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Non-Identity Measurement Models for Orientation Estimation Based on Directional Statistics

Gilitschenski, I.; Kurz, G. 1; Hanebeck, U. D. 1
1 Institut für Anthropomatik und Robotik (IAR), Karlsruher Institut für Technologie (KIT)

Abstract:

We propose a novel measurement update procedure for orientation estimation algorithms that are based on directional statistics. This involves consideration of two scenarios, orientation estimation in the 2D plane and orientation estimation in three-dimensional space. We make use of the von Mises distribution and the Bingham distribution in these scenarios. In the derivation, we discuss directional counterparts to the extended Kalman filter and a statistical-linearization-based filter. The newly proposed algorithm makes use of deterministic sampling and can be thought of as a directional variant of the measurement update that is used in well-known sample-based algorithms such as the unscented Kalman filter.


Scopus
Zitationen: 11
Zugehörige Institution(en) am KIT Institut für Anthropomatik und Robotik (IAR)
Publikationstyp Proceedingsbeitrag
Publikationsjahr 2015
Sprache Englisch
Identifikator ISBN: 978-0-9824-4386-6
KITopen-ID: 1000051025
Erschienen in Proceedings of the 18th International Conference on Information Fusion (Fusion 2015), 6-9 July 2015, Washington, DC, USA
Verlag Institute of Electrical and Electronics Engineers (IEEE)
Seiten 727-733
Nachgewiesen in Scopus
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