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Pfaff, F.* ; Li, K.* ; Hanebeck, U.D.*

The state space subdivision filter for estimation on se(2).

Sensors 21:6314 (2021)
Verlagsversion DOI
Open Access Gold
Creative Commons Lizenzvertrag
The SE(2) domain can be used to describe the position and orientation of objects in planar scenarios and is inherently nonlinear due to the periodicity of the angle. We present a novel filter that involves splitting up the joint density into a (marginalized) density for the periodic part and a conditional density for the linear part. We subdivide the state space along the periodic dimension and describe each part of the state space using the parameters of a Gaussian and a grid value, which is the function value of the marginalized density for the periodic part at the center of the respective area. By using the grid values as weighting factors for the Gaussians along the linear dimensions, we can approximate functions on the SE(2) domain with correlated position and orientation. Based on this representation, we interweave a grid filter with a Kalman filter to obtain a filter that can take different numbers of parameters and is in the same complexity class as a grid filter for circular domains. We thoroughly compared the filters with other state-of-the-art filters in a simulated tracking scenario. With only little run time, our filter outperformed an unscented Kalman filter for manifolds and a progressive filter based on dual quaternions. Our filter also yielded more accurate results than a particle filter using one million particles while being faster by over an order of magnitude.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Grid Filter ; Nonlinear Filtering ; Periodic Manifold ; Special Euclidean Group; Particle Filters; Tracking
Sprache englisch
Veröffentlichungsjahr 2021
HGF-Berichtsjahr 2021
ISSN (print) / ISBN 1424-8220
e-ISSN 1424-8220
Zeitschrift Sensors
Quellenangaben Band: 21, Heft: 18, Seiten: , Artikelnummer: 6314 Supplement: ,
Verlag MDPI
Verlagsort St Alban-anlage 66, Ch-4052 Basel, Switzerland
Begutachtungsstatus Peer reviewed
Institut(e) Helmholtz AI - KIT (HAI - KIT)
Förderungen KIT-Publication Fund of the Karlsruhe Institute of Technology
state of Baden-Wurttemberg through bwHPC
Helmholtz AI Cooperation Unit within the scope of the project Ubiquitous Spatio-Temporal Learning for Future Mobility (ULearn4Mobility)
DOI 10.3390/s21186314
WOS ID WOS:000701895400001
Scopus ID 85115158907
Erfassungsdatum 2021-10-27
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