CN106780623B - Rapid calibration method for robot vision system - Google Patents
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Abstract
The invention discloses a quick calibration method of a robot vision system, which comprises a robot, a working platform, a CCD camera and a computer, wherein the computer is used for controlling the CCD camera to collect image data of the working platform, and the computer can also control the movement of the robot; the method comprises the following steps: step 1, installing a CCD camera on a working platform, so that when a workpiece is placed on the working platform, part of the workpiece is positioned in a field area of the CCD camera, and characteristic coordinate points of the workpiece are acquired by coordinates of a camera coordinate system; step 2, writing a distortion correction algorithm by utilizing opencv, and carrying out quick distortion correction on the CCD camera; step 3, calibrating the proportional relation between the camera coordinate system and the world coordinate system and the actual size of the robot; and 4, calibrating the origin of the user coordinate system of the whole working platform. The calibration method has the advantages of low cost, convenient installation, quick calibration and easy realization.
Description
Technical Field
The invention belongs to the field of calibration of machine vision systems, and particularly relates to a calibration method of a robot vision system with a fixed camera.
Background
Calibration is an indispensable step of each piece of visual equipment, more and more automatic equipment starts to use machine vision at present, and the accuracy and the speed of calibration directly influence the production efficiency of enterprises. The existing methods are to directly calibrate the camera by using an early checkerboard calibration program, and the calibration of the robot and the camera is mostly assisted by a calibration tool, the existing methods are to install the camera at the tail end of a robot arm, and the hand-eye calibration is also faster by using the calibration tool, but due to the requirement of some work, the CCD camera is not suitable to be fixed at the tail end of the robot, and the following defects exist for a robot vision system of a fixed CCD:
(1) The acquisition speed of the internal parameters and the distortion matrix of the camera is low, the calibration of the CCD camera is mostly calibrated by using a checkerboard calibration program at present, and different cameras, different installation distances and lens focal lengths generate different distortions, so that the acquisition of the distortion matrix of the camera is often carried out once after equipment is installed and is used for correcting after image acquisition in the later working process, the image correction speed by using the internal parameters and the distortion matrix of the camera is often high, but the efficiency in acquiring the distortion matrix is very low, and a great amount of time for installing and debugging personnel can be wasted on the correction;
(2) The precision of detecting the large workpiece is low, the CCD is used for detecting the large workpiece and is always fixed at a position capable of completely placing the workpiece in a view field, the influence of the distortion of the CCD camera on the detection precision is increased due to the fact that the distance between the camera and the workpiece is too far, and the characteristics of an image are blurred due to the limitation of pixels of the camera;
(3) The corresponding relation and the proportion conversion of the coordinate system of the robot and the camera are usually completed through teaching of the robot on a special calibration tool, so that a complex process and a long time of a workpiece with higher precision are required to do the work, and the efficiency is reduced.
Disclosure of Invention
The invention aims to provide a method for rapidly calibrating a robot vision system, which has the advantages of low cost, convenience in installation, rapid calibration and easiness in realization.
In order to achieve the above object, the solution of the present invention is:
the robot vision system comprises a robot, a working platform, a CCD camera and a computer, wherein the computer is used for controlling the CCD camera to collect image data of the working platform, and the computer can also control the movement of the robot; the method comprises the following steps:
step 1, installing a CCD camera on a working platform, so that when a workpiece is placed on the working platform, part of the workpiece is positioned in a field area of the CCD camera, and characteristic coordinate points of the workpiece are acquired by coordinates of a camera coordinate system;
step 2, writing a distortion correction algorithm by utilizing opencv, and carrying out quick distortion correction on the CCD camera;
step 3, calibrating the proportional relation between the camera coordinate system and the world coordinate system and the actual size of the robot;
and 4, calibrating the origin of the user coordinate system of the whole working platform.
In the step 1, the method for collecting the characteristic coordinate points of the workpiece is as follows: the CCD camera recognizes local features of the workpiece in the field of view area and extracts coordinates of the features, simultaneously detects the placing posture of the workpiece on the working platform, collects coordinate relations among features in the workpiece according to the drawing of the workpiece, calculates difference values of horizontal coordinates and vertical coordinates between required feature coordinates and detected feature coordinates, and accordingly obtains required feature coordinate points.
In the step 2, the specific contents of the distortion correction algorithm are:
(1) The method comprises the steps that N images with different angles and containing the whole checkerboard are acquired by a CCD camera through moving or rotating the checkerboard;
(2) Presetting the number of rows and columns of inner focuses of a checkerboard, wherein the number of rows and columns cannot be the same;
(3) Setting point location data in a 3D scene;
(4) And (3) processing the checkerboard image acquired in the step (1) as follows:
a. the first image is called, and gray level processing is carried out;
b. reducing the image size by n times, wherein n is set according to the actual size of the image;
c. detecting corner points by using a checkerboard detection function;
d. reloading an original image, amplifying the detected angular point data with the abscissa and the ordinate of the angular point data by n times, and adding the angular point data into a sub-pixel angular point detection function;
e. putting the detected sub-pixel angular point coordinates into a set;
f. c, calling a next image, and repeating the steps a-e until all checkerboard images are processed;
(5) Adding the set of the coordinates of the angular points with the sub-pixels to a camera calibration matrix, and calculating internal parameters and distortion matrixes of the video camera by a camera calibration function;
(6) And storing and outputting camera internal parameters and distortion matrixes.
In the step (6), the camera internal parameters and the distortion matrix are stored and output in a vml suffix format.
Firstly, selecting two mark points in a blank area of a working platform in a view field area, detecting coordinates of the two mark points by using a camera, and representing a distance d1 between the two mark points by using the camera coordinates; then controlling the robot to find two mark points, and expressing the distance d2 between the two mark points by the world coordinates of the robot; finally, measuring the actual distance dimension d3 between the two mark points by using a ruler; let k1=d1/d 2, k2=d2/d 3, let the coordinates of the camera coordinate system be Cc, the coordinates of the robot world coordinate system be Cr, the coordinates of the user coordinate system of the work platform be Cu, cc=k1×cr=k1×k2×cu.
The specific content of the step 4 is as follows: defining a certain point on a working platform as an origin, recording robot world coordinates Cro (cro.x, cro.y), selecting a mark point in a field of view area of a CCD camera, and recording camera coordinates Ccp (ccp.x, ccp.y) and robot world coordinates Crp (crp.x, crp.y) of the mark point; calculating the distance between an origin and a mark point under the world coordinate of a robot, wherein the difference value ex=crp.x-cro.x of the two horizontal coordinates and the difference value ey=crp.y-cro.y of the two vertical coordinates; let the coordinates of the point to be measured obtained by the camera be Cca (Cca. X, cca. Y), when the robot moves from the origin to the point to be measured, the distances that the abscissa needs to move are dx and dy, and dx=cro.x+ex+ [ (Cca. X-ccp.x)/k 1], and dy=cro.y+ey+ [ (Cca. Y-ccp.y)/k 1].
After the scheme is adopted, the invention has the following advantages:
(1) The method overcomes the defect of low acquisition speed of the internal parameters and distortion matrix of the camera, and not only increases the calculation speed, but also does not reduce the calibration precision.
(2) The characteristic recognition precision of detecting the large-scale workpiece is effectively improved, and the device is beneficial to installation and debugging and reducing the size of the whole machine.
(3) And a special calibration tool is not used, so that the corresponding relation and the proportion conversion of the coordinate system of the robot and the camera are simply and quickly completed.
Drawings
FIG. 1 is a schematic representation of an implementation of the present invention;
FIG. 2 is a schematic diagram of a conventional checkerboard of opencv;
fig. 3 is a flow chart of the opencv camera intrinsic and distortion correction algorithm.
Detailed Description
The technical scheme of the present invention will be described in detail below with reference to the accompanying drawings.
As shown in fig. 1, the invention provides a method for rapidly calibrating a robot vision system, wherein the robot vision system comprises a robot 5, a working platform 2, a CCD camera 4 and a computer 6, wherein the robot 5 can be of any model and can be positioned with high precision; the CCD camera 4 has a fixed imaging lens facing the work platform 2; the computer 6 is used for controlling the actions of the CCD camera 4 and the robot 5; the computer 6 collects image data through the CCD camera 4 and processes the image data by using a calibration algorithm, and meanwhile, the computer 6 calculates the conversion relation between a user coordinate system and a robot world coordinate system and a camera coordinate system by controlling the movement of the robot 5 between marking points, so that calibration is completed.
The calibration method comprises the following steps:
step 1, a CCD camera 4 is installed at a corner of a working platform 2, so that a field area of the CCD camera is located in a range 3 in fig. 1, and after a workpiece is placed on the working platform 2 through a conveyor belt or other fixtures, the CCD camera 4 can identify local features of the workpiece in the field area and extract coordinates of the features, meanwhile, the placing posture of the workpiece on the working platform 2 is detected, a coordinate relation between internal features of the workpiece is written into a program according to a workpiece drawing, a difference value between a required feature coordinate and a detected feature coordinate is calculated through a conversion relation, and required feature coordinate points are indirectly obtained, wherein the coordinate point data are stored in terms of coordinates of a camera coordinate system.
For large workpieces, the accuracy is reduced in the prior art that the whole workpiece is placed in the view field of the camera, so that the distance between the CCD camera and the workpiece is shortened in the installation process, only a part of the workpiece is placed in the view field area of the CCD camera, and the required feature coordinates are indirectly obtained through the extraction of local features of the workpiece and the coordinate relationship between internal features of a drawing of the workpiece. Therefore, the characteristic recognition precision of the large-sized workpiece can be effectively improved, and the device is beneficial to installation and debugging and size reduction of the whole machine.
And 2, writing an distortion correction algorithm by using opencv, and carrying out quick distortion correction on the CCD camera.
The opencv standard common checkerboard is shown in fig. 2, and can be printed by a printer and then attached to a plate for use. And acquiring a CCD camera distortion matrix by using a checkerboard detection function of opencv, sub-pixel corner detection and a camera calibration function. However, since the checkerboard detection function of opencv is slower for larger-sized images, too large images cannot even detect checkerboard points, and the sub-pixel corner detection function requires more accurate image data to locate coordinates of corners, the larger the image size is, the better, which causes contradiction, if the image is reduced, the detection speed is fast, but the accuracy is sacrificed, and the image is not reduced, and a lot of time is wasted. The invention therefore proposes an aberration correction algorithm which is implemented as follows:
(1) Manually moving or rotating the checkerboard, so that the CCD camera obtains about 30 images containing the whole checkerboard;
(2) Presetting the number of rows and columns of inner focuses of a checkerboard, wherein the number of rows and columns cannot be the same;
(3) Setting point location data in a 3D scene;
(4) Loading a first of the checkerboard images into a program;
(5) Carrying out gray scale processing on the image;
(6) Reducing the image size by n times, wherein n can be set by a user according to the actual size of the image;
(7) Detecting corner points by using a checkerboard detection function;
(8) Reloading the original image, amplifying the detected angular point data abscissa and the detected angular point data ordinate by n times, and adding the detected angular point data abscissa and the detected angular point data ordinate into a sub-pixel angular point detection function;
(9) The detected sub-pixel angular point coordinates are put into a set, and the next image is loaded until all the images are detected;
(10) Adding the set of the coordinates of the angular points of the stored sub-pixels to a camera calibration matrix, and calculating internal parameters and distortion matrixes of the video camera by using a camera calibration function of opencv;
(11) The camera internal and distortion matrices are saved and output in a special format, here the special format is the vml suffix format that opencv uses exclusively for saving output and reading matrices.
And 3, calibrating the proportional relation between the camera coordinate system and the robot world coordinate system and the actual size after calibrating the camera distortion, randomly clicking two points on the working platform by using a marker, detecting the camera coordinates of the two points and the robot world coordinate, respectively calculating the distance between the two points under the coordinate system, and finally measuring the actual distance size between the two points by using a ruler. The proportional relation among the three distances can convert the coordinates obtained by the camera and the size data of the workpiece obtained from the drawing into coordinates which can be directly used by the robot.
Specifically, first, two points are randomly selected as marks by a marker pen in a blank area of a working platform in a field of view, coordinates of the two points are detected by a camera, the distance between the two points is calculated and is marked as'd 1', and the size of the coordinates of the camera is in units of pixels; then, the robot finds the two points through robot teaching to obtain the world coordinates of the two points, and the distance between the two points is calculated and recorded as'd 2'; finally, the actual distance between these two points is measured with a ruler, denoted as "d3". Let d1 and d2 be "k1", k1=d1/d 2, d2 and d3 be "k2", k2=d2/d 3, cc be the camera coordinate system, cr be the robot world coordinate system, cu be the user coordinate system of the work platform, cc=k1×cr=k1×k2×cu.
And 4, calibrating the origin of the user coordinate system of the whole working platform. The working platform is provided with a fixed origin mark 1, and the fixed origin mark is used for manually returning the robot to the origin for use when debugging the working platform. The robot coordinate system is suitable for a working platform of a large-sized workpiece, so that the view field area of the camera does not include an origin mark, a marker is used for randomly pointing a point in the view field area to obtain the camera coordinate and the robot coordinate of the point, and then the robot is moved to the origin mark of the working platform to obtain the robot coordinate of the origin. Through the proportional relation and the difference value between the three points, any coordinate obtained by the camera can be converted into a user coordinate of the working platform, and the points in the field of view of the camera and outside the field of view can be converted into the form of the camera coordinate in a later positioning algorithm to work, so that the calculated amount is effectively reduced. When the debugging working platform is maintained in the future, only the teaching robot reaches the origin mark, the calibration of the camera is not needed again, the workload of a debugger is reduced, and the debugging speed is increased.
In this embodiment, a marker is used to randomly point a point in the field of view, which is called a mark point P1, and the coordinates of the point are detected after imaging by a camera and written into a visual detection program of the working platform, and are denoted as Ccp (ccp.x, ccp.y). The robot is moved to a point P1 through teaching, the world coordinate of the robot at the point is obtained and is marked as Crp (Crp.x, crp.y), the robot is moved to an origin mark of a working platform, the robot coordinate of the origin is obtained and is marked as Cro (Cro.x, cro.y), the difference between the horizontal and vertical coordinates of the robot between the two points is calculated and is marked as Ex and Ey, ex=Crp.x-Cro.x, ey=Crp.y-Cro.y, and Ex and Ey are written into a program. Assuming that the arbitrary coordinates obtained by the camera are Cca (Cca.x, cca.y), the distances that the X and Y axes of the robot need to move when moving from the origin Cro to Cca are dx and dy, dx=cro.x+Ex+ [ (Cca.x-Ccp.x)/k 1], and dy=cro.y+Ey+ [ (Cca.y-Ccp.y)/k 1].
In summary, according to the method for rapidly calibrating the robot vision system, the CCD camera and the lens are fixed, the coordinates of the marking point in the field of view of the CCD camera and the coordinates of the robot point are obtained through the movement of the robot and the assistance of the marking point, the coordinates corresponding to the working area of the robot are obtained, the pixels and the coordinates are input into the computer, and the calculation is performed through the calibration algorithm model, so that the rapid calibration of the robot vision system is completed. The invention utilizes the accurate positioning of the robot to develop a special calibration algorithm, and can realize quick operation on any machine vision equipment without manufacturing a special calibration tool.
The above embodiments are only for illustrating the technical idea of the present invention, and the protection scope of the present invention is not limited thereto, and any modification made on the basis of the technical scheme according to the technical idea of the present invention falls within the protection scope of the present invention.
Claims (4)
1. The robot vision system comprises a robot, a working platform, a CCD camera and a computer, wherein the computer is used for controlling the CCD camera to collect image data of the working platform, and the computer can also control the movement of the robot; characterized in that the method comprises the following steps:
step 1, installing a CCD camera at a corner on a working platform, so that when a workpiece is placed on the working platform, the part of the workpiece is positioned in a field of view area of the CCD camera, the CCD camera can identify the local feature of the workpiece in the field of view area and extract the coordinate of the feature, meanwhile, the gesture of the workpiece placed on the working platform is detected, the coordinate relation between the internal feature of the workpiece is acquired according to a workpiece drawing, and the difference value between the required feature coordinate and the detected feature coordinate is calculated, so that the required feature coordinate point is obtained;
step 2, writing a distortion correction algorithm by utilizing opencv, and carrying out quick distortion correction on the CCD camera; the specific contents of the distortion correction algorithm are:
(1) The method comprises the steps that N images with different angles and containing the whole checkerboard are acquired by a CCD camera through moving or rotating the checkerboard;
(2) Presetting the number of rows and columns of inner focuses of a checkerboard, wherein the number of rows and columns cannot be the same;
(3) Setting point location data in a 3D scene;
(4) And (3) processing the checkerboard image acquired in the step (1) as follows:
a. the first image is called, and gray level processing is carried out;
b. reducing the image size by n times, wherein n is set according to the actual size of the image;
c. detecting corner points by using a checkerboard detection function;
d. reloading an original image, amplifying the detected angular point data with the abscissa and the ordinate of the angular point data by n times, and adding the angular point data into a sub-pixel angular point detection function;
e. putting the detected sub-pixel angular point coordinates into a set;
f. c, calling a next image, and repeating the steps a-e until all checkerboard images are processed;
(5) Adding the set of the coordinates of the angular points with the sub-pixels to a camera calibration matrix, and calculating internal parameters and distortion matrixes of the video camera by a camera calibration function;
(6) Storing and outputting camera internal parameters and distortion matrixes;
step 3, calibrating the proportional relation between the camera coordinate system and the world coordinate system and the actual size of the robot;
and 4, calibrating the origin of the user coordinate system of the whole working platform.
2. The method for rapidly calibrating a robot vision system according to claim 1, wherein: in the step (6), the camera internal parameters and the distortion matrix are stored and output in a vml suffix format.
3. The method for rapidly calibrating a robot vision system according to claim 1, wherein: firstly, selecting two mark points in a blank area of a working platform in a view field area, detecting coordinates of the two mark points by using a camera, and representing a distance d1 between the two mark points by using the camera coordinates; then controlling the robot to find two mark points, and expressing the distance d2 between the two mark points by the world coordinates of the robot; finally, measuring the actual distance dimension d3 between the two mark points by using a ruler; let k1=d1/d 2, k2=d2/d 3, let the coordinates of the camera coordinate system be Cc, the coordinates of the robot world coordinate system be Cr, the coordinates of the user coordinate system of the work platform be Cu, cc=k1×cr=k1×k2×cu.
4. A method for rapidly calibrating a robot vision system as claimed in claim 3, characterized in that: the specific content of the step 4 is as follows: defining a certain point on a working platform as an origin, recording robot world coordinates Cro (cro.x, cro.y), selecting a mark point in a field of view area of a CCD camera, and recording camera coordinates Ccp (ccp.x, ccp.y) and robot world coordinates Crp (crp.x, crp.y) of the mark point; calculating the distance between an origin and a mark point under the world coordinate of a robot, wherein the difference value ex=crp.x-cro.x of the two horizontal coordinates and the difference value ey=crp.y-cro.y of the two vertical coordinates; let the coordinates of the point to be measured obtained by the camera be Cca (Cca. X, cca. Y), when the robot moves from the origin to the point to be measured, the distances that the abscissa needs to move are dx and dy, and dx=cro.x+ex+ [ (Cca. X-ccp.x)/k 1], and dy=cro.y+ey+ [ (Cca. Y-ccp.y)/k 1].
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| CN110824525A (en) * | 2019-11-15 | 2020-02-21 | 中冶华天工程技术有限公司 | Self-positioning method of robot |
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Family Cites Families (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPS60128506A (en) * | 1983-12-16 | 1985-07-09 | Fujitsu Ltd | Calibrating method of robot coordinate system |
| CN101630409B (en) * | 2009-08-17 | 2011-07-27 | 北京航空航天大学 | Hand-eye vision calibration method for robot hole boring system |
| CN102294695A (en) * | 2010-06-25 | 2011-12-28 | 鸿富锦精密工业（深圳）有限公司 | Robot calibration method and calibration system |
| CN102221331B (en) * | 2011-04-11 | 2012-06-27 | 浙江大学 | Measuring method based on asymmetric binocular stereovision technology |
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