DocumentCode
3461966
Title
Monte Carlo Localization Robust against Successive Outliers
Author
Nakajima, Shigeyoshi ; Ikejiri, Masataka ; Toriu, Takashi
Author_Institution
Grad. Sch. of ENG, Osaka City Univ., Osaka, Japan
fYear
2009
fDate
7-9 Dec. 2009
Firstpage
1515
Lastpage
1518
Abstract
We propose new methods of localization for a robot from surround views and dead reckoning data. Localization is one of very important techniques for autonomous robots, e. g. in RoboCup (autonomous robot succor league). Recently a resetting Monte Carlo localization (ML) method was proposed. But the method cannot deal with successive outliers well. The methods we proposed in this paper are improvements of the resetting ML method and good at dealing with successive outliers.
Keywords
Monte Carlo methods; mobile robots; multi-robot systems; Monte Carlo localization; RoboCup; autonomous robot succor league; Automatic control; Bayesian methods; Cities and towns; Dead reckoning; Monte Carlo methods; Robot control; Robot sensing systems; Robotics and automation; Robust control; Robustness;
fLanguage
English
Publisher
ieee
Conference_Titel
Innovative Computing, Information and Control (ICICIC), 2009 Fourth International Conference on
Conference_Location
Kaohsiung
Print_ISBN
978-1-4244-5543-0
Type
conf
DOI
10.1109/ICICIC.2009.268
Filename
5412648
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