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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