Unscented Kalman Filter on Lie Groups for Visual Inertial Odometry

Abstract : Fusing visual information with inertial measurements for state estimation has aroused major interests in recent years. However, combining a robust estimation with computational efficiency remains challenging, specifically for low-cost aerial vehicles in which the quality of the sensors and the processor power are constrained by size, weight and cost. In this paper, we present an innovative filter for stereo visual inertial odometry building on: i) the recently introduced stereo multi-state constraint Kalman filter; ii) the invariant filtering theory; and iii) the unscented Kalman filter (UKF) on Lie groups. Our solution combines accuracy, robustness and versatility of the UKF. We then compare our approach to state-of-art solutions in terms of accuracy, robustness and computational complexity on the EuRoC dataset and a challenging MAV outdoor dataset.
Type de document :
Communication dans un congrès
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2018, Oct 2018, Madrid, Spain. 2018, 〈https://www.iros2018.org/〉
Liste complète des métadonnées

https://hal.archives-ouvertes.fr/hal-01735542
Contributeur : Martin Brossard <>
Soumis le : jeudi 19 juillet 2018 - 16:31:22
Dernière modification le : lundi 12 novembre 2018 - 10:59:25
Document(s) archivé(s) le : samedi 20 octobre 2018 - 15:27:44

Fichier

iros2018.pdf
Fichiers produits par l'(les) auteur(s)

Identifiants

  • HAL Id : hal-01735542, version 2

Citation

Martin Brossard, Silvere Bonnabel, Axel Barrau. Unscented Kalman Filter on Lie Groups for Visual Inertial Odometry. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2018, Oct 2018, Madrid, Spain. 2018, 〈https://www.iros2018.org/〉. 〈hal-01735542v2〉

Partager

Métriques

Consultations de la notice

268

Téléchargements de fichiers

406