Utilizing Model Structure for Efficient Simultaneous Localization and Mapping for a UAV Application
2008 (English)In: Proceedings of the 2008 IEEE Aerospace Conference, 2008, 1-10Conference paper (Refereed)
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. The second 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 models are 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 algorithm fuses 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.
Rao-Blackwellized/marginalized particle filter, Sensor fusion, Simultaneous localization and mapping, Inertial sensors, UAV, Vision
National CategoryEngineering and Technology Control Engineering
IdentifiersURN: urn:nbn:se:liu:diva-44274DOI: 10.1109/AERO.2008.4526442Local ID: 76152ISBN: 978-1-4244-1487-1ISBN: 978-1-4244-1488-8OAI: oai:DiVA.org:liu-44274DiVA: diva2:265136
2008 IEEE Aerospace Conference, Big Sky, MT, USA, March, 2008