Sensor fusion and surrounding environment mapping for a mobile robot using a mixed extended Kalman filter

In this work the localization of a mobile robot in an unknown environment is faced. A new version of the Extended Kalman Filter (EKF) is presented. The proposed EKF uses both measurements provided by robot on board and out of board sensors in order to emphasize the qualities and overcome the defects of such sensors. Moreover assuming a polynomial model for the robot surrounding environment bounds, an online algorithm able to build a map of this environment is presented. The proposed algorithms are tested in a numerical way contrasting them with a classical Extended Kalman Filter based only on the out of board sensors and with a fusing algorithm related only on the on board sensors.