TY - GEN
T1 - Visual state estimation using self-tuning kalman filter and echo state network
AU - Tsai, Chi Yi
AU - Dutoit, Xavier
AU - Song, Kai-Tai
AU - Van Brussel, Hendrik
AU - Nuttin, Marnix
PY - 2008/9/18
Y1 - 2008/9/18
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=51649116485&partnerID=8YFLogxK
U2 - 10.1109/ROBOT.2008.4543322
DO - 10.1109/ROBOT.2008.4543322
M3 - Conference contribution
AN - SCOPUS:51649116485
SN - 9781424416479
T3 - Proceedings - IEEE International Conference on Robotics and Automation
SP - 917
EP - 922
BT - 2008 IEEE International Conference on Robotics and Automation, ICRA 2008
Y2 - 19 May 2008 through 23 May 2008
ER -