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Dual Quaternion Sample Reduction for SE(2) Estimation

Li, Kailai ORCID iD icon 1; Pfaff, Florian 1; Hanebeck, Uwe D. 1
1 Institut für Anthropomatik und Robotik (IAR), Karlsruher Institut für Technologie (KIT)


We present a novel sample reduction scheme for random variables belonging to the SE(2) group by means of Dirac mixture approximation. For this, dual quaternions are employed to represent uncertain planar transformations. The Cramér–von Mises distance is modified as a smooth metric to measure the statistical distance between Dirac mixtures on the manifold of planar dual quaternions. Samples of reduced size are then obtained by minimizing the probability divergence via Riemannian optimization while interpreting the correlation between rotation and translation. We further deploy the proposed scheme for nonparametric modeling of estimates for nonlinear SE(2) estimation. Simulations show superior tracking performance of the sample reduction-based filter compared with Monte Carlo-based as well as parametric model-based planar dual quaternion filters.

Postprint §
DOI: 10.5445/IR/1000120958
Veröffentlicht am 06.07.2021
DOI: 10.23919/FUSION45008.2020.9190388
Zitationen: 3
Zitationen: 1
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Anthropomatik und Robotik (IAR)
Publikationstyp Proceedingsbeitrag
Publikationsdatum 05.07.2020
Sprache Englisch
Identifikator ISBN: 978-0-578-64709-8
KITopen-ID: 1000120958
Erschienen in Proceedings of 2020 23rd International Conference on Information Fusion (FUSION 2020) : 2020 23rd International Conference on Information Fusion (FUSION 2020) took place 6-9 July 2020 as a virtual conference, Pretoria, South Africa
Veranstaltung 23rd International Conference on Information Fusion (FUSION 2020), Online, 06.07.2020 – 09.07.2020
Verlag Institute of Electrical and Electronics Engineers (IEEE)
Seiten 8 S.
Bemerkung zur Veröffentlichung Die Veranstaltung fand als Online-Event statt
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