Global visual-motor estimation for uncalibrated visual servoing

Farahmand, Amir-massoud; Shademan, Azad; Jägersand, Martin

doi:10.1109/iros.2007.4399506

Cited by 34 publications

(33 citation statements)

References 13 publications

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“…To do away with this dependence, one could optimize for the parameters in the image Jacobian while error in the image plane is being minimized. This is done for instance, using Gauss-Newton to minimize squared image error and nonlinear least squares optimization for the image Jacobian [9], [10]; using weighted recursive least squares (RLS), not to obtain the true parameters, but instead an approximation that still guarantees asymptotic stability of the control law in the sense of Lyaponov [11]; or using k-nearest neighbor regression to store previously estimated local models or previous movements, and estimating the Jacobian using local least squares (LLS) [12]. To provide robustness to outliers in the computation of the Jacobian, [13] proposes the use of an M-estimator.…”

Section: Related Workmentioning

confidence: 99%

Uncalibrated image-based visual servoing

Santamaria-Navarro

Andrade‐Cetto

2013

2013 IEEE International Conference on Robotics and Automation

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Abstract-This paper develops a new method for uncalibrated image-based visual servoing. In contrast to traditional image-based visual servo, the proposed solution does not require a known value of camera focal length for the computation of the image Jacobian. Instead, it is estimated at run time from the observation of the tracked target. The technique is shown to outperform classical visual servoing schemes in situations with noisy calibration parameters and for unexpected changes in the camera zoom. The method's performance is demonstrated both in simulation experiments and in a ROS implementation of a quadrotor servoing task. The developed solution is tightly integrated with ROS and is made available as part of the IRI ROS stack.

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Section: Related Workmentioning

confidence: 99%

Uncalibrated image-based visual servoing

Santamaria-Navarro

Andrade‐Cetto

2013

2013 IEEE International Conference on Robotics and Automation

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“…It is worth also mentioning the Kalman method where the system is modelled by its state variables which are updated using Kalman filter equations [15], the Broyden method that recursively update the Jacobian by using the last movement and the previous Jacobian [5]. Recently, it was presented two new methods of estimating the global visual-motor Jacobian [7], the first one is a K-nearest neighbourhood regressor over Jacobian that uses previously estimated local models, the second method stores previous movements and computes an estimate of the Jacobian by solving a local least squares problem.…”

Section: A Multiple View Jacobianmentioning

confidence: 99%

A comparative study between analytic and estimated image Jacobian by using a stereoscopic system of cameras

Pari

Sebastián

Traslosheros

et al. 2010

2010 IEEE/RSJ International Conference on Intelligent Robots and Systems

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Abstract-This paper describes a comparative study of performance between the estimated image Jacobian that come from taking into account the epipolar geometry in a system of two cameras, and the well known analytic image Jacobian that is utilized for most applications in visual servoing. Image Based Visual Servoing architecture is used for controlling a 3 DOF articular system using two cameras in eye to hand configuration. Tests in static and dynamic cases were carried out, and showed that the performance of estimated Jacobian by using the properties of the epipolar geometry is such as good and robust against noise as the analytic Jacobian. This fact is considered as an advantage because the estimated Jacobian does not need laborious previous work prior to control task in contrast to the analytic Jacobian does.

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“…This nonparametric method fits the weighted best hyperplane in the neighborhood of the query point S and returnsF (S) (See Section 5.4 of [13]). See [14] and [15] for similar approaches.…”

Section: A Locally Linear Regression For Model Estimationmentioning

confidence: 99%

Model-based and model-free reinforcement learning for visual servoing

Farahmand

Shademan

Jägersand

et al. 2009

2009 IEEE International Conference on Robotics and Automation

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Abstract-To address the difficulty of designing a controller for complex visual-servoing tasks, two learning-based uncalibrated approaches are introduced. The first method starts by building an estimated model for the visual-motor forward kinematic of the vision-robot system by a locally linear regression method. Afterwards, it uses a reinforcement learning method named Regularized Fitted Q-Iteration to find a controller (i.e. policy) for the system (model-based RL). The second method directly uses samples coming from the robot without building any intermediate model (model-free RL). The simulation results show that both methods perform comparably well despite not having any a priori knowledge about the robot.

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Global visual-motor estimation for uncalibrated visual servoing

Cited by 34 publications

References 13 publications

Uncalibrated image-based visual servoing

Uncalibrated image-based visual servoing

A comparative study between analytic and estimated image Jacobian by using a stereoscopic system of cameras

Model-based and model-free reinforcement learning for visual servoing

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