基于改进系统模型的移动机器人定位

2004 International Conference on Intelligent Mechatronics and Automation, 2004. Proceedings. Pub Date : 2004-08-26 DOI:10.1109/ICIMA.2004.1384337

Zezhong Xu, Jilin Liu, Yongjin Shi, Keqiang Xia

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引用次数: 2

摘要

．．我。移动机器人的位置估计是自主导航的一个基本问题。在移动机器人中，系统方程一般是非线性的。扩展卡尔曼滤波是一种有效的移动机器人姿态跟踪工具，但由于线性逼近df而存在线性化误差。非线性系统方程。本文描述了一种基于线性系统模型的位置估计方法。系统状态向量增广后…地标的坐标被认为是观测信息。这样，过程和测量方程是线性的。基于最优KF递归估计移动机器人的位置。它避免了非线性系统方程的线性逼近，不存在线性化误差。所有这些技术都已在我们的移动机器人ATRVII上实现，该机器人配备了2D激光测距仪SICK。

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Mobile robot localization with improved system model

.. 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.

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2004 International Conference on Intelligent Mechatronics and Automation, 2004. Proceedings.

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