Mobile robot localization with improved system model

Abstract

.. I . Abstract -'Mobile robot positiqn estimation is a fundamental problem for autonomous navigation. System equations are generaliy .nonlinear in mobile robotics. Extended Kalman Filter is an efficient tool for mobile robot pose tracking, but it suffers from linearization errors due to linear approximation df.nonlinear system equations.. In .this paper we describe a position estimation method with linear system models. System state vector is augmented and.. the coordinate of landmark is considered'as observation information.' In this way, process and measurement.equations.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.