A closed-form solution to eye-to-hand calibration towards visual grasping

Pan, Hui; Wang, Na Li; Qin, Yin Shi

doi:10.1108/ir-07-2014-0365

Cited by 18 publications

(7 citation statements)

References 20 publications

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“…Alternatively, Liang and Mao [ 13 ] applied the Kronecker product to get the homogeneous linear equation for the rotation matrix. Pan et al [ 14 ] and Najifi et al [ 15 ] also presented similar work. However, all the aforementioned methods separated the orientation component from the positional component, and errors in the rotation estimation could be propagated into the translational estimations.…”

Section: Introductionmentioning

confidence: 53%

A computationally efficient method for hand–eye calibration

2017

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PurposeSurgical robots with cooperative control and semiautonomous features have shown increasing clinical potential, particularly for repetitive tasks under imaging and vision guidance. Effective performance of an autonomous task requires accurate hand–eye calibration so that the transformation between the robot coordinate frame and the camera coordinates is well defined. In practice, due to changes in surgical instruments, online hand–eye calibration must be performed regularly. In order to ensure seamless execution of the surgical procedure without affecting the normal surgical workflow, it is important to derive fast and efficient hand–eye calibration methods.MethodsWe present a computationally efficient iterative method for hand–eye calibration. In this method, dual quaternion is introduced to represent the rigid transformation, and a two-step iterative method is proposed to recover the real and dual parts of the dual quaternion simultaneously, and thus the estimation of rotation and translation of the transformation.ResultsThe proposed method was applied to determine the rigid transformation between the stereo laparoscope and the robot manipulator. Promising experimental and simulation results have shown significant convergence speed improvement to 3 iterations from larger than 30 with regard to standard optimization method, which illustrates the effectiveness and efficiency of the proposed method.

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Section: Introductionmentioning

confidence: 53%

A computationally efficient method for hand–eye calibration

2017

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“…Target workpiece coordinate system is {Obj}, and the pose TitalicObjW of the target is unknown. Through eye-to-hand calibration (Pan, Wang and Qin, 2014), the transformation matrix TBCam between {B}and {Cam} can be determined. The relationship between {TCP} and {B} TitalicTCPB can be obtained through calibration of the tool coordinate system and known end pose of the robot.…”

Section: Problem Formulationmentioning

confidence: 99%

Pose estimation of metal workpieces based on RPM-Net for robot grasping from point cloud

Chen

Zhang

2022

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Purpose Many metal workpieces have the characteristics of less texture, symmetry and reflectivity, which presents a challenge to existing pose estimation methods. The purpose of this paper is to propose a pose estimation method for grasping metal workpieces by industrial robots. Design/methodology/approach Dual-hypothesis robust point matching registration network (RPM-Net) is proposed to estimate pose from point cloud. The proposed method uses the Point Cloud Library (PCL) to segment workpiece point cloud from scenes and a trained-well robust point matching registration network to estimate pose through dual-hypothesis point cloud registration. Findings In the experiment section, an experimental platform is built, which contains a six-axis industrial robot, a binocular structured-light sensor. A data set that contains three subsets is set up on the experimental platform. After training with the emulation data set, the dual-hypothesis RPM-Net is tested on the experimental data set, and the success rates of the three real data sets are 94.0%, 92.0% and 96.0%, respectively. Originality/value The contributions are as follows: first, dual-hypothesis RPM-Net is proposed which can realize the pose estimation of discrete and less-textured metal workpieces from point cloud, and second, a method of making training data sets is proposed using only CAD models with the visualization algorithm of the PCL.

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“…In the “eye to hand”-based hand–eye coordination operation system, the camera and the manipulator are installed separately, which can help obtain image-related information of the fruit over a larger field of view, where it is easy to realize visual feedback control, but in this system, the movement of the manipulator causes the target object to be occluded. When the environment changes or the camera moves, the latter needs to be recalibrated [ 9 , 10 ]. The accuracy of the visual system and the mechanical system also affect the rate of success of operation, operational accuracy, and cost.…”

Section: Introductionmentioning

confidence: 99%

A “Global–Local” Visual Servo System for Picking Manipulators

Shi

Zhang

et al. 2020

Sensors

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During the process of automated crop picking, the two hand–eye coordination operation systems, namely “eye to hand” and “eye in hand” have their respective advantages and disadvantages. It is challenging to simultaneously consider both the operational accuracy and the speed of a manipulator. In response to this problem, this study constructs a “global–local” visual servo picking system based on a prototype of a picking robot to provide a global field of vision (through binocular vision) and carry out the picking operation using the monocular visual servo. Using tomato picking as an example, experiments were conducted to obtain the accuracies of judgment and range of fruit maturity, and the scenario of fruit-bearing was simulated over an area where the operation was ongoing to examine the rate of success of the system in terms of continuous fruit picking. The results show that the global–local visual servo picking system had an average accuracy of correctly judging fruit maturity of 92.8%, average error of fruit distance measurement in the range 0.485 cm, average time for continuous fruit picking of 20.06 s, and average success rate of picking of 92.45%.

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A closed-form solution to eye-to-hand calibration towards visual grasping

Cited by 18 publications

References 20 publications

A computationally efficient method for hand–eye calibration

A computationally efficient method for hand–eye calibration

Pose estimation of metal workpieces based on RPM-Net for robot grasping from point cloud

A “Global–Local” Visual Servo System for Picking Manipulators

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