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
fDate :
May 31 2010-June 2 2010
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;
Conference_Titel :
Computer and Robot Vision (CRV), 2010 Canadian Conference on
Conference_Location :
Ottawa, ON
Print_ISBN :
978-1-4244-6963-5
DOI :
10.1109/CRV.2010.33
