CN108436909A - A kind of hand and eye calibrating method of camera and robot based on ROS - Google Patents
A kind of hand and eye calibrating method of camera and robot based on ROS Download PDFInfo
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- CN108436909A CN108436909A CN201810203675.4A CN201810203675A CN108436909A CN 108436909 A CN108436909 A CN 108436909A CN 201810203675 A CN201810203675 A CN 201810203675A CN 108436909 A CN108436909 A CN 108436909A
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
- B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
- B25J9/00—Programme-controlled manipulators
- B25J9/16—Programme controls
- B25J9/1679—Programme controls characterised by the tasks executed
- B25J9/1692—Calibration of manipulator
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
- B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
- B25J9/00—Programme-controlled manipulators
- B25J9/16—Programme controls
- B25J9/1602—Programme controls characterised by the control system, structure, architecture
- B25J9/161—Hardware, e.g. neural networks, fuzzy logic, interfaces, processor
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
- B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
- B25J9/00—Programme-controlled manipulators
- B25J9/16—Programme controls
- B25J9/1694—Programme controls characterised by use of sensors other than normal servo-feedback from position, speed or acceleration sensors, perception control, multi-sensor controlled systems, sensor fusion
- B25J9/1697—Vision controlled systems
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Abstract
The invention discloses a kind of hand and eye calibrating methods of the camera based on ROS and robot, by building vision system, make mechanical arm and its operation object within the scope of camera fields of view, then camera and mechanical arm and the communication mechanism of ROS are established respectively, on the one hand the motion control of mechanical arm is realized, on the one hand camera image and mechanical arm state are obtained, carry out the acquisition of data, then internal reference and the calibration of outer ginseng of camera are carried out, obtain camera parameter, finally according to the automatic hand and eye calibrating for carrying out camera and mechanical arm of the difference of camera mounting means, hand and eye calibrating matrix is obtained.The present invention realizes the automatic hand and eye calibrating of camera and mechanical arm, reduce manual intervention, entire calibration process only needs to call station command twice, autonomy is high, does not limit camera type and quantity, does not limit mechanical arm type, scalability is strong, calibration process only needs to print the chessboard ruled paper of an A4, need not make any label, convenient and practical.
Description
Technical field
The present invention relates to a kind of robot vision method more particularly to a kind of tricks of camera and robot based on ROS
Scaling method.
Background technology
With the development of computer technology, an important research field of the computer vision as artificial intelligence, extensively
It is applied to all trades and professions generally.Computer vision technique is combined with robot technology, but also field in intelligent robotics obtains
To greatly developing.For mechanical arm capture, traditionally by the way of artificial teaching, if hand breaks mechanical arm, make mechanical arm to certain
A fixed position is captured, and this mode is more inefficient and since mechanical arm has no sensing capability to ambient enviroment, if
Mechanical arm position or object space change, and mechanical arm is then grabbed less than object.
Computer vision is applied to robot field, typically at binding pattern identification and the method analysis of image procossing
Image data is managed, spatial position and the posture of target object is obtained, has the pose of target object, robot can be autonomous
Ground carries out path planning.
And coordinate of the object under camera coordinates system is transformed under mechanical arm coordinate system, it is referred to as camera and robot
Hand and eye calibrating, this is important prerequisite and the basis that mechanical arm crawl is carried out using computer vision.According to camera installation site
Difference is generally divided into two kinds：On the robotic arm, camera is dynamic with mechanical arm for installation, referred to as eye at (eye in hand) on hand,
Outside mechanical arm, camera is not with manipulator motion, referred to as eye (eye to hand) outside hand.And general hand and eye calibrating
Process is complicated, needs manual intervention, or takes index point or manual record data, it has not been convenient to use, and be used in visual experiment
Many when hand and eye calibrating, either camera position or mechanical arm position changes, camera type or mechanical arm type hair
When changing, hand and eye calibrating will be re-started, and traditional hand and eye calibrating is time-consuming longer, has elongated visual experiment significantly
Period.
Invention content
The purpose of the present invention is to provide a kind of hand and eye calibrating methods of the camera based on ROS and robot.
Realize that the technical solution of the object of the invention is：A kind of hand and eye calibrating method of camera and robot based on ROS,
Include the following steps：
Step 1 builds vision system, makes mechanical arm and its operation object within the scope of camera fields of view；
Step 2 establishes camera and ROS, the communication mechanism of mechanical arm and ROS respectively；
Step 3, the motion control for realizing mechanical arm, while obtaining camera image and mechanical arm state；
Step 4 repeats step 3, when obtaining m camera image and mechanical arm state, executes step 5, otherwise executes step
Rapid 3；
Step 5, the internal reference that camera is carried out according to the camera image of acquisition and the calibration of outer ginseng, obtain camera parameter；
Step 6, the hand and eye calibrating that camera and mechanical arm are carried out according to camera mounting means, obtain hand and eye calibrating matrix.
The hand and eye calibrating method of camera and robot provided by the invention based on ROS, remarkable advantage are：Do not limit phase
Machine type and quantity do not limit mechanical arm type, and autgmentability is strong, and calibration process only needs to print the chessboard ruled paper of an A4,
It need not make any label, convenient and practical, entire calibration process only needs to call station command twice, and it is artificial not need other
Intervene, autonomy is high.
Present invention is further described in detail below in conjunction with the accompanying drawings.
Description of the drawings
Fig. 1 is that the present invention is based on the hand and eye calibrating method flow charts of the camera of ROS and robot.
Fig. 2 is the vision system schematic diagram built.
Relation schematic diagrams of the Fig. 3 between coordinate system.
Fig. 4 is using the schematic diagram for putting cloud under the point cloud to mechanical arm coordinate system under trick matrix conversion camera coordinates system.
Specific implementation mode
Below in conjunction with attached drawing to the present invention is based on the specific implementation modes of the camera of ROS and the hand and eye calibrating method of robot
It illustrates：
The hand and eye calibrating method flow chart of camera and robot based on ROS is as shown in Figure 1, include the following steps：
Step 1 builds vision system, makes mechanical arm and its operation object within the scope of camera fields of view；It is as follows：
Step 1-1, the camera for being generally used for auxiliary robot movement is binocular camera and depth camera, if camera is installed
Outside mechanical arm, it can select to install according to the parameter of camera itself, especially horizontal view angle, vertical angle of view and finding range
The position of camera；
Step 1-2, a scaling board (gridiron pattern or dot chart) is printed, is attached on one piece of flat hardboard.If
Eye is carried out in hand and eye calibrating on hand, then hardboard is fixed on to the position outside with a certain distance from mechanical arm；If carrying out eye to exist
Hand and eye calibrating outside hand, then be fixed on mechanical arm tail end by hardboard；
Step 2 establishes camera and ROS, the communication mechanism of mechanical arm and ROS respectively；It is as follows：
Step 2-1, driving packet of the installation camera at ROS, establishes camera and the communication of ROS, image data is published to
On the topic topic1 of ROS；
Step 2-2, driving packet of the installation mechanical arm at ROS, establishes mechanical arm and the communication of ROS, by mechanical arm tail end
Posture is published on topic topic2, and joint of mechanical arm state is published on topic topic3, and the control for providing mechanical arm connects
Mouth topic4；
Step 3, the motion control for realizing mechanical arm, while obtaining camera image and mechanical arm state；It is as follows：
Step 3-1, it subscribes to topic3 and obtains the current joint states joint_states of mechanical arm, by current joint shape
State is superimposed the random delta Δ joint_states of a small range generation as target joint position, i.e. joint_states_
Target sends joint instruction to topic4, the movement of control machinery arm；The variable being related to is specially：
Joint_states=[joint1,joint2,…,jointn]
Δ joint_states=[Δ joint1,Δjoint2,…,Δjointn]
Joint_states_target=joint_states+ Δs joint_states
Wherein, jointi(i=1,2 ... n) indicate the current state in i-th of joint, Δ jointi(i=1,2 ... n)
Indicate that i-th of joint needs the random delta being superimposed, n to indicate the joint number of mechanical arm；
Step 3-2, it after manipulator motion is to designated position, preserves Current camera and issues the RGB image on topic1,
Preserve mechanical arm tail end posture information under mechanical arm base coordinate system of the current publication on topic2
Wherein,Indicate rotational transformation matrix of the mechanical arm tail end relative to base coordinate system,Indicate mechanical arm tail end
Translation transformation matrix relative to base coordinate system；
Step 4, when obtaining m camera image and when mechanical arm state, execution step 5 is no to then follow the steps 3；
Step 5, the internal reference that camera is carried out according to the camera image of acquisition and the calibration of outer ginseng, obtain camera parameter；Specific step
It is rapid as follows：
Step 5-1, Corner Detection is carried out by the RGB image that step 3-2 is preserved respectively to m；
Step 5-2, internal reference and the calibration of outer ginseng of camera are carried out；Obtain internal reference matrix cameraMatrix, distortion factor
Pose [R of the scaling board relative to camera coordinates system in distCoeffs and each RGB imagec|tc]；
Wherein, internal reference matrixDistortion factor is distCoeffs=[k1,k2,
p1,p2,k3], fxAnd fyFor focal length, cxAnd cyFor the center point coordinate of imaging plane, k1、k2And k3Indicate coefficient of radial distortion, p1
And p2Indicate tangential distortion coefficient, RcAnd tcIt is expressed as indicating that scaling board becomes relative to the rotation of the RGB cameras of camera
It changes and translation transformation；
Step 6, the hand and eye calibrating that camera and mechanical arm are carried out according to camera mounting means, obtain hand and eye calibrating matrix；Tool
Steps are as follows for body：
Step 6-1, the m posture information of the m posture information and scaling board of mechanical arm tail end relative to camera is arranged,
Specially：
Grab2b=[[Rc|tc]1,[Rc|tc]2,...[Rc|tc]m]
Wherein, what grab2b was indicated is a series of posture informations of mechanical arm tail end, and what grid2c was indicated is scaling board phase
For a series of posture informations of camera；
Step 6-2, the problem of hand and eye calibrating acquisition hand and eye calibrating matrix X being carried out using two-step method, that is, solving AX=XB；
Wherein, X is the trick matrix finally to be solved, and A and B depend on the mounting means of camera, specific to indicate as follows：
Camera is installed on the robotic arm：
A=[grab2b [1]-1·grab2b[0],grab2b[2]-1·grab2b[1],...,grab2b[m-1]-1·
grab2b[m-2]]
B=[grid2c [1] grid2c [0]-1,grid2c[2]·grid2c[1]-1,...,grid2c[m-1]·
grid2c[m-2]-1]
Camera is mounted on outside mechanical arm：
A=[grab2b [1] grab2b [0]-1,grab2b[2]·grab2b[1]-1,...,grab2b[m-1]·
grab2b[m-2]-1]
B=[grid2c [1] grid2c [0]-1,grid2c[2]·grid2c[1]-1,...,grid2c[m-1]·
grid2c[m-2]-1]
Further detailed description is done to the present invention with reference to embodiment.
Embodiment
Vision system is built using ur3 mechanical arms and kinect2 depth cameras, wherein ur3 has 6 degree of freedom (joint),
Kinect2 is integrated with RGB cameras and Depth cameras, and kinect2 is mounted on the fixed position outside mechanical arm, it is contemplated that
The field range and finding range of kinect2, the schematic view of the mounting position of kinect2 and ur3 mechanical arms as shown in Fig. 2, and
The RGB camera coordinate systems O of kinect2 is markedc, ur3 mechanical arm pedestal coordinate systems Or, wherein RGB indicate respectively x,
Y, z-axis.
Scaling board is printed with A4 paper, the specification of scaling board selects chess5x7x0.03, as shown in Figure 2.
Relationship between the coordinate system used in entire calibration process as shown in figure 3, be related to 4 coordinate systems altogether：Machinery
Arm pedestal coordinate system Obase, mechanical arm tail end coordinate system Ograbber, scaling board coordinate system OgridAnd camera coordinates system Oc, Fig. 4
InIndicate mechanical arm tail end coordinate system OgrabberTo base coordinate system ObaseTransformation,Indicate that scaling board is sat
Mark system OgridTo camera coordinates system OcTransformation, be all measurable；And X indicates camera coordinates system OcTo mechanical arm base coordinate system
ObaseTransformation, that is, the trick matrix to be demarcated.
Driving packets of the kinect2 at ROS is installed, topic/kinect2/hd/image_ is sent image data to
color；Driving packets of the ur3 at ROS is installed, by the coordinate system transformation square of ur3 mechanical arm tail ends coordinate system to base coordinate system
6 joint angles states are published to topic/ur3/joint_ by battle array data real-time release to/ur3/grabber_states
Motion interface/ur_script of ur3 is arranged in states.
Assuming that joint of mechanical arm original state is：[1.9515,-1.0125,-1.8513,-0.1229,1.6139,
0.0290], each joint angles are superimposed the random delta in [- 0.2, a 0.2] range and are closed as mechanical arm target next time
Angle is saved, 6 target joint angles, which are sent to topic/ur_script control machinery arms end, reaches specified pose.Assuming that
Control machinery arm end movement is to 8 different poses in calibration process, after end reaches specified pose each time, by ordering
It reads topic/ur3/grabber_states and obtains pose of the current ur3 mechanical arm tail ends relative to base coordinate system, subscribe to words
Topic/ur3/joint_states obtains current ur3 currently each joint states, subscribes to topic/kinect2/hd/image_
Color obtains current RGB image.
The above process is the gatherer process of nominal data, and after 8 movements of mechanical arm, the data of preservation include：8 RGB figures
Picture and 8 mechanical arm tail end poses (relative to mechanical arm base coordinate system).
Zhang Zhengyou gridiron pattern standardizations, including Corner Detection and the calibration of inside and outside ginseng are used to 8 RGB images, obtain internal reference
Matrix and distortion factor are as follows：
DistCoeffs=[5.627e-02-7.420e-021.425e-03-1.695e-032.411e-02]
Outer ginseng is [Rc|tc]i(i=1,2 ... 8), wherein RcIndicate RGB camera of the scaling board relative to kinect2
Rotation transformation, tcIndicate translation transformation of the scaling board relative to the RGB cameras of kinect2.
It is relative to the pose of mechanical arm pedestal by the/ur3/grabber_states mechanical arm tail end obtained
WhereinIndicate rotation transformation square of the mechanical arm tail end relative to base coordinate system
Battle array,Indicate translation transformation matrix of the mechanical arm tail end relative to base coordinate system.
Therefore, it is as follows to obtain grab2b and grid2c difference for arrangement data：
Grab2b=[[Rc|tc]1,[Rc|tc]2,...[Rc|tc]8]
As can be seen that grab2b and grid2c indicate be 1x8 matrix
Since kinect2 is mounted on the fixed position outside ur3 mechanical arms, so A and B are expressed as：
A=[grab2b [1] grab2b [0]-1,grab2b[2]·grab2b[1]-1,...,grab2b[7]·
grab2b[6]-1]
B=[grid2c [1] grid2c [0]-1,grid2c[2]·grid2c[1]-1,...,grid2c[7]·
grid2c[6]-1]
AX=XB is solved equation using Tasi two-step methods, as a result X is as follows：
The point cloud under the visual angles kinect2 is transformed into mechanical arm coordinate system, display result such as Fig. 4 institutes using trick matrix
Show.
As long as it should be noted that determining camera mounting means, the topic listed in table 1 is set, program carries out automatically
Above example
Table 1
By above-described embodiment, it may indicate that the expansible row of hand and eye calibrating method that the present invention is carried is strong, different types of machine
Tool arm and different types of visual apparatus can use, and calibration process autonomy is high, does not need manual intervention,
The complex for reducing traditional hand and eye calibrating greatly accelerates the progress of visual experiment.
Claims (7)
1. a kind of hand and eye calibrating method of camera and robot based on ROS, which is characterized in that include the following steps：
Step 1 builds vision system, makes mechanical arm and its operation object within the scope of camera fields of view；
Step 2 establishes camera and ROS, the communication mechanism of mechanical arm and ROS respectively；
Step 3, the motion control for realizing mechanical arm, while obtaining camera image and mechanical arm state；
Step 4 repeats step 3, when obtaining m camera image and mechanical arm state, executes step 5, no to then follow the steps 3；
Step 5, the internal reference that camera is carried out according to the camera image of acquisition and the calibration of outer ginseng, obtain camera parameter；
Step 6, the hand and eye calibrating that camera and mechanical arm are carried out according to camera mounting means, obtain hand and eye calibrating matrix.
2. the hand and eye calibrating method of camera and robot according to claim 1 based on ROS, which is characterized in that step 2
The middle communication mechanism for establishing camera and ROS, mechanical arm and ROS respectively the specific steps are：
Step 2-1, driving packet of the installation camera at ROS, establishes camera and the communication of ROS, image data is published to ROS's
On topic topic1；
Step 2-2, driving packet of the installation mechanical arm at ROS, establishes mechanical arm and the communication of ROS, by mechanical arm tail end posture
It is published on topic topic2, joint of mechanical arm state is published on topic topic3, and provides the control interface of mechanical arm
topic4。
3. the hand and eye calibrating method of camera and robot according to claim 1 based on ROS, which is characterized in that step 3
It is middle to realize the motion control of mechanical arm, at the same obtain camera image and mechanical arm state the specific steps are：
Step 3-1, it subscribes to topic3 and obtains the current joint states joint_states of mechanical arm, current joint states are folded
Add the random delta Δ joint_states that a small range generates as target joint position joint_states_target, hair
Send joint instruction to topic4, the movement of control machinery arm, wherein the variable being related to is specially：
Joint_states=[joint1,joint2,…,jointn]
Δ joint_states=[Δ joint1,Δjoint2,…,Δjointn]
Joint_states_target=joint_states+ Δs joint_states
Wherein, jointi(i=1,2 ... n) indicate the current state in i-th of joint, Δ jointi(i=1,2 ... n) indicate
I-th of joint needs the random delta being superimposed, n to indicate the joint number of mechanical arm；
Step 3-2, it after manipulator motion is to designated position, preserves Current camera and issues the RGB image on topic1, preserve
Mechanical arm tail end posture information under mechanical arm base coordinate system of the current publication on topic2
Wherein,Indicate rotational transformation matrix of the mechanical arm tail end relative to base coordinate system,Indicate that mechanical arm tail end is opposite
In the translation transformation matrix of base coordinate system.
4. the hand and eye calibrating method of camera and robot according to claim 1 based on ROS, which is characterized in that step 4
Middle m >=8.
5. the hand and eye calibrating method of camera and robot according to claim 1 based on ROS, which is characterized in that step 5
Middle internal reference and the calibration of outer ginseng that camera is carried out according to the camera image of acquisition, obtains camera parameter, the specific steps are：
Step 5-1, Corner Detection is carried out by the RGB image that step 3-2 is preserved respectively to m；
Step 5-2, internal reference and the calibration of outer ginseng for carrying out camera, obtain internal reference matrix cameraMatrix, distortion factor
Pose [R of the scaling board relative to camera coordinates system in distCoeffs and each RGB imagec|tc]；
Wherein, internal reference matrixDistortion factor is distCoeffs=[k1,k2,p1,p2,
k3], fxAnd fyFor focal length, cxAnd cyFor the center point coordinate of imaging plane, k1、k2And k3Indicate coefficient of radial distortion, p1And p2Table
Show tangential distortion coefficient, RcAnd tcIt is expressed as indicating rotation transformation and translation of the scaling board relative to the RGB cameras of camera
Transformation.
6. the hand and eye calibrating method of camera and robot according to claim 1 based on ROS, which is characterized in that step 6
The middle hand and eye calibrating that camera and mechanical arm are carried out according to camera mounting means, obtains hand and eye calibrating matrix；The specific steps are：
Step 6-1, the m posture information of the m posture information and scaling board of mechanical arm tail end relative to camera is arranged, specifically
For：
Grab2b=[[Rc |tc]1,[Rc |tc]2,...[Rc |tc]m]
Wherein, what grab2b was indicated is m posture information of mechanical arm tail end, and what grid2c was indicated is scaling board relative to camera
M posture information；
Step 6-2, hand and eye calibrating is carried out using two-step method and obtains hand and eye calibrating matrix X.
7. the hand and eye calibrating method of camera and robot according to claim 6 based on ROS, which is characterized in that step
Hand and eye calibrating is carried out using two-step method in 6-2 and obtains hand and eye calibrating matrix X, that is, solves the problems, such as AX=XB, wherein X is last
The trick matrix to be solved, A and B depend on the mounting means of camera, specific to indicate as follows：
Camera is installed on the robotic arm：
A=[grab2b [1]-1·grab2b[0],grab2b[2]-1·grab2b[1],...,grab2b[m-1]-1·grab2b
[m-2]]
B=[grid2c [1] grid2c [0]-1,grid2c[2]·grid2c[1]-1,...,grid2c[m-1]·grid2c
[m-2]-1]
Camera is mounted on outside mechanical arm：
A=[grab2b [1] grab2b [0]-1,grab2b[2]·grab2b[1]-1,...,grab2b[m-1]·grab2b
[m-2]-1]
B=[grid2c [1] grid2c [0]-1,grid2c[2]·grid2c[1]-1,...,grid2c[m-1]·grid2c
[m-2]-1]。
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