Invariant Unscented Kalman Filter with application to attitude estimation

Abstract : The Invariant UKF, named IUKF, is a recently introduced algorithm dedicated to nonlinear systems possessing symmetries as illustrated by the quaternion-based kinematics modeling of a mini-UAV (Unmanned Aircraft Vehicle) considered in this paper. Within an invariant framework, this algorithm suggests a systematic approach to determine all the symmetry-preserving terms, without requiring any compatibility condition such as proposed in the PI-IUKF, by introducing both notion of invariant output errors and UKF algorithm formulation. We propose in this paper to evaluate the applicability of our proposed IUKF observer to the case of attitude estimation for small UAVs using low-cost sensors. The IUKF algorithm is successfully validated in experiments and demonstrates that nonlinear state estimation converges on a much bigger set of trajectories than for more traditional approaches.
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CDC 2017, 56th IEEE conference on decision and control , Dec 2017, Melbourne, Australia. pp.ISBN: 978-1-5090-2874-0 2018, Proceedings 2017 IEEE 56th Annual Conference on Decision and Control (CDC). 〈10.1109/CDC.2017.8264063〉
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Jean-Philippe Condomines, Cédric Seren, Gautier Hattenberger. Invariant Unscented Kalman Filter with application to attitude estimation. CDC 2017, 56th IEEE conference on decision and control , Dec 2017, Melbourne, Australia. pp.ISBN: 978-1-5090-2874-0 2018, Proceedings 2017 IEEE 56th Annual Conference on Decision and Control (CDC). 〈10.1109/CDC.2017.8264063〉. 〈hal-01509884〉

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