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JACIII Vol.25 No.6 pp. 982-988
doi: 10.20965/jaciii.2021.p0982
(2021)

Paper:

The Estimation of Image Jacobian Matrix with Time-Delay Compensation

Xinmei Wang*,**, Zhenzhu Liu*, Feng Liu*,**, and Leimin Wang*,**

*School of Automation, China University of Geosciences
No.388 Lumo Road, Hongshan District, Wuhan, Hubei 430074, China

**Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems
No.388 Lumo Road, Hongshan District, Wuhan, Hubei 430074, China

Received:
February 20, 2018
Accepted:
September 6, 2021
Published:
November 20, 2021
Keywords:
object feature point image estimation with time-delay compensation, image Jacobian matrix estimation with time-delay compensation, decorrelation Kalman filtering
Abstract

Time delay exists in image-based visual servo system, which will have a certain impact on the system control. To solve the impact of time delay, the time delay compensation of the object feature point image and the image Jacobian matrix is discussed in this paper. Some work is done in this paper: The estimation of the object feature point image under time delay is based on a proposed robust decorrelation Kalman filtering model, for the measurement vectors which cannot be obtained during time delay in the robust Kalman filtering model, a polynomial fitting method is proposed in which the selection of the polynomial includes the position, velocity and acceleration of the object feature point which impact the feature point trajectory, then the more accurate object feature point image can be obtained. From the estimated object feature point image under time delay, the more accurate image Jacobian matrix under time delay can be obtained. Simulation and experimental results verify the feasibility and superiority of this paper method.

Cite this article as:
Xinmei Wang, Zhenzhu Liu, Feng Liu, and Leimin Wang, “The Estimation of Image Jacobian Matrix with Time-Delay Compensation,” J. Adv. Comput. Intell. Intell. Inform., Vol.25, No.6, pp. 982-988, 2021.
Data files:
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Last updated on Nov. 30, 2021