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Author:
Karlsson, Rickard (Linköping University, Department of Electrical Engineering, Automatic Control) (Linköping University, The Institute of Technology)
Schön, Thomas (Linköping University, Department of Electrical Engineering, Automatic Control) (Linköping University, The Institute of Technology)
Törnqvist, David (Linköping University, Department of Electrical Engineering, Automatic Control) (Linköping University, The Institute of Technology)
Conte, Gianpaolo (Linköping University, Department of Computer and Information Science, Artificial Intelligence and Intergrated Computer systems) (Linköping University, The Institute of Technology)
Gustafsson, Fredrik (Linköping University, Department of Electrical Engineering, Automatic Control) (Linköping University, The Institute of Technology)
Title:
Utilizing Model Structure for Efficient Simultaneous Localization and Mapping for a UAV Application
Department:
Linköping University, Department of Computer and Information Science, Artificial Intelligence and Intergrated Computer systems
Linköping University, Department of Electrical Engineering, Automatic Control
Linköping University, The Institute of Technology
Publication type:
Conference paper (Other academic)
Language:
English
In:
Proceedings of Reglermöte 2008
Conference:
Reglermöte 2008, Luleå, Sweden June, 2008
Pages:
313-322
Year of publ.:
2008
URI:
urn:nbn:se:liu:diva-43508
Permanent link:
http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-43508
Local ID:
73995
Subject category:
Engineering and Technology
Control Engineering
SVEP category:
TECHNOLOGY
Keywords(en) :
Rao-Blackwellized/marginalized particle filter, Sensor fusion, Simultaneous localization and mapping, Inertial sensors, UAV, Vision
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.

Available from:
2009-10-10
Created:
2009-10-10
Last updated:
2013-02-23
Statistics:
102 hits