Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.13091/4776
Title: | A Generalizable D-VIO and Its Fusion with GNSS/IMU for Improved Autonomous Vehicle Localization | Authors: | Yusefi, A. Durdu, A. Bozkaya, F. Tiglioglu, S. Yilmaz, A. Sungur, C. |
Keywords: | Autonomous vehicles Autonomous Vehicles Cameras Deep Visual Inertial Odometry Global navigation satellite system GNSS IMU Localization Location awareness Odometry Sensor Fusion Simultaneous localization and mapping Visualization Autonomous vehicles Global positioning system Autonomous Vehicles Deep visual inertial odometry Global Navigation Satellite Systems Inertial measurements units Localisation Location awareness Odometry Sensor fusion Simultaneous localization and mapping Cameras |
Publisher: | Institute of Electrical and Electronics Engineers Inc. | Abstract: | An autonomous vehicle must be able to locate itself precisely and reliably in a large-scale outdoor area. In an attempt to enhance the localization of an autonomous vehicle based on Global Navigation Satellite System (GNSS)/Camera/Inertial Measurement Unit (IMU), when GNSS signals are interfered with or obstructed by reflected signals, a multi-step correction filter is used to smooth the inaccurate GNSS data obtained. The proposed solutions integrate a high amount of data from several sensors to compensate for the sensors' individual weaknesses. Additionally, this work proposes a Generalizable Deep Visual Intertial Odometry (GD-VIO) to better locate the vehicle in the event of GNSS outages. The algorithms suggested in this research have been tested through real-world experimentations, demonstrating that they are able to deliver accurate and trustworthy vehicle pose estimation. IEEE | URI: | https://doi.org/10.1109/TIV.2023.3316361 https://hdl.handle.net/20.500.13091/4776 |
ISSN: | 2379-8858 |
Appears in Collections: | Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collections |
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