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Invariant Smoothing on Lie Groups

Abstract : In this paper we propose a (non-linear) smoothing algorithm for group-affine observation systems, a recently introduced class of estimation problems on Lie groups that bear a particular structure. As most non-linear smoothing methods, the proposed algorithm is based on a maximum a posteriori estimator, determined by optimization. But owing to the specific properties of the considered class of problems, the involved linearizations are proved to have a form of independence with respect to the current estimates, leveraged to avoid (partially or sometimes totally) the need to relinearize. The method is validated on a robot localization example, both in simulations and on real experimental data.
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Submitted on : Wednesday, March 7, 2018 - 4:33:10 PM
Last modification on : Wednesday, November 17, 2021 - 12:31:05 PM
Long-term archiving on: : Friday, June 8, 2018 - 2:42:10 PM


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  • HAL Id : hal-01725847, version 1
  • ARXIV : 1803.02076


Paul Chauchat, Axel Barrau, Silvere Bonnabel. Invariant Smoothing on Lie Groups. IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2018, Oct 2018, Madrid, Spain. ⟨hal-01725847⟩



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