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Optimal Stochastic Linearization for Range-based Localization

Beutler, Frederik; Huber, Marco F.; Hanebeck, Uwe D.


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.

Volltext §
DOI: 10.5445/IR/1000035079
DOI: 10.1109/IROS.2010.5649076
Zitationen: 3
Cover der Publikation
Zugehörige Institution(en) am KIT Fakultät für Informatik – Institut für Anthropomatik (IFA)
Publikationstyp Proceedingsbeitrag
Publikationsjahr 2010
Sprache Englisch
Identifikator ISBN: 978-1-4244-6676-4
KITopen-ID: 1000035079
Erschienen in Proceedings of the 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2010), Taipei, Taiwan, October, 2010
Verlag Institute of Electrical and Electronics Engineers (IEEE)
Seiten 5731-5736
Nachgewiesen in Dimensions
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