Adaptive Homography-Based Visual Servo Tracking Control via a Quaternion Formulation

For the monocular camera-based mobile robot system, an adaptive hybrid visual servo regulation algorithm which is based on a fast homography decomposition method is proposed to drive the mobile robot to its desired position and orientation, even when object's imaging depth and camera's position extrinsic parameters are unknown. Firstly, the homography's particular properties caused by mobile robot's 2-DOF motion are taken into account to induce a fast homography decomposition method. Secondly, the homography matrix and the extracted orientation error, incorporated with the desired view's single feature point, are utilized to form an error vector and its open-loop error function. Finally, Lyapunov-based techniques are exploited to construct an adaptive regulation control law, followed by the experimental verification. The experimental results show that the proposed fast homography decomposition method is not only simple and efficient, but also highly precise. Meanwhile, the designed control law can well enable mobile robot position and orientation regulation despite the lack of depth information and camera's position extrinsic parameters.

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“…Obviously, when the optical axis is perpendicular to the rotation axis of the robot,k = −e 2 = [0 −1 0] T . According to (10) and (11),…”

Section: Propertymentioning

confidence: 99%

“…The visual servo of mobile robots includes visual tracking 2 Journal of Control Science and Engineering and visual regulation. References [8][9][10] conducted in-depth research on visual tracking. However, in system stability analysis, the speed and path of the robot need to be constrained.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Hybrid Visual Servo Regulation of Mobile Robots Based on Fast Homography Decomposition

Chun-Fu

Qing-shun

et al. 2015

Journal of Control Science and Engineering

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“…Image for mation, rigid body motion, and projective geometry are now well-understood [15], [18], as are the significant difficulties in computing solutions to the related inverse problems. A number of papers in the control field work towards the goal of linking control-theoretic design to an understanding of geometric image formation, with a par ticular focus on homography decomposition and depth estimation from correspondence of projected points [2], [5], [6], [8], [9], [14], [17]. Feedback terms used in these works require as input a stream of solutions to inverse problems in computer vision, such as reliable feature-correspondence and homography-decomposition for rigid pose estimation.…”

Section: Introductionmentioning

confidence: 99%

Depth invariant visual servoing

Karasev

Serrano

Vela

et al. 2011

IEEE Conference on Decision and Control and European Control Conference

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This paper studies the problem of achieving consistent performance for visual servoing. Given the nonlinearities introduced by the camera projection equa tions in monocular visual servoing systems, many control algorithms experience non-uniform performance bounds.The variable performance bounds arise from depth de pendence in the error rates. In order to guarantee depth invariant performance bounds, the depth nonlinearity must be cancelled, however estimating distance along the optical axis is problematic when faced with an object with unknown geometry. By tracking a planar visual feature on a given target, and measuring the area of the planar feature, a distance invariant input to state stable visual servoing controller is derived. Two approaches are given for achieving the visual tracking. Both of these approaches avoid the need to maintain long-term tracks of individual feature points. Realistic image uncertainty is captured in experimental tests that control the camera motion in a 3D renderer using the observed image data for feedback.

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“…[1], [2], [3], [4], [5], [6], [7]) have many desirable traits in comparison other image-based and position based control methods including: a nonsingular image-Jacobian, realizable Euclidean camera trajectories, and the error system includes both the camera position and orientation, and the feature point coordinates. However, a key issue that is pervasive in homography based techniques lies in the projection from the Euclidean-space to the image-space.…”

Section: Introductionmentioning

confidence: 99%

Inverse optimal homography-based visual servo control via an uncalibrated camera

Johnson

Dupree

et al. 2009

Proceedings of the 48h IEEE Conference on Decision and Control (CDC) Held Jointly With 2009 28th Chinese Control Conference

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A robust inverse optimal adaptive visual servo controller is described that asymptotically regulates a camera's pose to a desired pose despite uncertainty in the time-varying distance from the camera to the target and parametric uncertainty in the camera calibration matrix. A high gain robust inverse optimal controller asymptotically stabilizes the rotation error system, whereas, an inverse optimal adaptive controller is shown to stabilize the translation error by compensating for the unknown depth and camera calibration parameters. A Lyapunov-based stability analysis is used to examine the stability and an inverse optimal analysis is used to determine the optimality of the developed controller.

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Adaptive Homography-Based Visual Servo Tracking Control via a Quaternion Formulation

Cited by 59 publications

References 23 publications

Adaptive Hybrid Visual Servo Regulation of Mobile Robots Based on Fast Homography Decomposition

Adaptive Hybrid Visual Servo Regulation of Mobile Robots Based on Fast Homography Decomposition

Depth invariant visual servoing

Inverse optimal homography-based visual servo control via an uncalibrated camera

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