Range identification for perspective vision systems

Dixon, Warren E.; Fang, Yongchun; Dawson, D.M.; Flynn, Tom

doi:10.1109/acc.2003.1244065

Cited by 31 publications

(47 citation statements)

References 9 publications

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“…2-5 indicate that the proposed observer can be used to identify the range and hence, the Euclidean coordinates of an object feature moving with affine motion dynamics and a nonaffine PDS, provided the observability conditions are satisfied. These results are comparable to the results obtained in [5] which applied the range identification observer to a planar imaging surface.…”

Section: Discussionsupporting

confidence: 93%

“…Remark 3: Once 4 ( ) is identified, the complete 3D Euclidean coordinates of the object feature can be determined using (5) and (13). Provided the observability conditions given in (15) and (16) are satisfied 4 ( ) can be identified ifˆ ( ) approaches ( ) as (i.e.,ˆ 1 ( ),ˆ 2 ( ) and 3 ( ) approach 1 ( ), 2 ( ) and 3 ( ) as ) since the parameters ( ) = 1 2 3 are assumed to be known, and 1 ( ), 2 ( ) and 3 ( ) are measurable.…”

Section: B Estimator Design and Error Systemmentioning

confidence: 99%

“…Motivated to generalize the object motion beyond the skewsymmetric form in [7], Chen and Kano developed a new discontinuous observer in [3] that exponentially forces the state observation error to be uniformly ultimately bounded (UUB) for known motion parameters. In comparison to the UUB result in [3], a continuous observer was constructed in [5] to asymptotically identify the range information for a general affine system with known motion parameters (i.e., the result in [5] eliminated the skew-symmetric assumption and yielded an asymptotic result with a continuous observer). More recently, Ma et al developed a state estimation strategy in [9] and [10] for affine systems with known motion parameters where only a single homogeneous observation point is provided (i.e., a single image coordinate).…”

mentioning

confidence: 99%

“…The structure of the nonlinear observer is based on our previous observer in [5]; however, new observability conditions are imposed due to the nonaffine form of the projection resulting from the paraboloid image surface. A Lyapunov-based analysis is used to prove the range and the 3D coordinates of an object moving with general affine Euclidean motion (and nonaffine image dynamics) are asymptotically identified.…”

mentioning

confidence: 99%

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Lyapunov-based range and motion identification for a nonaffine perspective dynamic system

Gupta

Aiken

et al. 2006

2006 American Control Conference

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Many applications require the interpretation of the Euclidean motion of features of a 3-dimensional (3D) object through 2D images. In this paper, the range and the Euclidean coordinates of an object undergoing general affine motion are determined for a paraboloid imaging system. Unlike image systems that are based on a planar image surface (or spherical or ellipsoidal surfaces), the perspective dynamic system resulting from the paraboloid projected image does not maintain an affine form. Because of the nonaffine form, existing range identification observers can not be directly utilized. The contribution of the current result is the development of a nonlinear state estimator that can be applied to the nonaffine perspective dynamic system to determine the range and Euclidean coordinates of an object feature without the use of linear approximations. The nonlinear estimator asymptotically determines the range information from a single camera provided some observability conditions are satisfied and that the Euclidean motion parameters are known. The proposed technique is developed through a Lyapunov-based design and stability analysis, and simulation results are provided that illustrate the performance of the state estimator.

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Section: Discussionsupporting

confidence: 93%

Section: B Estimator Design and Error Systemmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Lyapunov-based range and motion identification for a nonaffine perspective dynamic system

Gupta

Aiken

et al. 2006

2006 American Control Conference

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“…Given the coordinates p i of a set of feature points at different times, there are numerous methods to solve for the relative displacement of the camera to the points, the motion of the points over time, the motion of the camera over time, etc. These methods include multi-view (epipolar) geometry [19][20][21], Kalman filtering [22][23][24] and nonlinear estimation [25,26]. Using this data in the feedback loop of a control system constitutes vision-based controls.…”

Section: Camera Projection Modelmentioning

confidence: 99%

A hardware in the loop simulation platform for vision-based control of unmanned air vehicles

et al. 2009

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Robust face tracking control of a mobile robot using self‐tuning Kalman filter and echo state network

Tsai

Dutoit

Song

et al. 2010

Asian Journal of Control

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This paper presents a novel design of face tracking algorithm and visual state estimation for a mobile robot face tracking interaction control system. The advantage of this design is that it can track a user's face under several external uncertainties and estimate the system state without the knowledge about target's 3D motion-model information. This feature is helpful for the development of a real-time visual tracking control system. In order to overcome the change in skin color due to light variation, a real-time face tracking algorithm is proposed based on an adaptive skin color search method. Moreover, in order to increase the robustness against colored observation noise, a new visual state estimator is designed by combining a Kalman filter with an echo state network-based self-tuning algorithm. The performance of this estimator design has been evaluated using computer simulation. Several experiments on a mobile robot validate the proposed control system.

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Range identification for perspective vision systems

Cited by 31 publications

References 9 publications

Lyapunov-based range and motion identification for a nonaffine perspective dynamic system

Lyapunov-based range and motion identification for a nonaffine perspective dynamic system

A hardware in the loop simulation platform for vision-based control of unmanned air vehicles

Robust face tracking control of a mobile robot using self‐tuning Kalman filter and echo state network

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