Yusefi, A.Durdu, A.Bozkaya, F.Tiglioglu, S.Yilmaz, A.Sungur, C.2023-11-112023-11-1120232379-88582379-8904https://doi.org/10.1109/TIV.2023.3316361https://hdl.handle.net/20.500.13091/4776An 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. IEEEeninfo:eu-repo/semantics/closedAccessAutonomous vehiclesAutonomous VehiclesCamerasDeep Visual Inertial OdometryGlobal navigation satellite systemGNSSIMULocalizationLocation awarenessOdometrySensor FusionSimultaneous localization and mappingVisualizationAutonomous vehiclesGlobal positioning systemAutonomous VehiclesDeep visual inertial odometryGlobal Navigation Satellite SystemsInertial measurements unitsLocalisationLocation awarenessOdometrySensor fusionSimultaneous localization and mappingCamerasArticle10.1109/TIV.2023.33163612-s2.0-85173015693