A Generalizable D-VIO and Its Fusion With GNSS/IMU for Improved Autonomous Vehicle Localization

Abdullah Yusefi, Akif Durdu, Firat Bozkaya, Sükrücan Tiglioglu, Alper Yilmaz, Cemil Sungur

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.