Hybridisations Based On Visual Information For The Localisation Of Self-Driving Cars

Abstract : Safran has been working for several years on autonomy of vehicles. Whether it is airborne with the UAV Patroller, or on the ground with the military vehicle eRider and with the civilian autonomous car in cooperation with Valeo. This paper focuses on the use of visual information to improve the localisation of the car. More precisely, it presents, from a theoretical point of view, the different kind of visual information that can be used in the navigation filter to improve the localisation of the car and the corresponding hybridisation. The efficiency of these hybridisation are evaluated one by one on simulated and/or on real data.
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Communication dans un congrès
ITSNT 2018, International Technical Symposium on Navigation and Timing, Oct 2018, Toulouse, France. 〈10.31701/itsnt2018.09〉
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https://hal-enac.archives-ouvertes.fr/hal-01942266
Contributeur : Laurence Porte <>
Soumis le : mercredi 12 décembre 2018 - 11:03:13
Dernière modification le : vendredi 14 décembre 2018 - 13:17:39

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Jean-Luc Demange, Valentin Demange, Maxime Ferreira, Kevin Honoré, T. Maziol, et al.. Hybridisations Based On Visual Information For The Localisation Of Self-Driving Cars. ITSNT 2018, International Technical Symposium on Navigation and Timing, Oct 2018, Toulouse, France. 〈10.31701/itsnt2018.09〉. 〈hal-01942266〉

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