On attitude representations for optimization-based Bayesian smoothing

On attitude representations for optimization-based Bayesian smoothing

Lorenz, Michael (Technische Universität Kaiserslautern)
Taetz, Bertram (Technische Universität Kaiserslautern)
Kok, M. (TU Delft Team Jan-Willem van Wingerden)
Bleser, Gabriele (Technische Universität Kaiserslautern)

2019

This paper presents a study of the behavior of seven different attitude representations in a batchwise orientation smoothing problem. The representations include the well-known unit quaternions and rotation vectors(/axis-angle), as well as modified Rodrigues parameters (MRPs). We consider error states as well as direct orientation formulations for the orientation and propose two methods to handle the singularity of MRPs in the latter case. The Bayesian smoothing problem is posed as a maximum a posteriori estimate with Gaussian noise, which results in a non-linear weighted least squares problem. With this we estimate the trajectory for a single inertial measurement unit with only sparse magnetometer samples for two challenging scenarios. In the evaluation we mainly focus on the convergence but also consider the estimation errors. Monte Carlo simulations show that orientation error states, with rotation vectors or MRPs, in general converge faster than other representations. However, with an initialization of the optimization problem up to deviations of 90 degrees MRPs with one of the proposed singularity handlings performs similar, but with a much smaller memory consumption. The source code for this study is available online.

Bayesian smoothing
optimization
orientation estimation
attitude representations
inertial sensors

http://resolver.tudelft.nl/uuid:eecee55e-9251-49a3-ad3e-1275e8d0a4f0

IEEE, Piscataway, NJ, USA

2020-08-27

978-0-9964527-8-6

Proceedings of the 22nd International Conference on Information Fusion

22nd International Conference on Information Fusion (FUSION 2019), 2019-07-02 → 2019-07-05, Shaw Centre, Ottawa, Canada

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Institutional Repository

conference paper

© 2019 Michael Lorenz, Bertram Taetz, M. Kok, Gabriele Bleser