Identification of a moving object's velocity with a fixed camera

Chitrakaran, V.K.; Dawson, D.M.; Dixon, Warren E.; Chen, J.

doi:10.1109/cdc.2003.1272496

Cited by 9 publications

(16 citation statements)

References 25 publications

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“…If the velocity of the object in the inertial reference frame is controlled and known as in Assumption 3, then it can be obtained in the camera reference frame by using the rotational matrix between the camera and inertial reference frames, which can be obtained by the integration of angular velocities. Next, angular velocities can be assumed to be available, since they can be measured using IMU or estimated using the homography matrix decomposition between consecutive camera frames [15], [29].…”

Section: Structure and Motion Dynamics Of Objectsmentioning

confidence: 99%

“…However, the proposed method involves the recasting of these dynamics into those of the measurable states in such a way that a sequential design and application of the robust integral signed error (RISE)-based robust nonlinear observers can be achieved as the following two steps. First, up-to-a-scale estimates of the camera velocities are obtained by tracking the stationary objects in images via an RISE-based nonlinear observer [15], [29]. This implies that the unknown constant scale factor should be further estimated to estimate the exact camera velocities.…”

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confidence: 99%

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

Chwa

Dani

Dixon

2016

IEEE Trans. Contr. Syst. Technol.

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We propose a method of estimating the motion of a monocular camera looking at moving objects and their range. Unlike the previous studies where the camera and object motion should be constrained in estimating structure and motion (SaM) of moving objects, the proposed method do not require those constraints even though only a monocular camera is used. By first arranging the SaM dynamics in terms of the measurable states, we design robust nonlinear observers in a sequential way for both static (stationary) and dynamic (moving) objects. Through the combination of these estimates obtained by nonlinear observers, the reconstruction of the 3-D structure of the dynamic objects can be achieved using just 2-D images of a monocular camera. Simulations are performed in the case of changing camera and object velocities, such that the advantages of the proposed method can be clearly demonstrated.

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Section: Structure and Motion Dynamics Of Objectsmentioning

confidence: 99%

mentioning

confidence: 99%

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

Chwa

Dani

Dixon

2016

IEEE Trans. Contr. Syst. Technol.

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“…In [22], an estimator was developed for online asymptotic identification of the signalė(t). Designatingê(t) as the estimate for e(t), the estimator was ρsgn (ẽ(τ )) dτ +(K + I 6 )ẽ(t) (3.199) whereẽ(t) , e(t) −ê(t) ∈ R 6 is the estimation error for the signal e(t), K, ρ ∈ R 6×6 are positive definite constant diagonal gain matrices, I 6 ∈ R 6×6 is the 6 × 6 identity matrix, t 0 is the initial time, and sgn(ẽ(t)) denotes the standard signum function applied to each element of the vectorẽ(t).…”

Section: Identification Of Velocitymentioning

confidence: 99%

Vision-Based Systems

2009

Lyapunov-Based Control of Robotic Systems

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Often robots are required to inspect, navigate through, and/or interact with unstructured or dynamically changing environments. Adequate sensing of the environment is the enabling technology required to achieve these tasks. Given recent advances in technologies such as computational hardware and computer vision algorithms, camera-based vision systems have become a popular and rapidly growing sensor of choice for robots operating in uncertain environments.Given some images of an environment, one of the first tasks is to detect and identify interesting features in the object. These features can be distinguished based on attributes such as color, texture, motion, and contrast. Textbooks such as [4,32,52,88,90,94,98,109] provide an excellent introduction and discussion of methods to find and track these attributes from image to image. This chapter assumes that some attributes can be used to identify an object of interest in an image, and points on the object, known as "feature points," can be tracked from one image to another. By observing how these feature points move in time and space, control and estimation algorithms can be developed. The first section of this chapter describes image geometry methods using a single camera. The development of this geometry enables the subsequent sections of the chapter to focus on the control and estimation of the motion of a plane attached to an object. © 2010 by Taylor and Francis Group, LLC383. Vision-Based SystemsThe terminology visual servo control refers to the use of information from a camera directly in the feedback loop of a controller. The typical objective of most visual servo controllers is to force a hand-held camera to a Euclidean position defined by a static reference image. Yet, many practical applications require a robotic system to move along a predefined or dynamically changing trajectory. For example, a human operator may predefine an image trajectory through a high-level interface, and this trajectory may need to be modified on-the-fly to respond to obstacles moving in and out of the environment. It is also well known that a regulating controller may produce erratic behavior and require excessive initial control torques if the initial error is large. The controllers in Section 3.3 focus on the more general tracking problem, where a robot end-effector is required to track a prerecorded time-varying reference trajectory. To develop the controllers, a homography-based visual servoing approach is utilized. The motivation for using this approach is that the visual servo control problem can be incorporated with a Lyapunov-based control design strategy to overcome many practical and theoretical obstacles associated with more traditional, purely image-based approaches. Specifically, one of the challenges of this problem is that the translation error system is corrupted by an unknown depth-related parameter. By formulating a Lyapunov-based argument, an adaptive update law is developed to actively compensate for the unknown depth parameter. In addition, the presented ap...

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“…Depending on applications, either range-only approach or bearingonly approach might be preferred. For example, in applications that do not require exchanges of radio signals, the bearing-only navigation would be favored because the bearing angles could be obtained by vision or image sensors (Chitrakaran, Dawsona, Dixonb, & Chen, 2005;Choi & Kim, 2014). This paper proposes a navigation algorithm relying upon only local bearing angles.…”

Section: Introductionmentioning

confidence: 99%