AUTHOR: Hamza Bouanzoul
RELATOR: Stefania Santini
CO-TRELATOR: Gianluca Toscano
TUTOR: Aniello Mungiello
ABSTRACT: This thesis presents a comprehensive exploration into the performance of three Kalman filter variants—Extended Kalman Filter (EKF), Cubature Kalman Filter (CKF), and Adaptive Cubature
Kalman Filter—in the context of vehicle pose estimation during straight motion. The study leverages real-world data obtained from the Xsens MTi-680G inertial navigation system (INS) hardware, fixed in the XEV Yoyo car. The project is centred around a test scenario involving straight motion, providing a detailed analysis of the filters' performances under specific motion dynamics. The XEV Yoyo car, equipped with the MTi-680G, serves as the platform for evaluating the filters' performance in capturing the vehicle's pose. Performance metrics encompassed include accuracy in position and orientation estimation, computational efficiency, and the adaptability of the filters to the dynamic behaviour exhibited during straight-line motion. Results obtained from the real-world test provide insights into the strengths and limitations of the EKF, CKF, and Adaptive CKF in the context of straight-line motion scenario.
