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Optimal Stochastic Linearization for Range-based Localization

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

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.

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Volltext §
DOI: 10.5445/IR/1000035079
Originalveröffentlichung
DOI: 10.1109/IROS.2010.5649076
Coverbild
Zugehörige Institution(en) am KIT Institut für Anthropomatik (IFA)
Publikationstyp Proceedingsbeitrag
Jahr 2010
Sprache Englisch
Identifikator ISBN: 978-1-4244-6676-4
urn:nbn:de:swb:90-350794
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 IEEE, Piscataway
Seiten 5731-5736
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