Stochastic self localization of a mobile robot

This paper describes a solution to the localization problem based on the Extended Kalman filter Estimation of the location and path of a Nonholonomic robot in a given environment is a key problem which needs to be overcame for successful mobile robot navigation. In this work a differential drive vehicle model is used and evolution of the vehicle motion is modeled using vehicle frame translation derived from successive dead reckoned poses as a control input. The nearest neighbor algorithm is used for the purpose of data association .The localization algorithm is simulated using matlab. The results shown that the proposed approach localizes the robot with the necessary accuracy