基于高维状态空间的移动机器人定位

位置跟踪是移动机器人自主导航中的一个主要任务.扩展的卡尔曼滤波定位方法是一个常用的位置跟踪方法,但是在对非线性系统方程进行线性化近似过程中引入了线性化误差.文中给出了一个基于线性系统模型的位置估计方法.用一个高维的状态向量表示机器人的位置空间,并选用环境路标的全局信息作为观测向量,此时系统动态模型和系统观测模型都是线性的,从而直接运用最优的线性卡尔曼滤波技术进行移动机器人位置估计.这种方法免除了非线性方程的线性近似过程,避免了线性化误差.实验表明,位置估计过程是收敛的、一致的.

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 position 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 global coordinates 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. Experimental results indicate that the proposed method is convergent and consistent.