A Switched Systems Framework for Guaranteed Convergence of Image-Based Observers With Intermittent Measurements

Parikh, Anup; Cheng, Teng-Hu; Chen, Hsi-Yuan; Dixon, Warren E.

doi:10.1109/tro.2016.2627024

Cited by 28 publications

(20 citation statements)

References 84 publications

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“…However, the tracking errors may still result in the feature points leaving the FoV. Parikh et al (2017) provide a different perspective on this issue for reference. They investigate the state estimation without visual feedback when the feature points are out of the FoV by means of switched systems.…”

Section: Future Directions For Researchmentioning

confidence: 99%

2.5D Vision-Based Estimation

Chen¹

2020

Encyclopedia of Systems and Control

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2.5D vision-based techniques, also known as hybrid vision-based techniques, provide flexible ways to estimate the range or velocity of moving objects. The information from both the image space (2D) and the Cartesian space (3D) is simultaneously utilized to construct the system state in this technology, which overcomes the disadvantages of the traditional visual serving schemes. It has been widely adopted in Motion from structure, structure from motion, and structure and motion problems.

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Section: Future Directions For Researchmentioning

confidence: 99%

2.5D Vision-Based Estimation

Chen¹

2020

Encyclopedia of Systems and Control

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“…The results ensure to the image state estimator convergence to reasonable bound under visual intermittent measurement. On the basis of [22], [23] designs an observer and predictor for the image feature outside the camera FOV. When the image features are unavailable, the target motion state can be estimated by switching system between the observer and the predictor.…”

Section: Introductionmentioning

confidence: 99%

“…When the target feature lost, the preorder observation sequence is used to determine the forgetting factor for estimating the missing visual states. Compared with [22] and [23], the proposed method solves the tracking problem in the case of temporary and complete occlusion of image features by solving one parameter (the forgetting factor) without additional design of trajectory predictor. The advantage of this method is convenient for applying to a real robot platform.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Visual Tracking for Robot Manipulator Using Adaptive Fading Kalman Filter

Zhang

Tang

et al. 2020

IEEE Access

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This paper focuses on the problem of visual tracking of a moving target with the temporary occlusion of image feature, a dynamic visual tracking control system for robot manipulator is developed by using adaptive fading Kalman filter (AFKF). The estimation of the residual covariance is used to compute the forgetting factor to automatically adjust the weight of the image observation data for improving the visual state estimation accuracy. When the target features are occluded, the prediction of missing observation sequence are generated by using the predicted compensation noise and preorder observation sequence to determine the forgetting factor for estimating the missing visual states. Then, a parameter adaptive law with projection error compensation is designed to realize the visual tracking with uncertain camera parameters. Finally, the trajectory tracking experiments based on a real robot platform is carried out to verify the performance of the proposed state estimator and tracking controller. The results show that the proposed method can accurately realize the visual tracking with the occluded trajectory and inaccurate camera parameters, which improves the flexibility of dynamic visual tracking of robot manipulator. INDEX TERMS Visual tracking, robot manipulation, Kalman filter, occlusion.

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“…[17]- [19] present the development of dwell time conditions to guarantee that the state estimate error converges to an ultimate bound under intermittent measurement. In [17]- [19], the estimation is based on the knowledge about the velocity of the moving object and the camera. However, in practice the velocity of the target is usually unknown, and modeling its dynamics is complicated and challenging.…”

Section: Introductionmentioning

confidence: 99%

Estimation and Tracking of a Moving Target by Unmanned Aerial Vehicles

Chen

Cheng

2019

2019 American Control Conference (ACC)

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An image-based control strategy along with estimation of target motion is developed to track dynamic targets without motion constraints. To the best of our knowledge, this is the first work that utilizes a bounding box as image features for tracking control and estimation of dynamic target without motion constraint. The features generated from a You-Only-Look-Once (YOLO) deep neural network can relax the assumption of continuous availability of the feature points in most literature and minimize the gap for applications. The challenges are that the motion pattern of the target is unknown and modeling its dynamics is infeasible. To resolve these issues, the dynamics of the target is modeled by a constant-velocity model and is employed as a process model in the Unscented Kalman Filter (UKF), but process noise is uncertain and sensitive to system instability. To ensure convergence of the estimate error, the noise covariance matrix is estimated according to history data within a moving window. The estimated motion from the UKF is implemented as a feedforward term in the developed controller, so that tracking performance is enhanced. Simulations are demonstrated to verify the efficacy of the developed estimator and controller.

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A Switched Systems Framework for Guaranteed Convergence of Image-Based Observers With Intermittent Measurements

Cited by 28 publications

References 84 publications

2.5D Vision-Based Estimation

2.5D Vision-Based Estimation

Dynamic Visual Tracking for Robot Manipulator Using Adaptive Fading Kalman Filter

Estimation and Tracking of a Moving Target by Unmanned Aerial Vehicles

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