Improving Poor GPS Area Localization for Intelligent Vehicles

Abstract : Precise positioning plays a key role in successful navigation of autonomous vehicles. A fusion architecture of Global Positioning System (GPS) and Laser-SLAM (Simultaneous Localization and Mapping) is widely adopted. While Laser-SLAM is known for its highly accurate localization, GPS is still required to overcome accumulated error and give SLAM a required reference coordinate. However, there are multiple cases where GPS signal quality is too low or not available such as in multi-story parking, tunnel or urban area due to multipath propagation issue etc. This paper proposes an alternative approach for these areas with WiFi Fingerprinting technique to replace GPS. Result obtained from WiFi Fingerprinting will then be fused with Laser-SLAM to maintain the general architecture, allow seamless adaptation of vehicle to the environment.
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Communication dans un congrès
MFI 2017 - IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, Nov 2017, Daegu, South Korea. pp.1-5
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Contributeur : Dinh Van Nguyen <>
Soumis le : lundi 9 octobre 2017 - 12:08:54
Dernière modification le : jeudi 2 août 2018 - 12:02:07
Document(s) archivé(s) le : mercredi 10 janvier 2018 - 13:47:20

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Dinh Van Nguyen, Fawzi Nashashibi, Trung-Kien Dao, Eric Castelli. Improving Poor GPS Area Localization for Intelligent Vehicles. MFI 2017 - IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, Nov 2017, Daegu, South Korea. pp.1-5. 〈hal-01613132〉

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