IoT-based Vehicle Localization in GNSS-denied Environments

Integration of global navigation satellite system (GNSS) and inertial navigation system (INS) presents significant potential for high-precision vehicle localization. However, this approach suffers from cumulative INS errors in GNSS-denied environments. To address this issue, this paper proposes a method to correct the navigation errors due to INS by using Internet of Things (IoT)-based vision positioning. More specifically, this method employs a binocular camera to assist in obtaining the vision positioning of the vehicle through recognition of LED lights installed in a type of ubiquitously deployed IoT devices that are increasingly used in smart transportation systems. The final navigation outcomes are attained by fusing the vision positioning results with INS information using an unscented Kalman filter (UKF). Real-world experiment results validate the effectiveness of the proposed vehicle localization method.