Presentation 2006-03-15
Hidden State Estimation Based on Mixture Kalman Filters : Application to Self-Localization of Cyber Rodent
Michiyuki MAGONO, Junichiro YOSHIMOTO, Shin ISHII, Kenji DOYA,
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Abstract(in English) Self-localization is one of the important topics in the field of mobile robotics. Method using probabilistic models such as Kalman filter (KF) and Monte Carlo localization (MCL) are widely-used because they can effectively deal with uncertainty of the environment. In this report, we propose a self-localization method based on mixture Kalman filters, which is a hybrid of KF and MCL to exploit complementary advantages of the two methods. The effectiveness of our method is demonstrated through computer simulations and real experiments with a rat-like mobile robot Cyber Rodent.
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Keyword(in English) Mixture Kalman Filters / Monte Carlo Localization / Kalman Filter / Mobile robot / Bayesian inference
Paper # NC2005-121
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Committee NC
Conference Date 2006/3/8(1days)
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Registration To Neurocomputing (NC)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Hidden State Estimation Based on Mixture Kalman Filters : Application to Self-Localization of Cyber Rodent
Sub Title (in English)
Keyword(1) Mixture Kalman Filters
Keyword(2) Monte Carlo Localization
Keyword(3) Kalman Filter
Keyword(4) Mobile robot
Keyword(5) Bayesian inference
1st Author's Name Michiyuki MAGONO
1st Author's Affiliation Nara Institute of Science and Technology:Okinawa Institute of Science and Technology Initial Research Project()
2nd Author's Name Junichiro YOSHIMOTO
2nd Author's Affiliation Okinawa Institute of Science and Technology Initial Research Project:Nara Institute of Science and Technology
3rd Author's Name Shin ISHII
3rd Author's Affiliation Nara Institute of Science and Technology
4th Author's Name Kenji DOYA
4th Author's Affiliation Okinawa Institute of Science and Technology Initial Research Project:Nara Institute of Science and Technology
Date 2006-03-15
Paper # NC2005-121
Volume (vol) vol.105
Number (no) 657
Page pp.pp.-
#Pages 6
Date of Issue