This paper presents a comparison of different fitters namely:
Extended Kalman Filter (EKF), Particle Filter (PF) and a
proposed Enhanced Particle / Kalman Filter (EPKF) used in
robot localization. These filters are implemented in matlab
environment and their performances are evaluated in terms of
computational time and error from ground truth and the
results are reported. The considered robot localizer uses radio
beacons that provide the ability to measure range only. Since
EKF and its variants are not capable to efficiently solve the
global localization problem, we propose the Enhanced Particle
/ Kalman Filter (EPKF) which provide the required initial
location to address this drawback of EKF. We propose using
PF as Initialization phase to coarsely predict the initial
location and numerous sets of data are experimented to get
robust conclusion. The results showed that the proposed
localization approach which adopts the particle filter as
initialization step to EKF achieves higher accuracy
localization while, the computational cost is kept almost as
EKF alone.