Unscented Kalman Filtering on Lie Groups

Abstract : In this paper, we first consider a simple Bayesian fusion problem in a matrix Lie group, and propose to tackle it using the unscented transform. The method is then leveraged to derive two simple alternative unscented Kalman filters on Lie groups, for both cases of noisy partial measurements of the state, and full state noisy measurements of the state on the group. The general method is applied to a robot localization problem, and results based on experimental data combined with extensive Monte-Carlo simulations at various noise levels illustrate the superiority of the approach over the standard UKF.
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Submitted on : Wednesday, June 21, 2017 - 3:25:03 PM
Last modification on : Thursday, February 7, 2019 - 3:05:04 PM
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  • HAL Id : hal-01489204, version 3

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Martin Brossard, Silvère Bonnabel, Jean-Philippe Condomines. Unscented Kalman Filtering on Lie Groups. IROS 2017, EEE/RSJ International Conference on Intelligent Robots and Systems, IEEE/RSJ, Sep 2017, Vancouver, Canada. ⟨hal-01489204v3⟩

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