AUTHOR: Davide Colucci
RELATOR: Stefania Santini
TUTOR: Aniello Mungiello
ABSTRACT: This dissertation presents the structure and the implementation of a multi-extended Kalman filter localization algorithm with fault detection capabilities. This algorithm has been developed for the driverless car of the Federico II racing team Unina Corse, that compete in the formula students’ events all over Europe.
The thesis will first introduce the odometry concept, principles and state of the art. Then proceed to introduce the context for which this algorithm has been developed: the formula student events, and competitions. After that the UniNa Corse team is briefly introduced, with its history and structure, then a broad overview of the car is given from the mechanical, electrical and software point of view where the components and the design choices are explained, with a more detailed explanation focused on the driverless algorithms structure.
Following this introduction, the odometry problem statement and the use cases contextualized in the formula student competitions will be presented, and only at this point will be explained the development of the structure of the localization algorithm that has followed the Model Based Control Design process (Appendix A2), starting with the analysis of the requirement and moving on with the different validation phases, finishing with the track test. In this section will also be given the overviews of the techniques used in the development process, such Extended Kalman Filter, Multi-Sensor Fusion, Chi-Square and drift detection algorithms.
In conclusion, ideas for further improvements of the algorithm and the testing phases will be discussed.
