WO2025213629A1 - 一种基于辅助相机的线阵相机标定方法及相关装置 - Google Patents

一种基于辅助相机的线阵相机标定方法及相关装置

Info

Publication number
WO2025213629A1
WO2025213629A1 PCT/CN2024/108113 CN2024108113W WO2025213629A1 WO 2025213629 A1 WO2025213629 A1 WO 2025213629A1 CN 2024108113 W CN2024108113 W CN 2024108113W WO 2025213629 A1 WO2025213629 A1 WO 2025213629A1
Authority
WO
WIPO (PCT)
Prior art keywords
camera
auxiliary
auxiliary camera
coordinate system
linear array
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
PCT/CN2024/108113
Other languages
English (en)
French (fr)
Inventor
高健
郑震宇
郑卓鋆
张揽宇
罗于恒
陈新
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Guangdong University of Technology
Original Assignee
Guangdong University of Technology
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Guangdong University of Technology filed Critical Guangdong University of Technology
Priority to US19/002,197 priority Critical patent/US12347144B1/en
Publication of WO2025213629A1 publication Critical patent/WO2025213629A1/zh
Pending legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Classifications

    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/80—Analysis of captured images to determine intrinsic or extrinsic camera parameters, i.e. camera calibration
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/13—Edge detection
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/30—Determination of transform parameters for the alignment of images, i.e. image registration
    • G06T7/33—Determination of transform parameters for the alignment of images, i.e. image registration using feature-based methods
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20112—Image segmentation details
    • G06T2207/20164—Salient point detection; Corner detection
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/10—Internal combustion engine [ICE] based vehicles
    • Y02T10/40—Engine management systems

Definitions

  • the present application relates to the field of camera calibration technology, and in particular to a linear array camera calibration method based on an auxiliary camera and related devices.
  • camera calibration is a key step that aims to determine the camera's internal parameters (such as focal length, optical center position, etc.) and external parameters (such as rotation matrix and translation vector) to achieve high-precision three-dimensional measurement and positioning.
  • internal parameters such as focal length, optical center position, etc.
  • external parameters such as rotation matrix and translation vector
  • the calibration process of linear scan cameras has certain particularities due to their structural characteristics.
  • Existing linear scan camera calibration methods are usually based on one-dimensional calibration objects, and the camera parameters are estimated by acquiring images at different angles. However, these methods are often affected by factors such as the accuracy of the calibration object production and image acquisition conditions, making it difficult to achieve high-precision calibration results.
  • the present application provides a linear array camera calibration method and related devices based on an auxiliary camera, which are used to improve the calibration accuracy of the linear array camera.
  • a first aspect of the present application provides a linear array camera calibration method based on an auxiliary camera, comprising:
  • auxiliary camera Calibrate the auxiliary camera by rigidly combining the calibrated auxiliary camera with the line array camera to be calibrated and mounting them on a motion platform. Place a checkerboard calibration plate within the imaging area of the combined camera.
  • the motion platform is driven to obtain the calibration plate images taken by the auxiliary camera at the initial and end positions and the dynamic scanning images of the linear array camera, and the motion vector of the combined camera in the auxiliary camera coordinate system is calculated;
  • the intrinsic parameters of the linear scan camera and the rigid transformation matrix between the auxiliary camera and the linear scan camera are calculated.
  • the step of acquiring the calibration plate image captured by the auxiliary camera at the initial position and the end position and the dynamic scanning image of the line array camera by driving the motion platform, and calculating the motion vector of the combined camera in the auxiliary camera coordinate system includes:
  • the N-point perspective monocular vision positioning method is used to calculate the extrinsic parameters of the auxiliary camera at the initial position and the final position;
  • the motion vector of the combined camera in the auxiliary camera coordinate system is calculated according to the extrinsic parameters of the auxiliary camera at the initial position, the extrinsic parameters of the end position, and the target number of rows.
  • calculating the coordinates of the registration point in the auxiliary camera coordinate system using the motion vector and the dynamic scan image includes:
  • the coordinates of the registration point in the auxiliary camera coordinate system are calculated using the motion vector, the sub-pixel coordinates of the registration point in the dynamic scanning image, and the sub-pixel coordinates of the checkerboard corner points in the dynamic scanning image.
  • the intrinsic parameters of the line scan camera and the rigid transformation matrix between the auxiliary camera and the line scan camera are calculated based on the properties of the rotation matrix and the line scan camera imaging model, including:
  • the coordinates of the registration points in the auxiliary camera coordinate system are transformed to the linear array camera coordinate system, and then the coordinates of the registration points in the linear array camera coordinate system are transformed to the pixel coordinate system to obtain the intermediate relationship.
  • the unit normal vector of the imaging plane of the linear array camera is obtained according to the equation of the imaging plane of the linear array camera in the auxiliary camera coordinate system, and the intrinsic parameters of the linear array camera and the rigid transformation matrix between the auxiliary camera and the linear array camera are obtained based on the relationship between the unit normal vector and the unknown parameters.
  • the method further includes:
  • the intrinsic parameters of the line array camera and the rigid transformation matrix between the auxiliary camera and the line array camera are corrected by the parameter correction amount to obtain the optimized intrinsic parameters of the line array camera and the optimized rigid transformation matrix.
  • a second aspect of the present application provides a linear array camera calibration device based on an auxiliary camera, comprising:
  • the combination unit is used to calibrate the auxiliary camera, rigidly combine the calibrated auxiliary camera and the line array camera to be calibrated and install them on the motion platform, and place the checkerboard calibration plate in the imaging area of the combined camera;
  • a first calculation unit is used to obtain the calibration plate image taken by the auxiliary camera at the initial position and the end position and the dynamic scanning image of the line array camera by driving the motion platform, and calculate the motion vector of the combined camera in the auxiliary camera coordinate system;
  • a second calculation unit configured to calculate the coordinates of the registration point in the auxiliary camera coordinate system using the motion vector and the dynamic scan image
  • a fitting unit configured to fit the equation of the imaging plane of the linear array camera in the auxiliary camera coordinate system through the coordinates of the plurality of registration points in the auxiliary camera coordinate system;
  • the third calculation unit is used to calculate the intrinsic parameters of the line scan camera and the rigid transformation matrix between the auxiliary camera and the line scan camera based on the properties of the rotation matrix and the line scan camera imaging model.
  • the first computing unit is specifically configured to:
  • the N-point perspective monocular vision positioning method is used to calculate the extrinsic parameters of the auxiliary camera at the initial position and the final position;
  • the motion vector of the combined camera in the auxiliary camera coordinate system is calculated according to the extrinsic parameters of the auxiliary camera at the initial position, the extrinsic parameters of the end position, and the target number of rows.
  • the second computing unit is specifically configured to:
  • the coordinates of the registration point in the auxiliary camera coordinate system are calculated using the motion vector, the sub-pixel coordinates of the registration point in the dynamic scanning image, and the sub-pixel coordinates of the checkerboard corner points in the dynamic scanning image.
  • a third aspect of the present application provides an electronic device, the device comprising a processor and a memory;
  • the memory is used to store program code and transmit the program code to the processor
  • the processor is configured to execute the auxiliary camera-based line array camera calibration method according to any one of the first aspects according to the instructions in the program code.
  • the present application provides a computer-readable storage medium for storing program code.
  • the program code is executed by a processor, the auxiliary camera-based linear array camera calibration method according to any one of the first aspects is implemented.
  • the present application provides a linear array camera calibration method based on an auxiliary camera, comprising: calibrating the auxiliary camera, rigidly combining the calibrated auxiliary camera and the linear array camera to be calibrated and mounting them on a motion platform, and placing a checkerboard calibration plate in the imaging area of the combined camera; acquiring the calibration plate images taken by the auxiliary camera at the initial position and the end position and the dynamic scanning image of the linear array camera by driving the motion platform, and calculating the motion vector of the combined camera in the auxiliary camera coordinate system; calculating the coordinates of the registration points in the auxiliary camera coordinate system based on the motion vector and the dynamic scanning image; fitting the equation of the linear array camera imaging plane in the auxiliary camera coordinate system based on the coordinates of multiple registration points in the auxiliary camera coordinate system; and calculating the intrinsic parameters of the linear array camera and the rigid transformation matrix between the auxiliary camera and the linear array camera based on the properties of the rotation matrix and the linear array camera imaging model.
  • a calibrated auxiliary camera is used to rigidly combine with the line array camera to be calibrated, and high-precision calibration is performed on a motion platform.
  • the line array camera parameters are calibrated based on the auxiliary camera, which not only reduces the number of images required for calibration but also improves the calibration accuracy.
  • FIG1 is a flow chart of a method for calibrating a linear array camera based on an auxiliary camera according to an embodiment of the present application
  • FIG2 is a schematic diagram of obtaining registration point coordinates provided in an embodiment of the present application.
  • FIG3 is a diagram of a linear array camera calibration device based on an auxiliary camera provided in an embodiment of the present application. A structural diagram.
  • An embodiment of the present application provides a linear array camera calibration method based on an auxiliary camera, including:
  • Step 101 calibrate the auxiliary camera.
  • the calibrated auxiliary camera and the line array camera to be calibrated are rigidly combined and mounted on a motion platform.
  • a checkerboard calibration plate is placed in the imaging area of the combined camera.
  • An area array camera can be used as an auxiliary camera, and then the auxiliary camera can be calibrated using Zhang Zhengyou's calibration method to obtain the intrinsic parameters P f of the auxiliary camera.
  • the calibrated auxiliary camera is rigidly connected to the line scan camera to be calibrated to obtain a combined camera.
  • the combined camera is then mounted on a single-axis high-precision linear motion platform.
  • a checkerboard calibration plate is then placed within the imaging area of the combined camera so that both the auxiliary camera and the line scan camera can capture the checkerboard calibration plate.
  • Step 102 Acquire the calibration plate images taken by the auxiliary camera at the initial position and the end position and the dynamic scanning images of the line array camera by driving the motion platform, and calculate the motion vector of the combined camera in the auxiliary camera coordinate system.
  • the N-point perspective monocular vision positioning method is used to calculate the extrinsic parameters of the auxiliary camera at the initial position and the end position; calculate the motion vector of the combined camera in the auxiliary camera coordinate system based on the extrinsic parameters of the auxiliary camera at the initial position, the end position and the target number of rows.
  • the auxiliary camera After placing the checkerboard calibration plate, the auxiliary camera captures the checkerboard image to obtain the checkerboard image at the initial position.
  • the motion platform is then driven to move the combined camera along a straight line for a certain distance, so that the line array camera captures a dynamic scanning image of the active rows n, and the auxiliary camera captures the checkerboard image at the end of the motion.
  • the extrinsic parameters Rf (0) and Tf (0) of the auxiliary camera at the initial position and the extrinsic parameters Rf (n) and Tf (n) of the end position can be obtained.
  • the motion vector Vf of the combined camera in the auxiliary camera coordinate system is obtained by the following formula (unit: mm/pixel):
  • VfX , VfY , and Vfz are the movement velocities of the combined camera in the X, Y, and Z directions in the auxiliary camera coordinate system, respectively.
  • Step 103 Calculate the coordinates of the registration point in the auxiliary camera coordinate system using the motion vector and the dynamic scanning image.
  • the coordinates of the registration point in the auxiliary camera coordinate system are calculated using the motion vector, the sub-pixel coordinates of the registration point in the dynamic scanning image, and the sub-pixel coordinates of the checkerboard corner points in the dynamic scanning image. Specifically, first, the sub-pixel corner coordinates of the dynamic scanning image of the line scan camera are extracted to obtain the sub-pixel coordinates of multiple corner points 0, 1, 2, ... [u P0 ,v P0 ], [u P1 ,v P1 ], [u P2 ,v P2 ] ... (as shown in Figure 2). Then, using the perspective-free and distortion-free characteristics of the line scan camera scanning direction, the relationship between the corner point coordinates and the registration point coordinates can be obtained:
  • XWP5 is the X-coordinate of corner point 5 in the world coordinate system
  • XWP6 is the X-coordinate of corner point 6 in the world coordinate system
  • XWIS3 is the X-coordinate of registration point 3 in the world coordinate system.
  • the registration point is the intersection of the imaging plane of the linear array camera and the grid lines of the checkerboard calibration plate. The registration point can be used to establish the correspondence between different coordinate systems, thereby realizing the conversion or transformation of the coordinate system.
  • the world coordinates ( XW , YW , ZW) of the registration point with y-coordinate v in the pixel coordinate system can be transformed into the coordinates in the auxiliary camera coordinate system ( Xf (v) , Yf (v ), Zf ( v )) using formula (3) :
  • the coordinates of the registration points in the auxiliary camera coordinate system can be calculated.
  • Step 104 Fitting the equation of the imaging plane of the linear array camera in the auxiliary camera coordinate system through the coordinates of the multiple registration points in the auxiliary camera coordinate system.
  • a fl , B fl , C fl , and D fl are coefficients of the imaging plane equation.
  • Step 105 Calculate the intrinsic parameters of the line scan camera and the rigid transformation matrix between the auxiliary camera and the line scan camera based on the properties of the rotation matrix and the line scan camera imaging model.
  • the coordinates of the registration points in the auxiliary camera coordinate system are transformed to the linear array camera coordinate system, and then the coordinates of the registration points in the linear array camera coordinate system are transformed to the pixel coordinate system to obtain the intermediate relationship.
  • the unit normal vector of the imaging plane of the linear scan camera is obtained according to the equation of the imaging plane of the linear scan camera in the auxiliary camera coordinate system.
  • the intrinsic parameters of the linear scan camera and the rigid transformation matrix between the auxiliary camera and the linear scan camera are obtained based on the relationship between the unit normal vector and the unknown parameters.
  • the points in the auxiliary camera coordinate system can be transformed into the line scan camera coordinate system by using formula (5). Without considering lens distortion, the points in the line scan camera coordinate system can be transformed into the pixel coordinate system by using the line scan camera imaging model (formula (6)).
  • R fl represents the rotation matrix in the rigid transformation
  • T fl represents the translation vector in the rigid transformation
  • f x represents the ratio of the image distance to the pixel size in the x direction
  • f y represents the ratio of the image distance to the pixel size in the y direction.
  • u 0 represents the principal point position in the x-direction, and the principal point is the point where the imaging plane intersects the optical axis perpendicularly.
  • r is the element in the rotation matrix R fl , t x , t y , and t z are the translation distances in the x, y, and z directions of the translation vector T fl respectively;
  • Formula (8) describes the ideal linear array camera imaging model. However, for strict geometric calibration, after solving the linear coefficients in the model, it is necessary to further calculate the geometric imaging model parameters with physical meaning from these linear coefficients.
  • the equation of the imaging plane of the line scan camera in the auxiliary camera coordinate system has been obtained, and the unit normal vector (a fl , b fl , c fl ) of the imaging plane of the line scan camera can be obtained as follows:
  • r 21 , r 22 , and r 23 can be calculated based on the properties of the rotation matrix. Since both the auxiliary camera coordinate system and the linear array camera coordinate system are right-handed coordinate systems and there is no reflection, the determinant of the rotation matrix should be +1. If the determinant of the calculated rotation matrix R fl satisfies the following inequality:
  • A A fl
  • B B fl
  • C C fl
  • D D fl .
  • step 105 the following steps may be further included:
  • Step 106 Optimize the intrinsic parameters of the line scan camera and the rigid transformation matrix between the auxiliary camera and the line scan camera.
  • the camera parameters obtained above are not optimal.
  • the main reason is that the distortion of the linear array camera lens is not considered in the above solution process.
  • k 1 and k 2 represent the radial distortion coefficients of the lens
  • p 1 represents the tangential distortion coefficient of the lens, which can more accurately model the radial and tangential distortion of the lens.
  • a camera parameter optimization model is established; by solving the camera parameter optimization model, the parameter correction value of the linear array camera is obtained; the intrinsic parameters of the linear array camera and the rigid transformation matrix between the auxiliary camera and the linear array camera are corrected by the parameter correction value, and the optimized intrinsic parameters and the optimized rigid transformation matrix of the linear array camera are obtained.
  • the rotation components to satisfy four conditions: the counterclockwise rotation direction is the positive rotation direction, the rotation axis characteristic is rotation around a fixed axis, the angle system is ⁇ - ⁇ - ⁇ , and the rotation component value range is - ⁇ /2 ⁇ /2, - ⁇ , - ⁇ . Furthermore, it is known that the world coordinate system, the auxiliary camera coordinate system, and the linear array camera coordinate system are all right-handed coordinate systems, so the rotation matrix R fl in the rigid transformation can be uniquely decomposed into a set of rotation components:
  • the least squares solution is usually regarded as the best output result, so the optimal parameter value can be obtained by minimizing the following function:
  • u i is the X coordinate of the sub-pixel point i detected in the real scanned image
  • N is the number of reprojection points.
  • the reprojection point coordinates can be derived
  • the formula is:
  • the embodiment of the present application proposes a linear array camera calibration method based on an auxiliary camera.
  • the method uses an area array camera as an auxiliary device and performs high-precision calibration on a moving platform by rigidly combining the linear array cameras to be calibrated.
  • the method not only improves the accuracy and reliability of the calibration, but also is easy to operate and low-cost, and can be widely used in the field of industrial measurement.
  • this method calibrates the linear array camera parameters based on an auxiliary camera, which not only reduces the number of images required for calibration, but also improves the calibration accuracy.
  • this method does not require the use of a calibration plate and a customized irregular image plate at the same time.
  • this method uses a dynamic scanning method for calibration, which can obtain a large number of alignment points, thereby improving the calibration accuracy.
  • this application takes into account the distortion of the linear array camera lens. Based on the linear array camera lens distortion model and the least squares optimization principle, a camera parameter optimization model is established. By optimizing the camera parameter optimization model, the linear array camera optimization parameters are obtained, thereby further optimizing the calibration accuracy of the linear array camera.
  • the above is an embodiment of a linear array camera calibration method based on an auxiliary camera provided by the present application.
  • the following is an embodiment of a linear array camera calibration device based on an auxiliary camera provided by the present application.
  • an embodiment of the present application provides a linear array camera calibration device based on an auxiliary camera, including:
  • Combination unit used to calibrate the auxiliary camera, and combine the calibrated auxiliary camera with the one to be calibrated
  • the linear array cameras are rigidly assembled and mounted on a motion platform, and a checkerboard calibration plate is placed within the imaging area of the assembled cameras;
  • a first calculation unit is used to obtain the calibration plate image taken by the auxiliary camera at the initial position and the end position and the dynamic scanning image of the line array camera by driving the motion platform, and calculate the motion vector of the combined camera in the auxiliary camera coordinate system;
  • a second calculation unit is used to calculate the coordinates of the registration point in the auxiliary camera coordinate system through the motion vector and the dynamic scanning image;
  • a fitting unit configured to fit the equation of the imaging plane of the linear array camera in the auxiliary camera coordinate system through the coordinates of the plurality of registration points in the auxiliary camera coordinate system;
  • the third calculation unit is used to calculate the intrinsic parameters of the line scan camera and the rigid transformation matrix between the auxiliary camera and the line scan camera based on the properties of the rotation matrix and the line scan camera imaging model.
  • the first computing unit is specifically configured to:
  • the N-point perspective monocular vision positioning method is used to calculate the extrinsic parameters of the auxiliary camera at the initial position and the final position;
  • the motion vector of the combined camera in the auxiliary camera coordinate system is calculated according to the extrinsic parameters of the auxiliary camera at the initial position, the extrinsic parameters of the end position and the target row number.
  • the second computing unit is specifically configured to:
  • the coordinates of the registration point in the auxiliary camera coordinate system are calculated using the motion vector, the sub-pixel coordinates of the registration point in the dynamic scanning image, and the sub-pixel coordinates of the checkerboard corner points in the dynamic scanning image.
  • the third computing unit is specifically configured to:
  • the coordinates of the registration points in the auxiliary camera coordinate system are transformed to the linear array camera coordinate system, and then the coordinates of the registration points in the linear array camera coordinate system are transformed to the pixel coordinate system to obtain the intermediate relationship.
  • the device further includes: an optimization unit, configured to:
  • the intrinsic parameters of the line scan camera and the rigid transformation matrix between the auxiliary camera and the line scan camera are corrected by the parameter correction amount, and the optimized intrinsic parameters and the optimized rigid transformation matrix of the line scan camera are obtained.
  • a high-precision calibration is performed on a moving platform by rigidly combining the line array cameras to be calibrated.
  • This method not only improves the accuracy and reliability of the calibration, but is also easy to operate and low-cost, and can be widely used in the field of industrial measurement.
  • this method calibrates the parameters of the line array camera based on an auxiliary camera, which not only reduces the number of images required for calibration, but also improves the calibration accuracy.
  • this method does not require the use of a calibration plate and a customized irregular image plate at the same time.
  • this method uses a dynamic scanning method for calibration, which can obtain a large number of alignment points, thereby improving the calibration accuracy.
  • this application takes into account the distortion of the linear array camera lens. Based on the linear array camera lens distortion model and the least squares optimization principle, a camera parameter optimization model is established. By optimizing the camera parameter optimization model, the optimized parameters of the linear array camera are obtained, thereby further optimizing the calibration accuracy of the linear array camera.
  • An embodiment of the present application further provides an electronic device, the device including a processor and a memory;
  • the memory is used to store program codes and transmit the program codes to the processor
  • the processor is configured to execute the auxiliary camera-based line array camera calibration method in the aforementioned method embodiment according to instructions in the program code.
  • An embodiment of the present application further provides a computer-readable storage medium for storing program code, and the program code is executed by a processor to implement the line array camera calibration method based on an auxiliary camera in the aforementioned method embodiment.
  • At least one (item) means one or more, and “plurality” means two or more.
  • “And/or” is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, “A and/or B” can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character “/” generally indicates that the previous and next associated objects are in an “or” relationship. "At least one of the following items” or similar expressions refers to any combination of these items, including any combination of single items or plural items.
  • At least one of a, b or c can mean: a, b, c, "a and b", “a and c", “b and c", or "a and b and c", where a, b, c can be single or multiple.
  • the disclosed devices and methods can be implemented in other ways.
  • the device embodiments described above are merely schematic.
  • the division of the units is merely a logical function division.
  • Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.
  • the units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.
  • each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.
  • the above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional unit.
  • the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium.
  • the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product.
  • the computer software product is stored in a storage medium, including a number of instructions for executing all or part of the steps of the method described in each embodiment of the present application through a computer device (which can be a personal computer, server, or network device, etc.).
  • the aforementioned storage media include: U disk, mobile hard disk, read-only memory (English full name: Read-Only Memory, English abbreviation: ROM), random access memory (English full name: Random Access Memory, English abbreviation: RAM), magnetic disk or optical disk and other media that can store program code.

Landscapes

  • Engineering & Computer Science (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Studio Devices (AREA)
  • Testing, Inspecting, Measuring Of Stereoscopic Televisions And Televisions (AREA)
  • Length Measuring Devices By Optical Means (AREA)

Abstract

本申请公开了一种基于辅助相机的线阵相机标定方法及相关装置，方法包括：对辅助相机进行标定，将已标定的辅助相机与待标定的线阵相机刚性组合并安装到运动平台上，将棋盘格标定板放置在组合相机的成像区域内；通过驱动运动平台获取辅助相机在初始位置、结束位置拍摄的标定板图像和线阵相机的动态扫描图像，并计算组合相机在辅助相机坐标系下的运动向量；通过运动向量和动态扫描图像计算配准点在辅助相机坐标系下的坐标；通过多个配准点在辅助相机坐标系下的坐标拟合线阵相机成像平面在辅助相机坐标系下的方程式；基于旋转矩阵的性质和线阵相机成像模型计算线阵相机的内参数以及辅助相机与线阵相机之间的刚性变换矩阵，提高了线阵相机标定精度。

Description

一种基于辅助相机的线阵相机标定方法及相关装置 技术领域
本申请涉及相机标定技术领域，尤其涉及一种基于辅助相机的线阵相机标定方法及相关装置。
背景技术
在光学测量领域中，相机标定是一个关键步骤，旨在确定相机的内部参数(如焦距、光心位置等)以及外部参数(如旋转矩阵和平移向量)，以实现高精度的三维测量和定位。线阵相机作为一种常见的工业相机，由于其结构特点，其标定过程具有一定的特殊性。现有的线阵相机标定方法通常基于一维标定物，通过获取不同角度下的图像来估计相机的参数。然而，这些方法往往受到标定物制作精度、图像采集条件等因素的影响，难以达到高精度的标定结果。
发明内容
本申请提供了一种基于辅助相机的线阵相机标定方法及相关装置，用于提高线阵相机的标定精度。
有鉴于此，本申请第一方面提供了一种基于辅助相机的线阵相机标定方法，包括：
对辅助相机进行标定，将已标定的辅助相机与待标定的线阵相机刚性组合并安装到运动平台上，将棋盘格标定板放置在组合相机的成像区域内；
通过驱动运动平台获取辅助相机在初始位置、结束位置拍摄的标定板图像和线阵相机的动态扫描图像，并计算组合相机在辅助相机坐标系下的运动向量；
通过所述运动向量和所述动态扫描图像计算配准点在辅助相机坐标系下的坐标；
通过多个配准点在辅助相机坐标系下的坐标拟合线阵相机成像平面在辅助相机坐标系下的方程式；
基于旋转矩阵的性质和线阵相机成像模型计算线阵相机的内参数以及辅助相机与线阵相机之间的刚性变换矩阵。
可选的，所述通过驱动运动平台获取辅助相机在初始位置、结束位置拍摄的标定板图像和线阵相机的动态扫描图像，并计算组合相机在辅助相机坐标系下的运动向量，包括：
获取辅助相机在初始位置拍摄的标定板图像，驱动运动平台使得组合相机沿直线运动预置距离，使得线阵相机获取目标行数的动态扫描图像，并获取辅助相机在运动的结束位置的标定板图像；
基于辅助相机在初始位置、结束位置拍摄的标定板图像，采用N点透视单目视觉定位方法计算辅助相机在初始位置的外参数以及结束位置的外参数；
根据辅助相机在初始位置的外参数、结束位置的外参数以及所述目标行数计算组合相机在辅助相机坐标系下的运动向量。
可选的，所述通过所述运动向量和所述动态扫描图像计算配准点在辅助相机坐标系下的坐标，包括：
通过所述运动向量、配准点在动态扫描图像中的亚像素坐标以及动态扫描图像中棋盘格角点的亚像素坐标计算配准点在辅助相机坐标系下的坐标。
可选的，基于旋转矩阵的性质和线阵相机成像模型计算线阵相机的内参数以及辅助相机与线阵相机之间的刚性变换矩阵，包括：
利用刚性变换中旋转矩阵的性质和线阵相机成像模型将辅助相机坐标系下的配准点的坐标变换到线阵相机坐标系下，再将线阵相机坐标系下的配准点的坐标变换到像素坐标系下，得到中间关系式；
从所述中间关系式中分离已知参数，得到未知参数关系式；
根据线阵相机成像平面在辅助相机坐标系下的方程式获取线阵相机成像平面的单位法向量，基于所述单位法向量和所述未知参数关系式求解得到线阵相机的内参数以及辅助相机与线阵相机之间的刚性变换矩阵。
可选的，所述方法还包括：
根据线阵相机的镜头畸变模型和最小二乘优化原理，建立相机参数优化模型；
通过求解所述相机参数优化模型，得到线阵相机的参数修正量；
通过所述参数修正量修正线阵相机的内参数以及辅助相机与线阵相机之间的刚性变换矩阵，得到线阵相机的优化后内参数以及优化后刚性变换矩阵。
本申请第二方面提供了一种基于辅助相机的线阵相机标定装置，包括：
组合单元，用于对辅助相机进行标定，将已标定的辅助相机与待标定的线阵相机刚性组合并安装到运动平台上，将棋盘格标定板放置在组合相机的成像区域内；
第一计算单元，用于通过驱动运动平台获取辅助相机在初始位置、结束位置拍摄的标定板图像和线阵相机的动态扫描图像，并计算组合相机在辅助相机坐标系下的运动向量；
第二计算单元，用于通过所述运动向量和所述动态扫描图像计算配准点在辅助相机坐标系下的坐标；
拟合单元，用于通过多个配准点在辅助相机坐标系下的坐标拟合线阵相机成像平面在辅助相机坐标系下的方程式；
第三计算单元，用于基于旋转矩阵的性质和线阵相机成像模型计算线阵相机的内参数以及辅助相机与线阵相机之间的刚性变换矩阵。
可选的，所述第一计算单元，具体用于：
获取辅助相机在初始位置拍摄的标定板图像，驱动运动平台使得组合相机沿直线运动预置距离，使得线阵相机获取目标行数的动态扫描图像，并获取辅助相机在运动的结束位置的标定板图像；
基于辅助相机在初始位置、结束位置拍摄的标定板图像，采用N点透视单目视觉定位方法计算辅助相机在初始位置的外参数以及结束位置的外参数；
根据辅助相机在初始位置的外参数、结束位置的外参数以及所述目标行数计算组合相机在辅助相机坐标系下的运动向量。
可选的，所述第二计算单元，具体用于：
通过所述运动向量、配准点在动态扫描图像中的亚像素坐标以及动态扫描图像中棋盘格角点的亚像素坐标计算配准点在辅助相机坐标系下的坐标。
本申请第三方面提供了一种电子设备，所述设备包括处理器以及存储器；
所述存储器用于存储程序代码，并将所述程序代码传输给所述处理器；
所述处理器用于根据所述程序代码中的指令执行第一方面任一种所述的基于辅助相机的线阵相机标定方法。
本申请第四方面提供了一种计算机可读存储介质，所述计算机可读存储介质用于存储程序代码，所述程序代码被处理器执行时实现第一方面任一种所述的基于辅助相机的线阵相机标定方法。
从以上技术方案可以看出，本申请具有以下优点：
本申请提供了一种基于辅助相机的线阵相机标定方法，包括：对辅助相机进行标定，将已标定的辅助相机与待标定的线阵相机刚性组合并安装到运动平台上，将棋盘格标定板放置在组合相机的成像区域内；通过驱动运动平台获取辅助相机在初始位置、结束位置拍摄的标定板图像和线阵相机的动态扫描图像，并计算组合相机在辅助相机坐标系下的运动向量；通过运动向量和动态扫描图像计算配准点在辅助相机坐标系下的坐标；通过多个配准点在辅助相机坐标系下的坐标拟合线阵相机成像平面在辅助相机坐标系下的方程式；基于旋转矩阵的性质和线阵相机成像模型计算线阵相机的内参数以及辅助相机与线阵相机之间的刚性变换矩阵。
本申请中，利用已标定的辅助相机刚性组合待标定的线阵相机，在运动平台上进行高精度标定，基于辅助相机标定线阵相机参数，不仅减少了标定时所需的图像数量，而且提高了标定准确度。
附图说明
为了更清楚地说明本申请实施例或现有技术中的技术方案，下面将对实施例或现有技术描述中所需要使用的附图作简单地介绍，显而易见地，下面描述中的附图仅仅是本申请的一些实施例，对于本领域普通技术人员来讲，在不付出创造性劳动性的前提下，还可以根据这些附图获得其它的附图。
图1为本申请实施例提供的一种基于辅助相机的线阵相机标定方法的一个流程示意图；
图2为本申请实施例提供的一种获取配准点坐标的示意图；
图3为本申请实施例提供的一种基于辅助相机的线阵相机标定装置的一 个结构示意图。
具体实施方式
为了使本技术领域的人员更好地理解本申请方案，下面将结合本申请实施例中的附图，对本申请实施例中的技术方案进行清楚、完整地描述，显然，所描述的实施例仅是本申请一部分实施例，而不是全部的实施例。基于本申请中的实施例，本领域普通技术人员在没有做出创造性劳动前提下所获得的所有其他实施例，都属于本申请保护的范围。
为了便于理解，请参阅图1，本申请实施例提供了一种基于辅助相机的线阵相机标定方法，包括：
步骤101、对辅助相机进行标定，将已标定的辅助相机与待标定的线阵相机刚性组合并安装到运动平台上，将棋盘格标定板放置在组合相机的成像区域内。
可以采用面阵相机作为辅助相机，然后可以采用张正友标定方法对辅助相机进行标定，得到辅助相机的内参数Pf。
将已标定的辅助相机与待标定的线阵相机刚性连接，得到组合相机，并将组合相机安装到单轴高精度直线运动平台上，然后将棋盘格标定板放置在组合相机的成像区域内，以便辅助相机和线阵相机均可以拍摄到棋盘格标定板。
步骤102、通过驱动运动平台获取辅助相机在初始位置、结束位置拍摄的标定板图像和线阵相机的动态扫描图像，并计算组合相机在辅助相机坐标系下的运动向量。
获取辅助相机在初始位置拍摄的标定板图像，驱动运动平台使得组合相机沿直线运动预置距离，使得线阵相机获取目标行数的动态扫描图像，并获取辅助相机在运动的结束位置的标定板图像；基于辅助相机在初始位置、结束位置拍摄的标定板图像，采用N点透视单目视觉定位方法计算辅助相机在初始位置的外参数以及结束位置的外参数；根据辅助相机在初始位置的外参数、结束位置的外参数以及目标行数计算组合相机在辅助相机坐标系下的运动向量。
在放置好棋盘格标定板后，通过辅助相机拍摄棋盘格图像，得到初始位置的棋盘格图像，然后驱动运动平台使得组合相机沿直线运动一段距离，使得线阵相机拍摄活动行数为n的动态扫描图像，并且通过辅助相机拍摄运动的结束位置的棋盘格图像。通过应用N点透视单目视觉定位方法可以求得辅助相机在初始位置的外参数Rf (0)、Tf (0)以及结束位置的外参数Rf (n)、Tf (n)。通过下面的公式求得组合相机在辅助相机坐标系下的运动向量Vf(单位：mm/pixel)：
式中，Vf-X、Vf-Y、Vf-z分别为组合相机在辅助相机坐标系下的X、Y、Z方向下的运动速度。
步骤103、通过运动向量和动态扫描图像计算配准点在辅助相机坐标系下的坐标。
通过运动向量、配准点在动态扫描图像中的亚像素坐标以及动态扫描图像中棋盘格角点的亚像素坐标计算配准点在辅助相机坐标系下的坐标。具体的，首先，对线阵相机的动态扫描图像进行亚像素角点坐标提取，可以得到0,1,2,…等多个角点的亚像素坐标[uP0,vP0]，[uP1,vP1]，[uP2,vP2]…(如图2所示)。然后利用线阵相机扫描方向无透视、无畸变的特性，可得到角点坐标和配准点坐标的关系式：
式中，XWP5是角点5在世界坐标系下的X坐标，XWP6是角点6在世界坐标系下的X坐标，XWIS3是配准点3在世界坐标系下的X坐标；其中，配准点是线阵相机成像平面与棋盘格标定板格子线的相交点，配准点可以用于建立不同坐标系之间的对应关系，从而实现坐标系的转换或变换。
获得辅助相机在初始位置的外参数Rf (0)、Tf (0)，运动向量Vf后，可以利用公式(3)将像素坐标系下y坐标为v的配准点的世界坐标(XW,YW,ZW)变换为辅助相机坐标系下的坐标(Xf (v),Yf (v),Zf (v))：
通过上述过程可以计算得到配准点在辅助相机坐标系下的坐标。
步骤104、通过多个配准点在辅助相机坐标系下的坐标拟合线阵相机成像平面在辅助相机坐标系下的方程式。
通过扫描多个位姿的棋盘格标定板，可以获得大量在辅助相机坐标系下的配准点坐标。排除配准点中的异常点后，可以利用最小二乘法进行平面拟合，获得线阵相机成像平面在面阵相机坐标系下的方程式：
AflXf+BflYf+CflZf+Dfl＝0        (4)
式中，Afl、Bfl、Cfl、Dfl均为成像平面方程的系数。
步骤105、基于旋转矩阵的性质和线阵相机成像模型计算线阵相机的内参数以及辅助相机与线阵相机之间的刚性变换矩阵。
利用刚性变换中旋转矩阵的性质和线阵相机成像模型将辅助相机坐标系下的配准点的坐标变换到线阵相机坐标系下，再将线阵相机坐标系下的配准点的坐标变换到像素坐标系下，得到中间关系式；
从中间关系式中分离已知参数，得到未知参数关系式；
根据线阵相机成像平面在辅助相机坐标系下的方程式获取线阵相机成像平面的单位法向量，基于单位法向量和未知参数关系式求解得到线阵相机的内参数以及辅助相机与线阵相机之间的刚性变换矩阵。
由于两个相机刚性连接，通过公式(5)可以将辅助相机坐标系下的点变换到线阵相机坐标系下。在不考虑镜头畸变的情况下，通过线阵相机成像模型(公式(6))可将线阵相机坐标系下的点变换到像素坐标系下。

式中，Rfl代表刚性变换中的旋转矩阵，Tfl代表刚性变换中的平移向量，fx代表像距与x方向像元尺寸的比值，fy代表像距与y方向像元尺寸的比值， u0代表x方向的主点位置，主点是成像平面与光轴垂直相交的点。
利用旋转矩阵的性质将公式(5)、公式(6)化简合并后，可以得到公式(7)：
式中，r为旋转矩阵Rfl中的元素，tx、ty、tz分别为平移向量Tfl中x、y、z方向上的平移距离；
根据直接线性变换理论，将所有的已知参数分离(即Xf、Yf、uXf、uYf、u)，并将所有的未知参数重新组合为新的系数mi(i＝1…6)得到未知参数关系式，如公式(8)所示，可以利用最小二乘方法从超定方程组中解算出这些系数。
其中，
公式(8)描述了理想情况下的线阵相机成像模型，但对于严格的几何检校来说，求解出模型中的线性系数后还需要进一步从这些线性系数中解算出具有物理含义的几何成像模型参数。
在上述步骤中已经获得了线阵相机成像平面在辅助相机坐标系下的方程式，进而可以获取线阵相机成像平面的单位法向量(afl,bfl,cfl)为：
经过刚性变换的旋转矩阵Rfl，可得：
在上述步骤中已求出m4、m5，已知m4＝r12，m5＝-r11，根据公式(9)可求得r13，再结合旋转矩阵的性质可求出r31：

O＝a2r12 4+a2r12 2r13 2+2abr11r12r13 2
Q＝2acr11r12 2r13+2ar11r12 3+2ar11r12r13 2
S＝b2r11 2r13 2+b2r12 2r13 2
W＝2bcr11 2r12r13+2bcr12 3r13+2br11 2r13 2
E＝c2r11 2r12 2+c2r12 4+2cr11 2r12r13+r11 2r12 2+r11 2r13 2
式中，O、Q、S、W、E为中间参数，a＝afl、b＝bfl、c＝cfl。
假设r31>0，根据旋转矩阵的性质可求出r21、r22、r23。由于辅助相机坐标系和线阵相机坐标系均为右手坐标系，不存在反射情况，故旋转矩阵的行列式的值应为+1。如果计算出来的旋转矩阵Rfl的行列式满足下面不等式：
则旋转矩阵的值是正确的，否则，应重新假设r31<0，再计算旋转矩阵Rfl的值。
在前面步骤中已求出m1、m2、Rfl，又知道m1＝-u0r12-fxr32，m2＝u0r11+fxr31，可以求解出线阵相机的内参数u0，fx。
由于线阵相机坐标系原点在成像平面上，所以刚性变换矩阵中的平移向量Tfl满足：
Afltx+Bflty+Cfltz+Dfl＝0        (10)
结合前面步骤中已求出的m3、m6，可求得平移向量Tfl的值：
式中，A＝Afl，B＝Bfl，C＝Cfl，D＝Dfl。
在前面步骤中已经求出两个相机之间刚性变换的旋转矩阵Rfl，可用公式(11)将辅助相机坐标系下的运动向量转换到线阵相机坐标系下：
至此，线阵相机基本模型中的11个待求参数全部利用线性和解析计算的方式得到了。
进一步，在步骤105之后，还可以包括：
步骤106、对线阵相机的内参数以及辅助相机与线阵相机之间的刚性变换矩阵进行优化。
对于高精度相机几何检校而言，通过上面获取的相机参数并非最优结果。最主要的原因在于在上述求解过程中未考虑线阵相机镜头的畸变。
根据Brown的畸变模型理论，可以得到线阵相机镜头的畸变参数对像点坐标的影响公式为：
x'＝x(1+k1x2+k2x4)+p1(x2+2x2)＝x+3p1x2+k1x3+k2x5     (12)
式中，k1，k2表示镜头的径向畸变系数，p1表示镜头的切向畸变系数，可以更准确地对镜头的径向和切向畸变建模。
根据线阵相机的镜头畸变模型(即公式(12))和最小二乘优化原理，建立相机参数优化模型；通过求解所述相机参数优化模型，得到线阵相机的参数修正量；通过所述参数修正量修正线阵相机的内参数以及辅助相机与线阵相机之间的刚性变换矩阵，得到线阵相机的优化后内参数以及优化后刚性变换矩阵。
定义绕Z轴旋转的角度为γ，绕Y轴旋转的角度为β，绕X轴旋转的角度为α。同时定义旋转分量满足四个条件：逆时针旋转的方向为旋转正方向，旋转轴特性为绕固定轴旋转，转角系统为γ-β-α，旋转分量取值范围为-Π/2≤β≤Π/2，-Π≤α≤Π，-Π≤γ≤Π。并且已知世界坐标系，辅助相机坐标系，线阵相机坐标系均为右手坐标系，所以刚性变换中的旋转矩阵Rfl可以唯一地分解成一组旋转分量：
在视觉测量中通常将最小二乘解视为最佳输出结果，因此最优参数值可以通过最小化以下函数得到：
式中，ui是真实扫描图像中检测到的亚像素点i的X坐标，是利用空间点和相机模型重新投影点i获得的坐标，N为重投影点数量。
根据线阵相机成像模型和镜头畸变模型可推导出重投影点坐标的公式：
其中，Sx是线阵相机像元在X方向上的尺寸，f＝fxSx，是不考虑畸变情况时，空间点在线阵相机图像坐标系下的重投影点坐标的x轴坐标值；
通过一阶泰勒展开线性化后，误差方程如下：
式中，(eu,ev)为每个点的重投影误差，Δ表示对应参数的修正值。
将公式(16)以向量和矩阵的形式重写，得到相机参数优化模型如下：
ei＝Di·M+gi
ei＝[eu ev]T
M＝[Δu0 Δp1 Δk1 Δk2 Δf Δα Δβ Δγ Δtx Δty Δtz]T

每一对配准点(ui,0)和都可以建立上述这个公式。根据最小二乘调整理论，令误差向量E＝0，得：
0＝D·M+G
求解上述线性超定方程组可以得到最小二乘解M，M是线阵相机的内参数和刚性变换矩阵的修正量。通过反复迭代上述的优化过程直至重投影误差满足需要为止，输出最优解M，得到线阵相机的参数修正量，通过参数修正量修正线阵相机的内参数以及辅助相机与线阵相机之间的刚性变换矩阵，得到线阵相机的优化后内参数以及优化后刚性变换矩阵，实现线阵相机的高精度标定。
本申请实施例提出了一种基于辅助相机的线阵相机标定方法，该方法利用面阵相机作为辅助装置，通过刚性组合待标定的线阵相机，在运动平台上进行高精度的标定，该方法不仅提高了标定的准确性和可靠性，而且操作简便、成本低廉，可以在工业测量领域得到广泛应用。与目前工业中常用的动态扫描标定方法相比，该方法基于辅助相机标定线阵相机参数，不仅减少了标定时所需的图像数量，而且提高了标定准确度。与现有的线阵相机和面阵相机的联合标定方法相比，该方法不需要同时使用标定板和定制的不规则图像平板。与现有的静态标定方法相比，该方法采用动态扫描方式标定，可以获得大量的配准点，从而提高标定精度。
进一步，本申请考虑了线阵相机镜头的畸变，根据线阵相机镜头畸变模型和最小二乘优化原理，建立相机参数优化模型，通过优化相机参数优化模型，获得线阵相机优化参数，进一步优化了线阵相机的标定精度。
以上为本申请提供的一种基于辅助相机的线阵相机标定方法的一个实施例，以下为本申请提供的一种基于辅助相机的线阵相机标定装置。
请参考图3，本申请实施例提供的一种基于辅助相机的线阵相机标定装置，包括：
组合单元，用于对辅助相机进行标定，将已标定的辅助相机与待标定的 线阵相机刚性组合并安装到运动平台上，将棋盘格标定板放置在组合相机的成像区域内；
第一计算单元，用于通过驱动运动平台获取辅助相机在初始位置、结束位置拍摄的标定板图像和线阵相机的动态扫描图像，并计算组合相机在辅助相机坐标系下的运动向量；
第二计算单元，用于通过运动向量和动态扫描图像计算配准点在辅助相机坐标系下的坐标；
拟合单元，用于通过多个配准点在辅助相机坐标系下的坐标拟合线阵相机成像平面在辅助相机坐标系下的方程式；
第三计算单元，用于基于旋转矩阵的性质和线阵相机成像模型计算线阵相机的内参数以及辅助相机与线阵相机之间的刚性变换矩阵。
作为进一步地改进，第一计算单元，具体用于：
获取辅助相机在初始位置拍摄的标定板图像，驱动运动平台使得组合相机沿直线运动预置距离，使得线阵相机获取目标行数的动态扫描图像，并获取辅助相机在运动的结束位置的标定板图像；
基于辅助相机在初始位置、结束位置拍摄的标定板图像，采用N点透视单目视觉定位方法计算辅助相机在初始位置的外参数以及结束位置的外参数；
根据辅助相机在初始位置的外参数、结束位置的外参数以及目标行数计算组合相机在辅助相机坐标系下的运动向量。
作为进一步地改进，第二计算单元，具体用于：
通过运动向量、配准点在动态扫描图像中的亚像素坐标以及动态扫描图像中棋盘格角点的亚像素坐标计算配准点在辅助相机坐标系下的坐标。
作为进一步地改进，第三计算单元，具体用于：
利用刚性变换中旋转矩阵的性质和线阵相机成像模型将辅助相机坐标系下的配准点的坐标变换到线阵相机坐标系下，再将线阵相机坐标系下的配准点的坐标变换到像素坐标系下，得到中间关系式；
从中间关系式中分离已知参数，得到未知参数关系式；
根据线阵相机成像平面在辅助相机坐标系下的方程式获取线阵相机成像 平面的单位法向量，基于单位法向量和未知参数关系式求解得到线阵相机的内参数以及辅助相机与线阵相机之间的刚性变换矩阵。
作为进一步地改进，装置还包括：优化单元，用于：
根据线阵相机的镜头畸变模型和最小二乘优化原理，建立相机参数优化模型；
通过求解相机参数优化模型，得到线阵相机的参数修正量；
通过参数修正量修正线阵相机的内参数以及辅助相机与线阵相机之间的刚性变换矩阵，得到线阵相机的优化后内参数以及优化后刚性变换矩阵。
本申请实施例，通过刚性组合待标定的线阵相机，在运动平台上进行高精度的标定，该方法不仅提高了标定的准确性和可靠性，而且操作简便、成本低廉，可以在工业测量领域得到广泛应用。与目前工业中常用的动态扫描标定方法相比，该方法基于辅助相机标定线阵相机参数，不仅减少了标定时所需的图像数量，而且提高了标定准确度。与现有的线阵相机和面阵相机的联合标定方法相比，该方法不需要同时使用标定板和定制的不规则图像平板。与现有的静态标定方法相比，该方法采用动态扫描方式标定，可以获得大量的配准点，从而提高标定精度。
进一步，本申请考虑了线阵相机镜头的畸变，根据线阵相机镜头畸变模型和最小二乘优化原理，建立相机参数优化模型，通过优化相机参数优化模型，获得线阵相机优化参数，进一步优化了线阵相机的标定精度。
本申请实施例还提供了一种电子设备，设备包括处理器以及存储器；
存储器用于存储程序代码，并将程序代码传输给处理器；
处理器用于根据程序代码中的指令执行前述方法实施例中的基于辅助相机的线阵相机标定方法。
本申请实施例还提供了一种计算机可读存储介质，计算机可读存储介质用于存储程序代码，程序代码被处理器执行前述方法实施例中的基于辅助相机的线阵相机标定方法。
所属领域的技术人员可以清楚地了解到，为描述的方便和简洁，上述描述的装置和单元的具体工作过程，可以参考前述方法实施例中的对应过程，在此不再赘述。
本申请的说明书及上述附图中的术语“第一”、“第二”、“第三”、“第四”等(如果存在)是用于区别类似的对象，而不必用于描述特定的顺序或先后次序。应该理解这样使用的数据在适当情况下可以互换，以便这里描述的本申请的实施例例如能够以除了在这里图示或描述的那些以外的顺序实施。此外，术语“包括”和“具有”以及他们的任何变形，意图在于覆盖不排他的包含，例如，包含了一系列步骤或单元的过程、方法、系统、产品或设备不必限于清楚地列出的那些步骤或单元，而是可包括没有清楚地列出的或对于这些过程、方法、产品或设备固有的其它步骤或单元。
应当理解，在本申请中，“至少一个(项)”是指一个或者多个，“多个”是指两个或两个以上。“和/或”，用于描述关联对象的关联关系，表示可以存在三种关系，例如，“A和/或B”可以表示：只存在A，只存在B以及同时存在A和B三种情况，其中A，B可以是单数或者复数。字符“/”一般表示前后关联对象是一种“或”的关系。“以下至少一项(个)”或其类似表达，是指这些项中的任意组合，包括单项(个)或复数项(个)的任意组合。例如，a，b或c中的至少一项(个)，可以表示：a，b，c，“a和b”，“a和c”，“b和c”，或“a和b和c”，其中a，b，c可以是单个，也可以是多个。
在本申请所提供的几个实施例中，应该理解到，所揭露的装置和方法，可以通过其它的方式实现。例如，以上所描述的装置实施例仅仅是示意性的，例如，所述单元的划分，仅仅为一种逻辑功能划分，实际实现时可以有另外的划分方式，例如多个单元或组件可以结合或者可以集成到另一个系统，或一些特征可以忽略，或不执行。另一点，所显示或讨论的相互之间的耦合或直接耦合或通信连接可以是通过一些接口，装置或单元的间接耦合或通信连接，可以是电性，机械或其它的形式。
所述作为分离部件说明的单元可以是或者也可以不是物理上分开的，作为单元显示的部件可以是或者也可以不是物理单元，即可以位于一个地方，或者也可以分布到多个网络单元上。可以根据实际的需要选择其中的部分或者全部单元来实现本实施例方案的目的。
另外，在本申请各个实施例中的各功能单元可以集成在一个处理单元中，也可以是各个单元单独物理存在，也可以两个或两个以上单元集成在一个单 元中。上述集成的单元既可以采用硬件的形式实现，也可以采用软件功能单元的形式实现。
所述集成的单元如果以软件功能单元的形式实现并作为独立的产品销售或使用时，可以存储在一个计算机可读取存储介质中。基于这样的理解，本申请的技术方案本质上或者说对现有技术做出贡献的部分或者该技术方案的全部或部分可以以软件产品的形式体现出来，该计算机软件产品存储在一个存储介质中，包括若干指令用以通过一台计算机设备(可以是个人计算机，服务器，或者网络设备等)执行本申请各个实施例所述方法的全部或部分步骤。而前述的存储介质包括：U盘、移动硬盘、只读存储器(英文全称：Read-Only Memory，英文缩写：ROM)、随机存取存储器(英文全称：Random Access Memory，英文缩写：RAM)、磁碟或者光盘等各种可以存储程序代码的介质。
以上所述，以上实施例仅用以说明本申请的技术方案，而非对其限制；尽管参照前述实施例对本申请进行了详细的说明，本领域的普通技术人员应当理解：其依然可以对前述各实施例所记载的技术方案进行修改，或者对其中部分技术特征进行等同替换；而这些修改或者替换，并不使相应技术方案的本质脱离本申请各实施例技术方案的精神和范围。

Claims (10)

  1. 一种基于辅助相机的线阵相机标定方法，其特征在于，包括：
    对辅助相机进行标定，将已标定的辅助相机与待标定的线阵相机刚性组合并安装到运动平台上，将棋盘格标定板放置在组合相机的成像区域内；
    通过驱动运动平台获取辅助相机在初始位置、结束位置拍摄的标定板图像和线阵相机的动态扫描图像，并计算组合相机在辅助相机坐标系下的运动向量；
    通过所述运动向量和所述动态扫描图像计算配准点在辅助相机坐标系下的坐标；
    通过多个配准点在辅助相机坐标系下的坐标拟合线阵相机成像平面在辅助相机坐标系下的方程式；
    基于旋转矩阵的性质和线阵相机成像模型计算线阵相机的内参数以及辅助相机与线阵相机之间的刚性变换矩阵。
  2. 根据权利要求1所述的基于辅助相机的线阵相机标定方法，其特征在于，所述通过驱动运动平台获取辅助相机在初始位置、结束位置拍摄的标定板图像和线阵相机的动态扫描图像，并计算组合相机在辅助相机坐标系下的运动向量，包括：
    获取辅助相机在初始位置拍摄的标定板图像，驱动运动平台使得组合相机沿直线运动预置距离，使得线阵相机获取目标行数的动态扫描图像，并获取辅助相机在运动的结束位置的标定板图像；
    基于辅助相机在初始位置、结束位置拍摄的标定板图像，采用N点透视单目视觉定位方法计算辅助相机在初始位置的外参数以及结束位置的外参数；
    根据辅助相机在初始位置的外参数、结束位置的外参数以及所述目标行数计算组合相机在辅助相机坐标系下的运动向量。
  3. 根据权利要求1所述的基于辅助相机的线阵相机标定方法，其特征在于，所述通过所述运动向量和所述动态扫描图像计算配准点在辅助相机坐标系下的坐标，包括：
    通过所述运动向量、配准点在动态扫描图像中的亚像素坐标以及动态扫描图像中棋盘格角点的亚像素坐标计算配准点在辅助相机坐标系下的坐标。
  4. 根据权利要求1所述的基于辅助相机的线阵相机标定方法，其特征在于，基于旋转矩阵的性质和线阵相机成像模型计算线阵相机的内参数以及辅助相机与线阵相机之间的刚性变换矩阵，包括：
    利用刚性变换中旋转矩阵的性质和线阵相机成像模型将辅助相机坐标系下的配准点的坐标变换到线阵相机坐标系下，再将线阵相机坐标系下的配准点的坐标变换到像素坐标系下，得到中间关系式；
    从所述中间关系式中分离已知参数，得到未知参数关系式；
    根据线阵相机成像平面在辅助相机坐标系下的方程式获取线阵相机成像平面的单位法向量，基于所述单位法向量和所述未知参数关系式求解得到线阵相机的内参数以及辅助相机与线阵相机之间的刚性变换矩阵。
  5. 根据权利要求4所述的基于辅助相机的线阵相机标定方法，其特征在于，所述方法还包括：
    根据线阵相机的镜头畸变模型和最小二乘优化原理，建立相机参数优化模型；
    通过求解所述相机参数优化模型，得到线阵相机的参数修正量；
    通过所述参数修正量修正线阵相机的内参数以及辅助相机与线阵相机之间的刚性变换矩阵，得到线阵相机的优化后内参数以及优化后刚性变换矩阵。
  6. 一种基于辅助相机的线阵相机标定装置，其特征在于，包括：
    组合单元，用于对辅助相机进行标定，将已标定的辅助相机与待标定的线阵相机刚性组合并安装到运动平台上，将棋盘格标定板放置在组合相机的成像区域内；
    第一计算单元，用于通过驱动运动平台获取辅助相机在初始位置、结束位置拍摄的标定板图像和线阵相机的动态扫描图像，并计算组合相机在辅助相机坐标系下的运动向量；
    第二计算单元，用于通过所述运动向量和所述动态扫描图像计算配准点在辅助相机坐标系下的坐标；
    拟合单元，用于通过多个配准点在辅助相机坐标系下的坐标拟合线阵相机成像平面在辅助相机坐标系下的方程式；
    第三计算单元，用于基于旋转矩阵的性质和线阵相机成像模型计算线阵 相机的内参数以及辅助相机与线阵相机之间的刚性变换矩阵。
  7. 根据权利要求6所述的基于辅助相机的线阵相机标定装置，其特征在于，所述第一计算单元，具体用于：
    获取辅助相机在初始位置拍摄的标定板图像，驱动运动平台使得组合相机沿直线运动预置距离，使得线阵相机获取目标行数的动态扫描图像，并获取辅助相机在运动的结束位置的标定板图像；
    基于辅助相机在初始位置、结束位置拍摄的标定板图像，采用N点透视单目视觉定位方法计算辅助相机在初始位置的外参数以及结束位置的外参数；
    根据辅助相机在初始位置的外参数、结束位置的外参数以及所述目标行数计算组合相机在辅助相机坐标系下的运动向量。
  8. 根据权利要求6所述的基于辅助相机的线阵相机标定装置，其特征在于，所述第二计算单元，具体用于：
    通过所述运动向量、配准点在动态扫描图像中的亚像素坐标以及动态扫描图像中棋盘格角点的亚像素坐标计算配准点在辅助相机坐标系下的坐标。
  9. 一种电子设备，其特征在于，所述设备包括处理器以及存储器；
    所述存储器用于存储程序代码，并将所述程序代码传输给所述处理器；
    所述处理器用于根据所述程序代码中的指令执行权利要求1-5任一项所述的基于辅助相机的线阵相机标定方法。
  10. 一种计算机可读存储介质，其特征在于，所述计算机可读存储介质用于存储程序代码，所述程序代码被处理器执行时实现权利要求1-5任一项所述的基于辅助相机的线阵相机标定方法。
PCT/CN2024/108113 2024-04-07 2024-07-29 一种基于辅助相机的线阵相机标定方法及相关装置 Pending WO2025213629A1 (zh)

Priority Applications (1)

Application Number Priority Date Filing Date Title
US19/002,197 US12347144B1 (en) 2024-04-07 2024-12-26 Line-scan camera calibration method based on an auxiliary camera and device thereof

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
CN202410405378.3 2024-04-07
CN202410405378.3A CN117994359B (zh) 2024-04-07 2024-04-07 一种基于辅助相机的线阵相机标定方法及相关装置

Related Child Applications (1)

Application Number Title Priority Date Filing Date
US19/002,197 Continuation US12347144B1 (en) 2024-04-07 2024-12-26 Line-scan camera calibration method based on an auxiliary camera and device thereof

Publications (1)

Publication Number Publication Date
WO2025213629A1 true WO2025213629A1 (zh) 2025-10-16

Family

ID=90901265

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2024/108113 Pending WO2025213629A1 (zh) 2024-04-07 2024-07-29 一种基于辅助相机的线阵相机标定方法及相关装置

Country Status (2)

Country Link
CN (1) CN117994359B (zh)
WO (1) WO2025213629A1 (zh)

Families Citing this family (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117994359B (zh) * 2024-04-07 2024-06-11 广东工业大学 一种基于辅助相机的线阵相机标定方法及相关装置
US12347144B1 (en) 2024-04-07 2025-07-01 Guangdong University Of Technology Line-scan camera calibration method based on an auxiliary camera and device thereof
CN120070600B (zh) * 2025-04-28 2025-11-28 北京思谋智能科技有限公司 相机标定方法、装置、设备及计算机可读存储介质

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106289106A (zh) * 2016-08-04 2017-01-04 北京航空航天大学 一种线阵相机和面阵相机相结合的立体视觉传感器及标定方法
CN109242909A (zh) * 2018-08-17 2019-01-18 中科慧远视觉技术（洛阳）有限公司 一种面向高精度二维尺寸测量的线阵相机标定算法
CN114419170A (zh) * 2022-03-30 2022-04-29 杭州灵西机器人智能科技有限公司 线阵相机和面阵相机的联合标定方法、装置和介质
WO2023045147A1 (zh) * 2021-09-24 2023-03-30 上海闻泰电子科技有限公司 双目摄像机的标定方法、系统、电子设备和存储介质
CN116051659A (zh) * 2023-03-30 2023-05-02 山东科技大学 一种线阵相机与2d激光扫描仪联合标定方法
CN117994359A (zh) * 2024-04-07 2024-05-07 广东工业大学 一种基于辅助相机的线阵相机标定方法及相关装置

Family Cites Families (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109272574B (zh) * 2018-09-10 2020-04-10 武汉大学 基于投影变换的线阵旋转扫描相机成像模型构建方法和标定方法
CN111735479B (zh) * 2020-08-28 2021-03-23 中国计量大学 一种多传感器联合标定装置及方法
CN114241177B (zh) * 2021-12-20 2025-04-29 民航成都电子技术有限责任公司 一种基于线阵扫描成像的机场道面表观图像检测系统
CN116188591B (zh) * 2022-12-28 2025-09-16 合肥工业大学 多相机全局标定方法及装置、电子设备
CN116147534B (zh) * 2023-03-03 2025-10-03 浙江大学 一种基于镜面辅助的多视角三维激光扫描系统及复杂表面全景测量方法
CN116681772B (zh) * 2023-03-30 2026-02-10 中国人民解放军网络空间部队信息工程大学 一种非共视下多相机在线标定方法

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106289106A (zh) * 2016-08-04 2017-01-04 北京航空航天大学 一种线阵相机和面阵相机相结合的立体视觉传感器及标定方法
CN109242909A (zh) * 2018-08-17 2019-01-18 中科慧远视觉技术（洛阳）有限公司 一种面向高精度二维尺寸测量的线阵相机标定算法
WO2023045147A1 (zh) * 2021-09-24 2023-03-30 上海闻泰电子科技有限公司 双目摄像机的标定方法、系统、电子设备和存储介质
CN114419170A (zh) * 2022-03-30 2022-04-29 杭州灵西机器人智能科技有限公司 线阵相机和面阵相机的联合标定方法、装置和介质
CN116051659A (zh) * 2023-03-30 2023-05-02 山东科技大学 一种线阵相机与2d激光扫描仪联合标定方法
CN117994359A (zh) * 2024-04-07 2024-05-07 广东工业大学 一种基于辅助相机的线阵相机标定方法及相关装置

Also Published As

Publication number Publication date
CN117994359A (zh) 2024-05-07
CN117994359B (zh) 2024-06-11

Similar Documents

Publication Publication Date Title
WO2025213629A1 (zh) 一种基于辅助相机的线阵相机标定方法及相关装置
CN106457562B (zh) 用于校准机器人的方法和机器人系统
CN111612794B (zh) 基于多2d视觉的零部件高精度三维位姿估计方法及系统
US9734419B1 (en) System and method for validating camera calibration in a vision system
JP2002027507A (ja) カメラ・キャリブレーション装置及び方法、並びに、記憶媒体
CN111060898B (zh) 场端多线束激光雷达内参标定方法
JPH08210816A (ja) ロボット−視覚センサシステムにおいてセンサ座標系とロボット先端部の関係を定める座標系結合方法
CN109272555B (zh) 一种rgb-d相机的外部参数获得及标定方法
CN116045851A (zh) 线激光轮廓仪标定方法、装置、电子设备及存储介质
CN106815866B (zh) 鱼眼相机的标定方法及其标定系统和标板
WO2024021654A1 (zh) 一种用于线结构光3d相机的误差校正方法以及装置
CN114926538B (zh) 单目激光散斑投影系统的外参数标定方法和装置
CN115239819A (zh) 相机标定方法、装置、系统、电子装置和存储介质
CN116580108A (zh) 一种双目立体视觉相机标定参数的优化方法
CN111652945A (zh) 一种相机标定方法
CN109544642B (zh) 一种基于n型靶标的tdi-ccd相机参数标定方法
CN118279410A (zh) 摄像头坐标转换参数标定方法、系统及摄像头
CN115601450B (zh) 周视标定方法及相关装置、设备、系统和介质
Zhang et al. Improved camera calibration method and accuracy analysis for binocular vision
CN113240751B (zh) 一种机器人末端相机的标定方法
CN119665859A (zh) 一种用于结构件的多目视觉孔位测量方法及装置
US12347144B1 (en) Line-scan camera calibration method based on an auxiliary camera and device thereof
CN115239816B (zh) 一种相机标定方法、系统、电子设备及存储介质
CN118037605A (zh) 基于外参调整的平面成像透视变形校正方法
JP2013170829A (ja) ひずみ計測装置及びひずみ計測方法

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 24934696

Country of ref document: EP

Kind code of ref document: A1