DocumentCode :
3291414
Title :
Optimal stochastic linearization for range-based localization
Author :
Beutler, Frederik ; Huber, Marco F. ; Hanebeck, Uwe D.
Author_Institution :
Intell. Sensor-Actuator-Syst. Lab. (ISAS), Inst. for Anthropomatics, Karlsruhe, Germany
fYear :
2010
fDate :
18-22 Oct. 2010
Firstpage :
5731
Lastpage :
5736
Abstract :
In range-based localization, the trajectory of a mobile object is estimated based on noisy range measurements between the object and known landmarks. In order to deal with this uncertain information, a Bayesian state estimator is presented, which exploits optimal stochastic linearization. Compared to standard state estimators like the Extended or Unscented Kalman Filter, where a point-based Gaussian approximation is used, the proposed approach considers the entire Gaussian density for linearization. By employing the common assumption that the state and measurements are jointly Gaussian, the linearization can be calculated in closed form and thus analytic expressions for the range-based localization problem can be derived.
Keywords :
Gaussian processes; approximation theory; linearisation techniques; mobile robots; optimisation; position control; state estimation; Bayesian state estimator; extended Kalman filter; optimal stochastic linearization; point based Gaussian approximation; range based localization; trajectory estimation; unscented Kalman filter;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Intelligent Robots and Systems (IROS), 2010 IEEE/RSJ International Conference on
Conference_Location :
Taipei
ISSN :
2153-0858
Print_ISBN :
978-1-4244-6674-0
Type :
conf
DOI :
10.1109/IROS.2010.5649076
Filename :
5649076
Link To Document :
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