Application of particle filter to autonomous navigation system for outdoor environment

The self-localization of mobile robots by using GPS and INS with extended Kalman filter has attracted significant research attention in recent years. Although the extended Kalman filter (EKF) has been extensively employed to solve these problems in mobile robots, the performance of the EKF can degrade significantly, if the correct a priori knowledge of sensor/measurement noise covariance matrices is not available since depending on available GPS satellite positions. In this paper, we propose a new particle filter based self-localization method for autonomous navigation in outdoor unknown environments, and compare the conventional GPS with extended Kalman filter and the GPS with particle filter.