Adaptive image feature prediction and control for visual tracking with a hand-eye coordinated camera

Feddema, John T.; Lee, C.S.G.

doi:10.1109/21.59979

Cited by 141 publications

(63 citation statements)

References 13 publications

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“…move along a line in 3 DOF) we have found that almost any controller based on the Newton method will work [15,16]. There are also many accounts of success in practical application for low DOF manipulation in the literature [4,21,2,11]. Robot arm control is also fairly insensitive to somewhat ill conditioned visual measurements, such as caused by bad camera placement, and the particular choice of visual features to track.…”

Section: Discussionmentioning

confidence: 99%

“…The hand manipulates a grasped object in approximate motor pose space 4 ¢ ¡ ¢ £ ¥ ¤ through the transformations derived in section 5, and observes the results of its actions as changes in a visual perception or "feature" vector…”

Section: Viewing Modelmentioning

confidence: 99%

“…[4,21]), or (3) determined experimentally by physically executing a set of orthogonal calibration movements (eg. [1,9]) and approximating the Jacobian with finite differences:…”

Section: Visual-motor Model Estimationmentioning

confidence: 99%

“…Visual servoing, when supplemented with online visual model estimation, fits into the active vision paradigm. Results with visual servoing and varying degrees of model adaption have been presented for robot arms [23,4,21,15,16,13,2,11] 2 . Visual models suitable for specifying visual alignments have also been studied [9,1,10], but it remains to be proven that the approach works on more complex manipulators than the serial link robot arm.…”

Section: Introductionmentioning

confidence: 99%

“…In addition to being able to do uncalibrated visual servo control, the online estimated models are useful for instance for: (1) Prediction and constraining search in visual tracking [16,4] (2) Doing local coordinate transformations between manipulator (joint), world and visual frames [15,16]. (3) Synthesizing views from other agent poses [14].…”

Section: Introductionmentioning

confidence: 99%

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Acquiring visual-motor models for precision manipulation with robot hands

Jägersand

Fuentes

Nelson

1996

Lecture Notes in Computer Science

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Abstract. Dextrous high degree of freedom (DOF) robotic hands provide versatile motions for fine manipulation of potentially very different objects. However, fine manipulation of an object grasped by a multifinger hand is much more complex than if the object is rigidly attached to a robot arm. Creating an accurate model is difficult if not impossible. We instead propose a combination of two techniques: the use of an approximate estimated motor model, based on the grasp tetrahedron acquired when grasping an object, and the use of visual feedback to achieve accurate fine manipulation. We present a novel active vision based algorithm for visual servoing, capable of learning the manipulator kinematics and camera calibration online while executing a manipulation task. The approach differs from previous work in that a full, coupled image Jacobian is estimated online without prior models, and that a trust region control method is used, improving stability and convergence. We present an extensive experimental evaluation of visual model acquisition and visual servoing in 3, 4 and 6 DOF.

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Section: Discussionmentioning

confidence: 99%

Section: Viewing Modelmentioning

confidence: 99%

“…[4,21]), or (3) determined experimentally by physically executing a set of orthogonal calibration movements (eg. [1,9]) and approximating the Jacobian with finite differences:…”

Section: Visual-motor Model Estimationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Acquiring visual-motor models for precision manipulation with robot hands

Jägersand

Fuentes

Nelson

1996

Lecture Notes in Computer Science

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Automated instrument tracking in robotically assisted laparoscopic surgery

Uecker

Lee

Wang

et al. 1995

J. Image Guid. Surg.

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This paper describes a practical and reliable image analysis and tracking algorithm to achieve automated instrument localization and scope maneuvering in robotically assisted laparoscopic surgery. Laparoscopy is a minimally invasive surgical procedure that utilizes multiple small incisions on the patient's body through which the surgeon inserts tools and a videoscope in order to conduct an operation. The scope relays images of internal organs to a camera, and the images are displayed on a video screen. The surgeon performs the operation by viewing the scope images rather than performing the traditional “open” procedure, where a large incision is made on the patient's body for direct viewing. The current mode of laparoscopy employs an assistant to hold the scope and position it in response to the surgeon's verbal commands. However, this results in suboptimal visual feedback, because the scope is often aimed incorrectly and vibrates due to hand trembling. We have developed a robotic laparoscope positioner to replace the assistant. The surgeon commands the robotic positioner through a hand/foot controller interface. To further simplify the human‐machine interface that controls the robotic scope positioner, we report here a novel scope‐positioning scheme using automated image analysis and robotic visual servoing. The scheme enables the surgeon to control visual feedback and to perform surgery more efficiently without requiring additional use of the hands. J Image Guid Surg 1:308–325 (1995). © 1996 Wiley‐Liss, Inc.

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Predicting a moving object position for visual servoing: theory and experiments

Gortcheva¹,

Garrido²,

González³

et al. 2001

Adaptive Control & Signal

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In order to perform visual servoing tasks in a robotic system, one is confronted with the low sampling rate of standard cameras and the time delay introduced by image processing. One way to circumvent the time-delay problem is to estimate future positions of the moving object of interest employing prediction techniques. In this work, three prediction techniques, namely Kalman "ltering and two adaptive techniques employing least squares with forgetting factor and the projection algorithm, respectively, are evaluated in terms of their prediction error. Experimental results show that the adaptive techniques give best results and the Kalman "lter-based predictor shows a high sensitivity to velocity changes of the moving object.

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Adaptive image feature prediction and control for visual tracking with a hand-eye coordinated camera

Cited by 141 publications

References 13 publications

Acquiring visual-motor models for precision manipulation with robot hands

Acquiring visual-motor models for precision manipulation with robot hands

Automated instrument tracking in robotically assisted laparoscopic surgery

Predicting a moving object position for visual servoing: theory and experiments

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