Visual state estimation using self-tuning kalman filter and echo state network

Chi Yi Tsai, Xavier Dutoit, Kai-Tai Song*, Hendrik Van Brussel, Marnix Nuttin

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

This paper presents a novel design of visual state estimation for an image-based tracking control system to estimate system state during visual tracking control process. The advantage of this design is that it can estimate the target status and target image velocity without using the knowledge of target's 3D motion-model information. This advantage is helpful for real-time visual tracking controller design. In order to increase the robustness against random observation noise, a neural network based self-tuning algorithm is proposed using echo state network (ESN) technique. The visual state estimator is designed by combining a Kalman filter with the ESN-based self-tuning algorithm. The performance of this estimator design has been evaluated using computer simulation. Several interesting experiments on a mobile robot validate the proposed algorithms.

Original languageEnglish
Title of host publication2008 IEEE International Conference on Robotics and Automation, ICRA 2008
Pages917-922
Number of pages6
DOIs
StatePublished - 18 Sep 2008
Event2008 IEEE International Conference on Robotics and Automation, ICRA 2008 - Pasadena, CA, United States
Duration: 19 May 200823 May 2008

Publication series

NameProceedings - IEEE International Conference on Robotics and Automation
ISSN (Print)1050-4729

Conference

Conference2008 IEEE International Conference on Robotics and Automation, ICRA 2008
CountryUnited States
CityPasadena, CA
Period19/05/0823/05/08

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    Tsai, C. Y., Dutoit, X., Song, K-T., Van Brussel, H., & Nuttin, M. (2008). Visual state estimation using self-tuning kalman filter and echo state network. In 2008 IEEE International Conference on Robotics and Automation, ICRA 2008 (pp. 917-922). [4543322] (Proceedings - IEEE International Conference on Robotics and Automation). https://doi.org/10.1109/ROBOT.2008.4543322