Navigable map-aided differential odometry to enhance GNSS in adverse conditions
Résumé
This paper studies the benefits of integrating proprioceptive measurements and a-priory geographical knowledge in the GNSS computation to increase availability and accuracy for modern ITS applications. A tightly-coupled framework for merging GNSS pseudo-ranges and odometric measurements is first introduced. An efficient way to merge a 2D navigable road-map is then presented, including the road selection and the map measurement model. A cautious fusion strategy, which provides enhanced GNSS-like positioning is performed, using an Extended Kalman filter monitoring innovation signals. This method has been applied to real-field data recorded during spring 2007. Results show the benefits of the proposed framework for GPS-computation under adverse conditions, i.e. with bad satellites availability and configuration.
Domaines
Automatique / RobotiqueOrigine | Fichiers produits par l'(les) auteur(s) |
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