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.
Type de document :
Communication dans un congrès
IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2018, Oct 2018, Madrid, Spain
Liste complète des métadonnées

Littérature citée [35 références]  Voir  Masquer  Télécharger

https://hal.archives-ouvertes.fr/hal-01725847
Contributeur : Paul Chauchat <>
Soumis le : mercredi 7 mars 2018 - 16:33:10
Dernière modification le : jeudi 7 février 2019 - 15:43:17
Document(s) archivé(s) le : vendredi 8 juin 2018 - 14:42:10

Fichier

invariant-smoothing-lie(6).pdf
Fichiers produits par l'(les) auteur(s)

Identifiants

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

Citation

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〉

Partager

Métriques

Consultations de la notice

182

Téléchargements de fichiers

73