Localization Method for Autonomous Vehicles with Sensor Fusion Using Extended and Unscented Kalman Filters

This paper presents the design and experimental validation of a localization method for autonomous driving. The investigated method proposes and compares the application of the Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) to the sensor fusion of onboard data streaming from a Global Positioning System (GPS) sensor and an Inertial Navigation System (INS). In the paper, the design of the hardware layout and the proposed software architecture is presented. The method is experimentally validated in real time by using a properly instrumented all-wheel-drive electric racing vehicle and a compact Sport Utility Vehicle (SUV). The proposed algorithm is deployed on a high-performance computing platform with an embedded Graphical Processing Unit that is mounted on board the considered vehicles. The reported experimental results include the outcomes of the localization algorithm at submeter accuracy and the estimated vehicle’s states for the retained single-track vehicle model that is exploited for further control strategies. The experimental results show a substantial equivalence of the application of the two filters. Nevertheless, the UKF-based method is characterized by a significantly lower estimation variance in the localization task, thus providing more robust results.