Kuroda Yoji
   Department   Undergraduate School  , School of Science and Technology
   Position   Professor
Language English
Publication Date 2011/12
Type International Conference
Peer Review Peer reviewed
Title Online Motion Model Parameter Estimation using Augmented Kalman Filter and Discriminative Training
Contribution Type Co-authored (other than first author)
Journal Proc. of IEEE International Conference on Robotics and Biomimetics (ROBIO2011)
Journal TypeAnother Country
Publisher IEEE
Volume, Issue, Page pp.1035-1040
Author and coauthor Yuto Fujii and Yoji Kuroda
Details In this paper, we propose an online motion model parameter estimation method. To achieve accurate localization, accurate estimation of motion model parameters is needed. However, the true values of motion model parameters change sequentially according to alteration of surrounding environments. Therefore the online estimation is absolutely imperative. As a typical method to estimate motion model parameters sequentially, Augmented Kalman Filter (AKF) is there. AKF achieve parameter estimation through Kalman filtering algorithm. However, AKF has serious problems to be implemented in real robot operation. These problems are the accuracy of observation and the limitation to motion control of robots. To solve these problems and achieve accurate motion model parameter estimation, proposed method introduces discriminative training. The introduction of discriminative training increases the convergence performance and stability of parameter estimation through AKF. The proposal method achieves accurate motion model parameter estimation in real robot operation. This paper describes the efficiency of our technique through simulations and an outdoor experiment.
URL for researchmap http://ieeexplore.ieee.org/document/6181424/