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On sensor pose parameterization for inertial aided visual SLAM

: Kleinert, Markus; Stilla, Uwe

Postprint urn:nbn:de:0011-n-2212717 (614 KByte PDF)
MD5 Fingerprint: d2f81943f5c5858b9a8d91b94988d32d
Erstellt am: 8.12.2012

Rizos, C. ; IEEE Geoscience and Remote Sensing Society:
3rd International Conference on Indoor Positioning and Indoor Navigation, IPIN 2012 : Sydney, Australia, 13 - 15 November 2012
New York, NY: IEEE, 2012
ISBN: 978-1-4673-1955-3 (Print)
ISBN: 978-1-4673-1954-6
International Conference on Indoor Positioning and Indoor Navigation (IPIN) <3, 2012, Sydney>
Konferenzbeitrag, Elektronische Publikation
Fraunhofer IOSB ()

When appropriate infrastructure is not available, localization of pedestrians becomes a difficult task. This is especially the case in urban or indoor scenarios, where satellite navigation is hindered due to occlusions or multipath effects. A promising alternative is to combine a small, low-cost IMU with a camera in order to exploit the complementary error characteristics of these devices by simultaneously estimating the positions of observed landmarks and the trajectory of the sensor system with a stochastic filter. In this work, a standard approach to parameterize the error in position and attitude estimates that is commonly used in GNSSINS integration is compared to alternative parameterizations that are based on the twist representation of rigid body motions, which has gained increasing popularity in the literature. For this purpose, the error-state transition and measurement equations are formulated for the twist representation as well as for the standard approach. Finally, the different approaches are compared on a simulated and a real indoor dataset by applying an extended Kalman filter (EKF).