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Indoor localization of vehicles using Deep Learning

 
: Kumar, Anil Kumar Tirumala Ravi; Schäufele, Bernd; Becker, Daniel; Sawade, Oliver; Radusch, Ilja

:

Institute of Electrical and Electronics Engineers -IEEE-; IEEE Computer Society:
IEEE 17th International Symposium on a World of Wireless, Mobile and Multimedia Networks, WoWMoM 2016 : June 21-24, 2016, Coimbra, Portugal
Piscataway, NJ: IEEE, 2016
ISBN: 978-1-5090-2185-7
ISBN: 978-1-5090-2186-4
ISBN: 978-1-5090-2184-0
6 S.
International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM) <17, 2016, Coimbra>
Englisch
Konferenzbeitrag
Fraunhofer FOKUS ()

Abstract
Modern vehicles are equipped with numerous driver assistance and telematics functions, such as Turn-by-Turn navigation. Most of these systems rely on precise positioning of the vehicle. While Global Navigation Satellite Systems (GNSS) are available outdoors, these systems fail in indoor environments such as a car-park or a tunnel. Alternatively, the vehicle can localize itself with landmark-based positioning and internal car sensors, yet this is not only costly but also requires precise knowledge of the enclosed area. Instead, our approach is to use infrastructure-based positioning. Here, we utilize off-the shelf cameras mounted in the car-park and Vehicle-to-Infrastructure Communication to allow all vehicles to obtain an indoor position given from an infrastructure-based localization service. Our approach uses a Convolutional Neural Network (CNN) with Deep Learning to identify and localize vehicles in a car-park. We thus enable position-based Driver Assistance Systems (DAS) and telematics in an underground facility. We compare the novel Deep Learning classifier to a conventional classifier using Haar-like features.

: http://publica.fraunhofer.de/dokumente/N-421430.html