A Comparison of Nonlinear Filters on Mobile Robot Pose Estimation

Abstract

Pose estimation for mobile robots attracts a lot of attention in recent years. In order to remove process and measurement noise, a number of filtering approaches are available to use: the extended Kalman filter (EKF), the unscented Kalman filter (UKF), and several variants of the particle filter (PF). This thesis quantitatively explores and compares the performance of the different filtering techniques applied to mobile robot pose estimation. The main criteria