Robotic eye-in-hand calibration by calibrating optical axis and target pattern

Chen, Chichyang; Stitt, Steven; Zheng, Yuan F.

doi:10.1007/bf01258384

Cited by 7 publications

(5 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The stereo vision data is recorded at 25 frames per second with a resolution of 720 × 576. Chessboard calibration [22] is utilized to estimate the rigid transform relationship between the frames of the PSMs' end-effectors and the coordinate frames of the stereo images.…”

Section: (Master Side) (Slave Side)mentioning

confidence: 99%

A Self-Adaptive Motion Scaling Framework for Surgical Robot Remote Control

Zhang

Xiao

Huang

et al. 2019

IEEE Robot. Autom. Lett.

View full text Add to dashboard Cite

Master-slave control is a common form of humanrobot interaction for robotic surgery. To ensure seamless and intuitive control, a mechanism of self-adaptive motion scaling during teleoperaton is proposed in this paper. The operator can retain precise control when conducting delicate or complex manipulation, while the movement to a remote target is accelerated via adaptive motion scaling. The proposed framework consists of three components: 1) situation awareness, 2) skill level awareness, and 3) task awareness. The self-adaptive motion scaling ratio allows the operators to perform surgical tasks with high efficiency, forgoing the need of frequent clutching and instrument repositioning. The proposed framework has been verified on a da Vinci Research Kit (dVRK) to assess its usability and robustness. An in-house database is constructed for offline model training and parameter estimation, including both the kinematic data obtained from the robot and visual cues captured through the endoscope. Detailed user studies indicate that a suitable motion-scaling ratio can be obtained and adjusted online. The overall performance of the operators in terms of control efficiency and task completion is significantly improved with the proposed framework.Index Terms-Learning and adaptive systems, telerobotics and teleoperation, medical robots and systems.

show abstract

Section: (Master Side) (Slave Side)mentioning

confidence: 99%

A Self-Adaptive Motion Scaling Framework for Surgical Robot Remote Control

Zhang

Xiao

Huang

et al. 2019

IEEE Robot. Autom. Lett.

View full text Add to dashboard Cite

show abstract

“…where A is the relative motion between F robot and a fixed reference frame at the base of the robot F base and is determined using forward kinematics [38], B represents the relative motion between F cam and an observed reference frame, normally a chessboard calibration grid [8], F grid . These transforms can be written as the product of two rigid transformations:…”

Section: A Hand-eye Calibrationmentioning

confidence: 99%

“…where τ and τ are discrete time values indicating that the two transforms are captured at different time instances, as shown in Figure 2(a), where a solution exists when the two transforms have non-parallel rotation axes [8]. For the hand-eye problem, obtaining a wide range of camera to calibration target transforms increases the accuracy of the calibration and the error in estimating the rotation part of the transform is inversely proportional to the sine value of relative rotation axes [15].…”

Section: A Hand-eye Calibrationmentioning

confidence: 99%

“…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%

“…The physical geometry of the instruments is known in 3D from manufacturing and computer aided design (CAD) models are available. The laparoscopic camera can be controlled through the robotic system, which decreases the sensitivity of the calibration procedure to noise [8], [9], [28]. This means that numerous methods for instrument tracking in 3D from the laparoscopic video can be used as an initial platform for automated hand-eye calibration.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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)

View full text Add to dashboard Cite

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.

show abstract

Application of visual tracking for robot‐assisted laparoscopic surgery

Zhang¹,

Payandeh²

2002

J. Robotic Syst.

View full text Add to dashboard Cite

With the increasing popularity of laparoscopic surgery, the demand for better modes of laparoscopic surgery also increases. The current laparoscopic surgery mode requires an assistant to hold and manipulate the endoscope through commands from the surgeon. However, during lengthy surgery procedures, accurate and on‐time adjustment of the camera cannot be guaranteed due to the fatigue and hand trembling of the camera assistant. This article proposes a practical visual tracking method to achieve automated instrument localization and endoscope maneuvering in robot‐assisted laparoscopic surgery. Solutions concerning this approach, such as, endoscope calibration, marker design, distortion correction, and endoscope manipulator design are described in detail. Experimental results are presented to show the feasibility of the proposed method. © 2002 Wiley Periodicals, Inc.

show abstract

Robotic eye-in-hand calibration by calibrating optical axis and target pattern

Cited by 7 publications

References 11 publications

A Self-Adaptive Motion Scaling Framework for Surgical Robot Remote Control

A Self-Adaptive Motion Scaling Framework for Surgical Robot Remote Control

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

Application of visual tracking for robot‐assisted laparoscopic surgery

Contact Info

Product

Resources

About