DocumentCode
2572833
Title
A Comparison of the EKF, SPKF, and the Bayes Filter for Landmark-Based Localization
Author
Tong, Chi Hay ; Barfoot, Timothy D.
Author_Institution
Inst. for Aerosp. Studies, Univ. of Toronto, Toronto, ON, Canada
fYear
2010
fDate
May 31 2010-June 2 2010
Firstpage
199
Lastpage
206
Abstract
The conventional approach to nonlinear state estimation, the Extended Kalman Filter (EKF), is quantitatively compared to the performance of the relative newcomer, the Sigma-Point Kalman Filter (SPKF). These approaches are applied to the problem of localization of a mobile robot using a known map, and compared under the context of the practical best performance of a Bayes Filter-type method using a particle filter with a very large number of particles.
Keywords
Kalman filters; mobile robots; nonlinear estimation; particle filtering (numerical methods); path planning; state estimation; Bayes filter; extended Kalman filter; landmark-based localization; mobile robot; nonlinear state estimation; particle filter; sigma-point Kalman filter; Computer vision; Filters; Hardware; Integral equations; Mathematical model; Mobile robots; Robot vision systems; State estimation; State-space methods; Time measurement;
fLanguage
English
Publisher
ieee
Conference_Titel
Computer and Robot Vision (CRV), 2010 Canadian Conference on
Conference_Location
Ottawa, ON
Print_ISBN
978-1-4244-6963-5
Type
conf
DOI
10.1109/CRV.2010.33
Filename
5479184
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