クロダ　ヨウジ   Kuroda Yoji   黒田　洋司    所属   明治大学  理工学部    職種   専任教授
言語種別	英語
発行・発表の年月	2011/12
形態種別	国際会議議事録
査読	査読あり
標題	Robust Localization System using Online / Offline Hybrid Learning
執筆形態	共著(筆頭者以外)
掲載誌名	Proc. of IEEE/SICE International Symposium on System Integration (SII2011)
掲載区分	国外
出版社・発行元	IEEE/SICE
巻・号・頁	pp.1299-1304
著者・共著者	Yuto Fuji and Yoji Kuroda
概要	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 achieves 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.
researchmap用URL	http://ieeexplore.ieee.org/document/6147636/