Position Estimation for a Mobile Robot with Augmented System State

Position estimation is a fundamental problem for an autonomous mobile robot. Extended Kalman Filter is an efficient tool for mobile robot pose tracking, but it suffers from linearization errors due to linear approximation of nonlinear system equations. In this paper we describe a position estimation method with linear system models. The position of mobile robot is indirectly represented with an augmented system state vector. The coordinate of landmark is considered as observation information. In this way, motion model and observation model are linear. The position of mobile robot is estimated recursively based on optimal KF. It avoids linear approximation of nonlinear system equations and is free of linearization error. All these techniques have been implemented on our mobile robot ATRVII equipped with 2D laser rangefinder SICK.