Range and Motion Estimation of a Monocular Camera Using Static and Moving Objects

Chwa, Dongkyoung; Dani, Ashwin P.; Dixon, Warren E.

doi:10.1109/tcst.2015.2508001

Cited by 53 publications

(51 citation statements)

References 44 publications

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“…Tracking with proposed controller: The controller proposed in (10) and (11) are employed in the simulation. Considering the sufficient conditions in (22), (25), (40), and (44), K sr , K pr , K st , and K pt can be first estimated approximately and then selected sufficiently large. After the tuning process, the control gain matrices are set to the following values:…”

Section: Simulation Results and Discussionmentioning

confidence: 99%

“…is larger than the right-hand side of (22). Therefore, the magnitude of K sr can be reduced with the introduction of the hyperbolic functions.…”

Section: Lemmamentioning

confidence: 99%

“…on condition that the control gain matrix K sr is adjusted according to (22), and the control gain matrix K pr is selected sufficiently large to satisfy…”

Section: Theoremmentioning

confidence: 99%

“…To ensure asymptotic convergence utilizing a continuous control input, the robust integral of the signum of the error (RISE)-based controller is presented in [21]. Recently, RISE-based feedback control has been exploited to solve different problems such as range estimation [22], velocity identification [23], unmanned aerial vehicle control [24,25], and autonomous underwater vehicle control [26]. Nevertheless, to the best of our knowledge, the RISE methodology has not been extended to address the vision-based control problem that involves tracking a moving object.This paper proposes a homography-based visual servo controller for a rigid body to track a moving object in 3-D space with a fixed relative pose (i.e., position and orientation) using visual feedback.…”

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confidence: 99%

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Asymptotic moving object tracking with trajectory tracking extension: A homography‐based approach

Zhang

Chen

Jia

2017

Intl J Robust & Nonlinear

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Summary In this paper, a homography‐based visual servo controller is developed for a rigid body to track a moving object in three‐dimensional space with a fixed relative pose. Specifically, a monocular camera is mounted on the rigid body, and the desired relative pose is expressed by a pre‐recorded reference image. Homography is exploited to obtain the orientation and scaled position for controller design. Considering the unknown moving object's velocities and distance information, a continuous nonlinear visual controller is developed using the robust integral of the signum of the error methodology. To facilitate the stability analysis, the system uncertainties regarding the moving object's velocities and distance information are divided into the error‐unrelated system uncertainties and the error‐related system uncertainties. After that, the upper bounds of the error‐related system uncertainties are derived with composited system errors. An asymptotic tracking of the leading object is proved based on the Lyapunov methods and the derived upper bounds. In addition, the proposed controller is extended to address the trajectory tracking problem. Simulation results validate the effectiveness of the proposed approach. Copyright © 2017 John Wiley & Sons, Ltd.

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Section: Simulation Results and Discussionmentioning

confidence: 99%

“…is larger than the right-hand side of (22). Therefore, the magnitude of K sr can be reduced with the introduction of the hyperbolic functions.…”

Section: Lemmamentioning

confidence: 99%

“…on condition that the control gain matrix K sr is adjusted according to (22), and the control gain matrix K pr is selected sufficiently large to satisfy…”

Section: Theoremmentioning

confidence: 99%

mentioning

confidence: 99%

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Asymptotic moving object tracking with trajectory tracking extension: A homography‐based approach

Zhang

Chen

Jia

2017

Intl J Robust & Nonlinear

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“…camera velocity/acceleration, observed target velocities and so on) to estimate the geometry of a scene and/or the camera motion (see e.g. De Luca et al (2008); Martinelli (2012); Eudes et al (2013); Grabe et al (2013); Chwa et al (2016)). Some of these works also identified and discussed the singularities of the problem, but without proposing any active control strategy to avoid them.…”

Section: Introductionmentioning

confidence: 99%

Coupling active depth estimation and visual servoing via a large projection operator

Spica

Giordano

Chaumette

2017

The International Journal of Robotics Research

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The goal of this paper is to propose a coupling between the execution of a Image-Based Visual Servoing (IBVS) task and an active Structure from Motion (SfM) strategy. The core idea is to modify online the camera trajectory in the null-space of the (main) servoing task for rendering the camera motion 'more informative' w.r.t. the estimation of the 3-D structure. Consequently, the SfM convergence rate and accuracy is maximized during the servoing transient. The improved SfM performance also benefits the servoing execution, since a higher accuracy in the 3-D parameters involved in the interaction matrix improves the IBVS convergence by significantly mitigating the negative effects (instability, loss of feature visibility) of a poor knowledge of the scene structure. Active maximization of the SfM performance results, in general, in a deformed camera trajectory w.r.t. what would be obtained with a classical IBVS: therefore, we also propose an adaptive strategy able to automatically activate/deactivate the SfM optimization as a function of the current level of accuracy in the estimated 3-D structure. We finally report a thorough experimental validation of the overall approach under different conditions and case studies. The reported experiments support well the theoretical analysis and clearly show the benefits of the proposed coupling between visual control and active perception.

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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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Range and Motion Estimation of a Monocular Camera Using Static and Moving Objects

Cited by 53 publications

References 44 publications

Asymptotic moving object tracking with trajectory tracking extension: A homography‐based approach

Asymptotic moving object tracking with trajectory tracking extension: A homography‐based approach

Coupling active depth estimation and visual servoing via a large projection operator

2.5D Vision-Based Estimation

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