Hand/eye calibration for electronic assembly robots

Zhuang, Hanqi

doi:10.1109/robot.1997.614323

Cited by 12 publications

(9 citation statements)

References 14 publications

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“…It is known that the sought unique solution X can be found if the pose of the object can be measured using additional robot configurations, or if the pose of another object is measured. [1][2][3][4][5][6][7] In both cases, it is possible to write a second equation…”

Section: General Properties Of the Equation Ax = Xbmentioning

confidence: 99%

“…A sensor mounted on a robot gripper is sometimes used to measure the position of one body. [1][2][3][4][5][6][7][8] The sensor should be able to measure the pose of the body ͑position and orientation͒ with respect to an intrinsic frame defined on the sensor. Practical applications include ͑but are not restricted to͒ robot calibration, localization of a mechanical part, or self-localization of mobile robots.…”

Section: Introductionmentioning

confidence: 99%

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Hand to sensor calibration: A geometrical interpretation of the matrix equation

Fassi¹,

Legnani

2005

J. Robotic Syst.

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In this paper, the matrix equation AX = XB used for hand to sensor calibration of robotmounted sensors is analyzed using a geometrical approach. The analysis leads to an original way to describe the properties of the equation and to find all of its solutions. It will also be highlighted why, when multiple instances A i X = XB i ͑i =1,2, . . .͒ of the equation are to be solved simultaneously, the system is overconstrained. Finally, singular cases are also discussed.

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Section: General Properties Of the Equation Ax = Xbmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Hand to sensor calibration: A geometrical interpretation of the matrix equation

Fassi¹,

Legnani

2005

J. Robotic Syst.

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“…Extrinsic parameter estimation during camera calibration or pose estimation are used to determine cam T grid and the robot's forward kinematics are used to determine base T robot from synchronised joint encoder data. The calibration object can have different designs, for example, [8], [9], [10], [11], [12], [13] use a planar checker board whereas [14], [15], [16] use a uniform planar grid of black circles. The principle behind estimating the hand-eye transformation using these objects is that because their physical dimensions are known in advance, it is possible to estimate the extrinsic parameters of the camera with respect to the object directly from images [17], [18].…”

Section: Introductionmentioning

confidence: 99%

Hand-eye calibration for robotic assisted minimally invasive surgery without a calibration object

Pachtrachai

Allan

Pawar

et al. 2016

2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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In a robot mounted camera arrangement, handeye calibration estimates the rigid relationship between the robot and camera coordinate frames. Most hand-eye calibration techniques use a calibration object to estimate the relative transformation of the camera in several views of the calibration object and link these to the forward kinematics of the robot to compute the hand-eye transformation. Such approaches achieve good accuracy for general use but for applications such as robotic assisted minimally invasive surgery, acquiring a calibration sequence multiple times during a procedure is not practical. In this paper, we present a new approach to tackle the problem by using the robotic surgical instruments as the calibration object with well known geometry from CAD models used for manufacturing. Our approach removes the requirement of a custom sterile calibration object to be used in the operating room and it simplifies the process of acquiring calibration data when the laparoscope is constrained to move around a remote centre of motion. This is the first demonstration of the feasibility to perform hand-eye calibration using components of the robotic system itself and we show promising validation results on synthetic data as well as data acquired with the da Vinci Research Kit.

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“…Euclidean group [7], [9], [10], quaternion [11]- [13] and dual quaternion [14]- [16]. Constraints for the calibration optimisation can be developed, such as using epipolar constraints [17], using pure rotational movements [18], performing handeye calibration for fewer unknown parameters for SCARA robot [19] or using global optimisation methods [20]. Such numerous approached solve the problem with different numerical stability, but critically they all rely on full synchronisation between the two data streams.…”

mentioning

confidence: 99%

CHESS—Calibrating the Hand-Eye Matrix With Screw Constraints and Synchronization

Pachtrachai

Vasconcelos

Dwyer

et al. 2018

IEEE Robot. Autom. Lett.

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Hand-eye calibration is a classic problem in robotics that aims to find the transformation between two rigidly attached reference frames, usually a camera and a robot end-effector or a motion tracker. Most hand-eye calibration techniques require two data streams, one containing the eye (camera) motion and the other containing the hand (robot/tracker) motion, and the classic hand-eye formulation assumes that both data streams are fully synchronized. However, different motion capturing devices and cameras often have variable capture rates and timestamps that cannot always be easily triggered in sync. Although probabilistic approaches have been proposed to solve for nonsynchronized data streams, they are not able to deal with different capture rates. We propose a new approach for unsynchronized hand-eye calibration that is able to deal with different capture rates and time delays. Our method interpolates and resamples the signal with the lowest capture rate in a way that is consistent with the twist motion constraints of the hand-eye problem. Cross-correlation techniques are then used to produce two fully synchronized data streams that can be used to solve the hand-eye problem with classic methods. In our experimental section, we show promising validation results on simulation data and also on real data obtained from a robotic arm holding a camera. Index Terms-Calibration and identification, kinematics. I. INTRODUCTION S ENSORS and vision systems are often integrated into robotic platforms to provide extra information of the surroundings which helps in localisation of the workspace [1]. An example in robotics-assisted minimally invasive surgery (RMIS) is stereo-laparoscopic cameras or GPS in mobile vehicles for various types of simultaneous localization and mapping (SLAM) applications [2]. For RMIS, accurate and real-time localisation of an operative site with a laparoscope is an important component towards developing new capabilities in robotic surgery such as autonomy with visual servoing or surgical

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Hand/eye calibration for electronic assembly robots

Cited by 12 publications

References 14 publications

Hand to sensor calibration: A geometrical interpretation of the matrix equation

Hand to sensor calibration: A geometrical interpretation of the matrix equation

Hand-eye calibration for robotic assisted minimally invasive surgery without a calibration object

CHESS—Calibrating the Hand-Eye Matrix With Screw Constraints and Synchronization

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