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Utilizing Model Structure for Efficient Simultaneous Localization and Mapping for a UAV Application
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2008 (English)Rapport (Other academic)
Abstract [en]

This contribution aims at unifying two recent trends in applied particle filtering (PF). The first trend is the major impact in simultaneous localization and mapping (SLAM) applications, utilizing the FastSLAM algorithm. Thesecond one is the implications of the marginalized particle filter (MPF) or the Rao-Blackwellized particle filter (RBPF) in positioning and tracking applications. Using the standard FastSLAM algorithm, only low-dimensional vehicle modelsare computationally feasible. In this work, an algorithm is introduced which merges FastSLAM and MPF, and the result is an algorithm for SLAM applications, where state vectors of higher dimensions can be used. Results using experimental data from a UAV (helicopter) are presented. The algorithmfuses measurements from on-board inertial sensors (accelerometer and gyro) and vision in order to solve the SLAM problem, i.e., enable navigation over a long period of time.

Place, publisher, year, pages
Linköping: Linköping University Electronic Press, 2008. 10 p.
Series
LiTH-ISY-R, ISSN 1400-3902 ; 2836
Keyword [en]
Rao-Blackwellized/marginalized particle filter, Sensor fusion, Simultaneous localization and mapping, Inertial sensors, UAV, Vision
National Category
Control Engineering
Identifiers
urn:nbn:se:liu:diva-56155 (URN)LiTH-ISY-R-2836 (ISRN)oai:DiVA.org:liu-56155 (OAI)
Available from2010-04-30 Created:2010-04-30 Last updated:2014-08-12Bibliographically approved

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Karlsson, RickardSchön, ThomasTörnqvist, DavidConte, GianpoloGustafsson, Fredrik
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