the marked object, and number the marks in the image to obtain the image with the numbered mark;
  • the marker position relationship module is used to obtain the relative position information of each marker on the surface of the object according to the image with the numbered marker and establish the corresponding relationship between the marker number and the marker position information;
  • the image-to-be-tested module is used to acquire the real-time image to be tested and detect the marks in the real-time image to be tested to obtain the mark to be tested;
  • the physical quantity calculation module is used to calculate the object surface position corresponding to the current real-time image to be measured according to the position of the mark to be measured in the real-time image to be measured and the position information of the object surface corresponding to the mark.

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Abstract

一种物体表面高精度定位方法及系统，定位方法包括：在物体表面制作预设分布密度的标记；对带标记的物体进行拍摄，并编号图像中的标记；获取物体表面各个标记的相对位置信息并建立标记编号与标记位置信息的对应关系；获取实时待测图像并检测实时待测图像内的标记；计算得到当前实时待测图像所对应的物体表面位置。定位系统包括：标记制作模块、编号模块、标记位置关系模块、待测图像模块和物理量解算模块。由此，能够通过单台相机拍摄物体表面的自带特征或制作的纹理特征，实现物体二维平移、三维平移、二维转角等参数的高精度测量，可广泛应用于测量技术领域。

Description

一种物体表面高精度定位方法及系统 技术领域
本发明涉及测量技术领域，尤其涉及一种物体表面高精度定位方法及系统。
背景技术
对物体表面进行高精度定位是测量领域的重要内容。通过物体表面的高精度定位，可以直接高精度测量该物体的位移或转动，进而可以为精密仪器、精密设备或高精度武器装备等提供高精度的位移或转角数据。此类技术可以广泛地应用于机器人、航空航天装备、光电经纬仪、雷达、数控机床及各种工业自动化设备等领域，对国防、航空航天、精密制造、自动化产业等行业都具有重要意义。
目前，物体表面高精度定位的主要方式大多需要依靠光栅来实现。光栅测量技术是一种利用莫尔条纹进行精密测量的技术。其测量原理是：两个光栅重叠时会产生莫尔条纹，莫尔条纹能够对两个光栅的相对微小位移进行放大，通过测量莫尔条纹的相关信息即可实现对光栅的高精度定位，从而实现位移或角度的高精度测量。虽然光栅测量技术具有体积小、精度高、抗干扰能力强等优点，但对材质、工艺的要求非常高，制造加工很困难；即使完成了高精度光栅的加工制造，在进行实际应用时，光栅测量技术依然会受到多种因素制约，实际测量精度受到很大影响，例如码盘的母板误差和刻线误差等编码器光学部分的影响、轴承与结构等机械部分的影响、光发射源与接收单元等电气部分影响以及编码器使用中的信号延迟的影响等。
技术问题
本发明的目的是提供一种物体表面高精度定位方法及系统，此算法为半监督学习方法，能够通过单台相机拍摄物体表面的自带特征或制作的纹理特征，实现物体二维平移、三维平移、二维转角等参数的高精度测量。
技术解决方案
本发明所采用的第一技术方案是：一种物体表面高精度定位方法，包括以下步骤：
在物体表面制作预设分布密度的标记，得到带标记的物体；
制作的标记需要符合如下设置：1.确保在对物体表面成像后，图像中不同标记的背景图有一定区分度，从而可以快速可靠地从图像中检测和识别标记；2.标记在物体表面需要达到一定的分布密度，保证相机在对物体表面的任何待测局部拍摄时，成像视场 内至少存在1个完整标记。
对带标记的物体进行拍摄，并编号图像中的标记，得到带编号标记的图像；
有3种不同命名编号的方式，可以根据实际情况选择其一：1.根据标记自身的图像特征信息进行命名编号；2.根据标记周围的随机纹理图像信息进行命名编号；3.标记自身信息结合其周围纹理的图像信息进行命名编号。
根据带编号标记的图像获取物体表面各个标记的相对位置信息并建立标记编号与标记位置信息的对应关系；
获取实时待测图像并检测实时待测图像内的标记，得到待测标记；
根据待测标记在实时待测图像中的位置以及该标记对应的物体表面位置信息，计算得到当前实时待测图像所对应的物体表面位置。
进一步，所述在物体表面制作预设分布密度的标记，得到带标记的物体这一步骤，其具体包括：
采用预设样式的标记，通过标记制作方法表面刻制标记，得到带标记的物体；
所述预设样式的标记包括线段、点、圆、方形、十字及其组合形状；
所述标记制作方法包括激光雕刻、印刷和刻蚀；
所述标记的刻制间距小于图像画幅的一半。
进一步，所述对带标记的物体进行拍摄，并编号图像中的标记，得到带编号标记的表面图像这一步骤，其具体包括：
对带标记的物体的表面拍摄成像，得到表面图像；
通过阈值分割对表面图像进行处理，检测得到表面图像内的所有标记的目标中心；
以目标中心为参考点，截取预设像素大小的图像作为对应标记的标记特征图像；
采用主成分分析法建立标记特征图像与标记编号的对应关系，将标记特征图像降至低维，并取主成分矩阵前10个主成分为表示该标记的一维向量，确定唯一编号；
得到带编号标记的表面图像。
进一步，所述根据带编号标记的图像获取物体表面各个标记的相对位置信息并建立标记编号与标记位置信息的对应关系这一步骤，其具体包括：
通过图像拼接方法将带编号标记的图像进行图像拼接，得到完整表面图像；
以完整表面图像为基准图并建立基准图的图像像素坐标与实际物体表面坐标的关系；
在基准图像上进行标记的识别和定位，并根据基准图像像素坐标与实际物体表面坐标的关系，确定各个标记与物体表面坐标的对应关系。
进一步，所述获取实时待测图像并检测实时待测图像内的标记，得到待测标记这一步骤，其具体包括：
根据变换公式确定各标记中心点在物体表面的精确位置；
对物体表面进行实时成像，得到实时待测图像；
对实时待测图像进行标记检测，基于向量比对确认实时待测图像内标记对应的标记编号。
进一步，所述变换公式如下式：
Figure PCTCN2021133252-appb-000001
上式中，s为中心点在摄像机坐标系中的Z坐标，(u,v)为中心图像坐标，(f x,f y)为相机的等效焦距，(c x,c y)为图像主点，r ij(i,j＝1,2,3)为旋转矩阵R元素，t i(i＝1,2,3)为平移向量，(x w,y w,z w)代表物体表面与标记中心对应的三维点坐标。
进一步，所述根据待测标记在实时待测图像中的位置以及该标记对应的物体表面位置信息，计算得到当前实时待测图像所对应的物体表面位置这一步骤，其具体包括：
根据标记编号获取实时图像中心在基准图像上的对应坐标；
根据基准图像像素坐标与物体表面参数的对应关系、实时图像中心在基准图像上的坐标，确定实时图像中心对应的物体表面点；
根据实时图像中心对应的物体表面点，得到当前实时待测图像所对应的物体表面位置；
所述当前实时待测图像所对应的物体表面位置包括一维坐标、二维坐标、三维坐标、一维转角和二维转角。
进一步，所述根据待测标记在实时待测图像中的位置以及该标记对应的物体表面位置信息，计算得到当前实时待测图像所对应的物体表面位置这一步骤，其具体包括：
Figure PCTCN2021133252-appb-000002
上式中，θ为待测旋转体的旋转角度，y 0为旋转体外表面展开的周线原点，y c为旋转体外表面展开的标识中心坐标点，L为旋转体外表面展开的周线长。
进一步，所述二维转角的计算公式如下：
Figure PCTCN2021133252-appb-000003
上式中，图像中心坐标为(u,v)对应在待测球体表面的世界坐标记为(x w,y w,z w)，α为待测球体的经度，β为待测球体的纬度。
本发明所采用的第二技术方案是：一种物体表面高精度定位系统，包括：
标记制作模块，用于在物体表面制作预设分布密度的标记，得到带标记的物体；
编号模块，用于对带标记的物体进行拍摄，并编号图像中的标记，得到带编号标记的图像；
标记位置关系模块，用于根据带编号标记的图像获取物体表面各个标记的相对位置信息并建立标记编号与标记位置信息的对应关系；
待测图像模块，用于获取实时待测图像并检测实时待测图像内的标记，得到待测标记；
物理量结算模块，用于根据待测标记在实时待测图像中的位置以及该标记对应的物体表面位置信息，计算得到当前实时待测图像所对应的物体表面位置。
有益效果
本发明通过单台相机拍摄物体表面的自带特征或制作的纹理特征，实现物体二维平移、三维平移、二维转角等参数的高精度测量。通过对制作完成后的标记进行测量，高精度得到各标记相对于待测物体的实际位置，再根据标记的实际位置(而不是理论位置)来进行物体表面的定位和测量，从而避免了标记的加工误差、安装误差等因素对测量精度的影响；且本发明无需制作和安装信息环，无需高精度加工和制作编码标志，方法更为简单经济。
附图说明
图1是本发明一种物体表面高精度定位方法的步骤流程图；
图2是本发明一种物体表面高精度定位系统的结构框图；
图3是本发明具体实施例相机成像示意图；
图4是本发明具体实施例标记制作示意图；
图5是本发明具体实施例命名编号示意图；
图6是本发明具体实施例物理量计算示意图。
本发明的实施方式
本发明以单台线阵相机对圆台侧面进行扫描成像、且圆台表面缺少明显随机自然纹理为例，对本发明的实施方式进行进一步的详细说明。
参照图1，本发明提供了一种物体表面高精度定位方法，该方法包括以下步骤：
S1、在物体表面制作预设分布密度的标记，得到带标记的物体；
具体地，如图4所示，通过激光雕刻技术，采用线段样式的标记，在已经制作了随机纹理的物体表面刻制线段，线段的刻制间距小于图像画幅的一半，保证在对待测物体表面成像时图像内至少有1个完整标记。
S2、对带标记的物体进行拍摄，并编号图像中的标记，得到带编号标记的图像；
具体地，对物体表面所有的标记成像，然后对所有图像通过阈值分割检测标记，使用质心法确定标记图像坐标；并以标记中心为参考点截取一定范围(如200×200像素)的图像作为该标记对应的特征图像(由于纹理信息的随机性，每个标记的特征图像都不相同)；最后采用主成分分析法(PCA)区分标记的特征图像并编号，编号示意图参照图5。
S3、根据带编号标记的图像获取物体表面各个标记的相对位置信息并建立标记编号与标记位置信息的对应关系；
具体地，如图3所示，对于表面缺少明显随机自然纹理的圆台，首先在待测圆台表面喷涂金属漆，金属颗粒的大小根据图像像素的物理分辨率来确定，使得物体表面成像后产生散斑形状的随机图案。然后使用对圆台侧面的各个局部进行具有一定重叠度的成像，并通过图像拼接技术得到圆台一周完整的图像，以此作为基准图。建立基准图的图像像素坐标与圆台表面局部区域二维坐标的对应关系。
可使用三坐标测量仪或者立体视觉测量的技术，直接测量物体表面各标记的坐标，确定各个标记与物体表面坐标的对应关系。
S4、获取实时待测图像并检测实时待测图像内的标记，得到待测标记；
S5、根据待测标记在实时待测图像中的位置以及该标记对应的物体表面位置信息，计算得到当前实时待测图像所对应的物体表面位置。
具体地，物理量解算参照图6。
进一步作为本方法优选实施例，所述S2、对带标记的物体进行拍摄，并编号图像中的标记，得到带编号标记的图像这一步骤，其具体包括：
S21、对带标记的物体的表面拍摄成像，得到表面图像；
S22、通过阈值分割对表面图像进行处理，检测得到表面图像内的所有标记的目标中心；
具体地，通过阈值对图像进行二值化分割：
Figure PCTCN2021133252-appb-000004
式中，f(x,y)为图像灰度值，x,y为像素坐标，Th为阈值。然后对灰度值为255的像素点进行区域增长，并根据目标的面积确认目标，如果当前像素点周围相连的像素值为255的点个数小于阈值Ts，则当前点不是目标；相反，如果像素值为255的点个数大于阈值Ts，则当前点确认是目标：
Figure PCTCN2021133252-appb-000005
在对确认的目标进行质心定位得到目标中心，质心为各个像素值为255的点的坐标中心：
Figure PCTCN2021133252-appb-000006
其中，n为当前目标中像素值为255的点的个数，(x i,y i)为各点的图像坐标。
S23、以目标中心为参考点，截取预设像素大小的图像作为对应标记的标记特征图像；
具体地，以目标中心为参考点，截取200×200像素大小的图像作为该标记的特征图像，可以通过特征图像来区分不同的标记。
S24、采用主成分分析法建立标记特征图像与标记编号的对应关系，将标记特征图像降至预设的10维，并取主成分矩阵前10个主成分为表示该标记的一维向量，确定唯一编号；
具体地，采用主成分分析法建立标记特征图像与标记编号的对应关系，将200×200像素大小的标记特征图像降维成10个分量表示的一维向量，并以该一维向量作为标记的位移编号。
S25、得到带编号标记的表面图像。
进一步作为本方法优选实施例，所述S24这一步骤，其具体包括：
S241、将200×200的矩阵的每一个行向量(每个变量)都减去该行向量的均值，从而使得新行向量的均值为0，得到新的数据集矩阵X；
S242、求X的协方差矩阵，并求出协方差矩阵的特征值λ和单位特征向量e；
S243、按照特征值从大到小的顺序，将单位特征向量排列成矩阵，得到转换矩阵P，并按PX计算出主成分矩阵；
S244、将特征图像降至特定的10维，直接取主成分矩阵前10个主成分；如果对于表示图像的目标维度无固定要求，可以用特征值计算方差贡献率和方差累计贡献率，取方差累计贡献率最大的前10个主成分，这10个主成分即为表示该标记的一维向量， 以此向量作为标记的特征信息，确定唯一编号。
进一步作为本方法优选实施例，所述根据带编号标记的图像获取物体表面各个标记的相对位置信息并建立标记编号与标记位置信息的对应关系这一步骤，其具体包括：
S31、通过图像拼接方法将带编号标记的图像进行图像拼接，得到完整表面图像；
S32、以完整表面图像为基准图并建立基准图的图像像素坐标与实际物体表面坐标的关系；
S33、在基准图像上进行标记的识别和定位，并根据基准图像像素坐标与实际物体表面坐标的关系，确定各个标记与物体表面坐标的对应关系。
具体地，假设圆台表面局部区域为近似平面，则图像平面点和物体表面局部区域上二维点的关系可表示为：
Figure PCTCN2021133252-appb-000007
其中，a,b,c,d,e,f为描述两个平面之间变形的6个参数，(u,v)为图像像素坐标，(x,y)为圆台表面近似平面上的坐标。变形参数可以通过物体表面控制点(u i,v i)与图像对应点(x i,y i)联立如下方程组计算获取：
Figure PCTCN2021133252-appb-000008
以基准图v方向对应于回转体的转轴高度方向，以基准图的u方向对应于回转体的转角，圆台表面点用其对应的转轴高度和转角表示为(θ,h)，图像坐标(u,v)与圆柱侧表面坐标(θ,h)的对应关系为：
Figure PCTCN2021133252-appb-000009
其中，a,b为比例参数，可以通过相机标定获取，也可以通过物体表面控制点(θ i,h i)与图像对应点(u i,v i)联立得到下列方程组计算获取：
Figure PCTCN2021133252-appb-000010
在基准图像上进行各个标记的识别和定位，并根据基准图像像素坐标与实际物体表面坐标的关系，确定各个标记与物体表面坐标的对应关系。
进一步作为本方法优选实施例，所述获取实时待测图像并检测实时待测图像内的标记，得到待测标记这一步骤，其具体包括：
S41、根据变换公式确定各标记中心点在物体表面的精确位置；
具体地，测量得到各标记中心点对应的圆台表面精确位置，如二维坐标或者三维坐标。 其中，标志中心在圆台坐标系下的二维坐标(x c,y c)和三维坐标(x w,y w,z w)可以通过二维或三维坐标测量仪直接测量得到；也可以使用三维扫描仪，扫描得到标志点及圆台表面其它部位的三维点云，从而得到标志点相对于圆台坐标系的三维坐标(x w,y w,z w)。
采用图像测量技术，可以根据成像参数确定标志中心点对应的圆台表面位置：设标志中心图像坐标为(u,v)，则其与世界坐标(x w,y w,z w)的变换公式如下：
Figure PCTCN2021133252-appb-000011
如上式所示，右边第一个矩阵为相机内参数矩阵，第二个矩阵为相机外参数矩阵。相机的内参矩阵和外参矩阵可以通过张正友标定法等相机标定方法获取。使用上式中的(x w,y w,z w)代表圆台表面与标志中心对应的三维点坐标。s为中心点在摄像机坐标系中的Z坐标，(u,v)为中心图像坐标，(f x,f y)为相机的等效焦距，(c x,c y)为图像主点，r ij(i,j＝1,2,3)为旋转矩阵R元素，t i(i＝1,2,3)为平移向量，(x w,y w,z w)代表物体表面与标记中心对应的三维点坐标。
S42、对物体表面进行实时成像，得到实时待测图像；
S43、对实时待测图像进行标记检测，基于向量比对确认实时待测图像内标记对应的标记编号。
具体地，对圆台表面进行实时成像，对实时图像进行标记检测和定位(检测过程同S3)，确认当前标记对应的标记编号，并计算出实时图像中心在基准图像上的对应坐标。具体步骤如下：
标记检测确认：根据PCA降维准则对当前标记的特征图像进行降维，得到10分量的一维向量，将该向量与基准图中所有标记的一维向量分别进行比对，以基准图中相似度最大的标记编号作为当前标记编号。两个向量{a i}、{b i}的相似度C表示为：
Figure PCTCN2021133252-appb-000012
根据标记定位，确定实时图中心在基准图上的位置：设实时图像上检测出的标记在实时图上的坐标为(u r,v r)，根据标记向量的相似度判断为基准图上的第n号标记，第n号标记(中心)在基准图上的坐标为(x n,y n)，则根据该标记得到实时图中心在基准图像上的坐标(u c,v c)为
Figure PCTCN2021133252-appb-000013
其中，width和height分别为实时图像的像素宽度和高度。
进一步作为本方法优选实施例，所述根据待测标记在实时待测图像中的位置以及该标记对应的物体表面位置信息，计算得到当前实时待测图像所对应的物体表面位置这一步骤，其具体包括这一步骤，其具体包括：
S51、根据标记编号获取实时图像中心在基准图像上的对应坐标。
S52、根据基准图像像素坐标与物体表面参数的对应关系、实时图像中心在基准图像上的坐标，确定实时图像中心对应的物体表面点；
S53、根据实时图像中心对应的物体表面点，得到当前实时待测图像所对应的物体表面位置；
具体地，得到实时图像中心在基准图像上的坐标(u c,v c)后，根据基准图像像素坐标与物体表面参数的对应关系，就可以确定实时图中心对应的物体表面点。
当物体表面为平面时，实时图像中心(u c,v c)对应的物体表面的二维坐标(x c,y c)可以表示为：
Figure PCTCN2021133252-appb-000014
根据物体表面的二维坐标(x c,y c)，可以直接计算实时图中心对应点的位移等参数。
对于圆台，以基准图v方向对应于圆台的转轴高度方向，以基准图的u方向对应于圆台的转动角度，实时图中心(u c,v c)对应的物体表面点(θ c,h c)表示为：
Figure PCTCN2021133252-appb-000015
所述当前实时待测图像所对应的物体表面位置包括一维坐标、二维坐标、三维坐标、一维转角和二维转角。
根据实时图中心(u c,v c)对应的物体表面点(θ c,h c)，可以计算一维角度和二维角度的变化：
图像位置与物体一维转角的对应关系：设圆台周长为L，圆台轴线方向为x方向，圆台周线方向为y方向，设图像中心的像素坐标为(u,v)，其对应的物体表面二维坐标为(x c,y c)，则该点对应的旋转角度为：
Figure PCTCN2021133252-appb-000016
上式中，θ为待测旋转体的旋转角度，y 0为旋转体外表面展开的周线原点，y c为旋转 体外表面展开的标识中心坐标点，L为旋转体外表面展开的周线长。
图像位置与球面二维转角(经纬度)的对应关系：图像中心坐标为(u,v)对应在球面上的世界坐标为(x w,y w,z w)，假设世界坐标系是以球心为原点，则经纬度为：
Figure PCTCN2021133252-appb-000017
上式中，图像中心坐标为(u,v)对应在待测球体表面的世界坐标记为(x w,y w,z w)，α为待测球体的经度，β为待测球体的纬度。
一维角度变化：两次实时成像时图像中心对应的物体一维角度变化计算如下：
Δθ＝θ c1-θ c2＝au c1-au c2
二维角度变化：两次实时成像时图像中心对应的球体表面二维角度变化(假设原点在球心，且球半径为r)计算如下：
Figure PCTCN2021133252-appb-000018
本发明具有如下优势：
a)相对于光栅技术和成像式编码器技术依据刻度和标识的理论位置进行测量，本发明的测量依据是待测物体表面标记的真实位置。本发明通过对制作完成后的随机纹理信息和标记进行测量，高精度得到各标记相对于待测物体的实际位置，再根据标记的实际位置(而不是理论位置)来进行物体表面的定位和测量，从而避免了标记的加工误差、安装误差等因素对测量精度的影响。这样测量不仅精度高、可靠性好，而且大幅降低了对加工工艺、器件材质的要求。
b)相对于信息环识别的技术，本发明可以直接在物体表明制作随机散斑信息和标记(喷涂、粘贴)，无需制作和安装信息环，无需高精度加工和制作编码标志，方法更为简单经济。
c)相对于信息环只能针对圆柱体测量，通过测量圆柱侧面一维位移进行转角测量。本发明可以直接应用于圆台(侧面与上下底面均可)、圆锥、圆盘以及任意不规则回转体的表面位移测量与转角测量。
d)相对于信息环识别和双像机的测量技术，其采用的遍历搜索式图像定位方法效率低下，很难用于实时快速测量。本发明提出了直接检测和识别标识以进行图像定位的方法，无需在图像定位的时候对基准图像的所有像素进行遍历搜索，具有执行效率 高、实时性好、简单可靠的优点。
e)相对于成像式编码器和信息环标识采用的单一标识编码测量方式，本发明采用无编码的简单图案(如点、线、十字丝等)，也可以兼容编码标识，这样不仅使标识的加工制作更为简单方便，彻底摆脱了高精度加工要求，还可以使像机成像的范围更小，从而获得更高测量精度。
f)相对于双像机的测量方案，本发明只需要采用单像机，即可实现对物体表面标记点的识别定位，使得整个测量过程更为简单高效。
g)相对于现有的只能进行一维转角和一维位移高精度测量的技术，本发明在此基础上还可以用于二维平移、三维平移、二维转角等参数的测量。
如图2所示，一种物体表面高精度定位系统，包括：
标记制作模块，用于在物体表面制作预设分布密度的标记，得到带标记的物体；
编号模块，用于对带标记的物体进行拍摄，并编号图像中的标记，得到带编号标记的图像；
标记位置关系模块，用于根据带编号标记的图像获取物体表面各个标记的相对位置信息并建立标记编号与标记位置信息的对应关系；
待测图像模块，用于获取实时待测图像并检测实时待测图像内的标记，得到待测标记；
物理量解算模块，用于根据待测标记在实时待测图像中的位置以及该标记对应的物体表面位置信息，计算得到当前实时待测图像所对应的物体表面位置。
上述方法实施例中的内容均适用于本系统实施例中，本系统实施例所具体实现的功能与上述方法实施例相同，并且达到的有益效果与上述方法实施例所达到的有益效果也相同。
以上是对本发明的较佳实施进行了具体说明，但本发明创造并不限于所述实施例，熟悉本领域的技术人员在不违背本发明精神的前提下还可做作出种种的等同变形或替换，这些等同的变形或替换均包含在本申请权利要求所限定的范围内。

Claims (10)

  1. 一种物体表面高精度定位方法，其特征在于，包括以下步骤：
    在物体表面制作预设分布密度的标记，得到带标记的物体；
    对带标记的物体进行拍摄，并编号图像中的标记，得到带编号标记的图像；
    根据带编号标记的图像获取物体表面各个标记的相对位置信息并建立标记编号与标记位置信息的对应关系；
    获取实时待测图像并检测实时待测图像内的标记，得到待测标记；
    根据待测标记在实时待测图像中的位置以及该标记对应的物体表面位置信息，计算得到当前实时待测图像所对应的物体表面位置。
  2. 根据权利要求1所述一种物体表面高精度定位方法，其特征在于，所述在物体表面制作预设分布密度的标记，得到带标记的物体这一步骤，其具体包括：
    采用预设样式的标记，通过标记制作方法表面刻制标记，得到带标记的物体；
    所述预设样式的标记包括线段、点、圆、方形、十字及其组合形状；
    所述标记制作方法包括激光雕刻、印刷和刻蚀；
    所述标记的刻制间距小于图像画幅的一半。
  3. 根据权利要求2所述一种物体表面高精度定位方法，其特征在于，所述对带标记的物体进行拍摄，并编号图像中的标记，得到带编号标记的图像这一步骤，其具体包括：
    对带标记的物体的表面拍摄成像，得到表面图像；
    通过阈值分割对表面图像进行处理，检测得到表面图像内的所有标记的目标中心；
    以目标中心为参考点，截取预设像素大小的图像作为对应标记的标记特征图像；
    采用主成分分析法建立标记特征图像与标记编号的对应关系，将标记特征图像降至低维，并取主成分矩阵前10个主成分为表示该标记的一维向量，确定唯一编号；
    得到带编号标记的表面图像。
  4. 根据权利要求3所述一种物体表面高精度定位方法，其特征在于，所述根据带编号标记的图像获取物体表面各个标记的相对位置信息并建立标记编号与标记位置信息的对应关系这一步骤，其具体包括：
    通过图像拼接方法将带编号标记的图像进行图像拼接，得到完整表面图像；
    以完整表面图像为基准图并建立基准图的图像像素坐标与实际物体表面坐标的关系；
    在基准图像上进行标记的识别和定位，并根据基准图像像素坐标与实际物体表面坐标的关系，确定各个标记与物体表面坐标的对应关系。
  5. 根据权利要求4所述一种物体表面高精度定位方法，其特征在于，所述获取实时待测图像并检测实时待测图像内的标记，得到待测标记这一步骤，其具体包括：
    根据变换公式确定各标记中心点在物体表面的精确位置；
    对物体表面进行实时成像，得到实时待测图像；
    对实时待测图像进行标记检测，基于向量比对确认实时待测图像内标记对应的标记编号。
  6. 根据权利要求5所述一种物体表面高精度定位方法，其特征在于，所述变换公式如下式：
    Figure PCTCN2021133252-appb-100001
    上式中，s为中心点在摄像机坐标系中的Z坐标，(u,v)为中心图像坐标，(f x,f y)为相机的等效焦距，(c x,c y)为图像主点，r ij(i,j＝1,2,3)为旋转矩阵R元素，t i(i＝1,2,3)为平移向量，(x w,y w,z w)代表物体表面与标记中心对应的三维点坐标。
  7. 根据权利要求6所述一种物体表面高精度定位方法，其特征在于，所述根据待测标记在实时待测图像中的位置以及该标记对应的物体表面位置信息，计算得到当前实时待测图像所对应的物体表面位置这一步骤，其具体包括：
    根据标记编号获取实时图像中心在基准图像上的对应坐标；
    根据基准图像像素坐标与物体表面参数的对应关系、实时图像中心在基准图像上的坐标，确定实时图像中心对应的物体表面点；
    根据实时图像中心对应的物体表面点，得到当前实时待测图像所对应的物体表面位置；
    所述当前实时待测图像所对应的物体表面位置包括一维坐标、二维坐标、三维坐标、一维转角和二维转角。
  8. 根据权利要求7所述一种物体表面高精度定位方法，其特征在于，所述一维转角的计算公式如下：
    Figure PCTCN2021133252-appb-100002
    上式中，θ为待测旋转体的旋转角度，y 0为旋转体外表面展开的周线原点，y c为旋转体外表面展开的标识中心坐标点，L为旋转体外表面展开的周线长。
  9. 根据权利要求8所述一种物体表面高精度定位方法，其特征在于，所述二维转角的计算 公式如下：
    Figure PCTCN2021133252-appb-100003
    上式中，图像中心坐标为(u,v)对应在待测球体表面的世界坐标记为(x w,y w,z w)，α为待测球体的经度，β为待测球体的纬度。
  10. 一种物体表面高精度定位系统，其特征在于，包括：
    标记制作模块，用于在物体表面制作预设分布密度的标记，得到带标记的物体；
    编号模块，用于对带标记的物体进行拍摄，并编号图像中的标记，得到带编号标记的图像；
    标记位置关系模块，用于根据带编号标记的图像获取物体表面各个标记的相对位置信息并建立标记编号与标记位置信息的对应关系；
    待测图像模块，用于获取实时待测图像并检测实时待测图像内的标记，得到待测标记；
    物理量解算模块，用于根据待测标记在实时待测图像中的位置以及该标记对应的物体表面位置信息，计算得到当前实时待测图像所对应的物体表面位置。
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