Localization For AutonomousDriving using Statistical Filtering : A GPS aided navigation approach with EKF and UKF

Abstract: A critical requirement for safe autonomous driving is to have an accurate state estimate of thevehicle. One of the most ubiquitous yet reliable ways for this task is through the integrationof the onboard Inertial Navigation System (INS) and the Global Navigation Satellite System(GNSS). This integration can further be assisted through fusion of information from otheronboard sensors. On top of that, a ground vehicle enforces its own set of rules, through non-holonomic constraints, which along with other vehicle dynamics can aid the state estimation.In this project, a sequential probabilistic inference approach has been followed, that fusesthe high frequency, short term accurate INS estimates, with low frequency, drift free GPSobservations. The fusion of GPS and IMU has been sought through a modular asynchronousloosely coupled framework, capable of augmenting additional observation sources to facilitatethe state estimation and tracking process. Besides GPS and IMU, the applied strategy makesuse of wheel speed sensor measurements, nonholonomic constraints and online estimationof IMU sensor biases as well wheel speed scalling factor. Theses augmentations have beenshown to increase the robustness of the localization module, under periods of GPS outage.The Extended Kalman Filter (EKF) has seen extensive usage for such sensor fusion tasks,however, the performance can be limited due to the propagation of the covariance throughlinearization of the underlying non-linear model. The Unscented Kalman Filter (UKF) avoidsthe issue of linearization based on jacobians. Instead, it uses a carefully chosen set ofsample points in order to accurately map the probability distribution. Correspondingly, thesurrounding literature also indicates towards the UKF out performing EKF in such tasks.Therefore, the present thesis also seeks to evaluate these claims.The EKF and SRUKF (Square Root UKF) instances of the developed algorithm have beentested on real sensor logs, recorded from a Scania test vehicle. Under no GPS outage situation,the implemented localization algorithm performs within a position RMSE of 60cm.The robustness of the localization algorithm, to GPS outages, is evaluated by simulating0-90% lengths of GPS unavailability, during the estimation process. Additionally, to unfoldthe impact of parameters, the individual modules within the suggested framework wereisolated and analysed with respect to their contribution towards the algorithm’s localizationperformance.Out of all, the online estimation of IMU sensor biases proved to be critical for increasingthe robustness of the suggested localization algorithm to GPS shortage, especially for the EKF.In terms of the distinction, both the EKF and the SRUKF performed to similar capabilities,however, the UKF showed better results for higher levels of GPS cuts.