| 迭代卡尔曼滤波在机器人定位中的应用 |
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| 引用本文： | 龙慧,胡利,周宴宇.迭代卡尔曼滤波在机器人定位中的应用[J].现代电子技术,2010,33(22):123-125. |
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| 作者姓名： | 龙慧 胡利 周宴宇 |
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| 作者单位： | [1]湖南生物机电职业技术学院信息技术系,湖南长沙410126 [2]湖南长沙智能控制系统有限公司,湖南长沙410001 [3]中南大学信息科学工程学院,湖南长沙410075 |
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| 基金项目： | 湖南省教育厅基金资助项目 |
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| 摘 要： | 定位是移动机器人最基本的问题之一。应用了迭代卡尔曼滤波（IEKF）集成航位推算和全局观测信息,解决机器人的定位问题。该方法在卡尔曼滤波测量更新阶段,多次迭代计算估计状态,直到误差小于一定的阈值。减少了由于泰勒展开的截断带来的定位误差,使得算法的收敛稳定性增强。最后通过仿真实验与EKF方法比较。结果表明,IEKF在移动机器人定位中是一种有效的方法。
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| 关 键 词： | 迭代卡尔曼滤波 误差分析 移动机器人 机器人定位 |
Application of Iterated Kalman Filtering in Robot Localization |
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| LONG Hui,HU Li,ZHOU Yan-yu.Application of Iterated Kalman Filtering in Robot Localization[J].Modern Electronic Technique,2010,33(22):123-125. |
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| Authors: | LONG Hui HU Li ZHOU Yan-yu |
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| Affiliation: | 1. Hunan Biological and Electromechanical Polytechnic, Changsha 410126, Chinas2. Hunan Intelligent Control System Company, Changsha 410001, Chinas 3. College of Information Engineering, Central South University, Changsha 410075, China) |
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| Abstract: | Localization is one of the most fundamental problem in mobile robots. The localization problem was solved by iterated extended Kalman filtering （IEKF） by combining the reckon reference and global observation information. In the measurement update phase of Kalman filtering, the state estimation is iterated many times until estimation error is lower than the threshold and localization error aroused from Taylor expand is reduced effectively. The convergent stability was improved by the method. Finally, IEKF and EKF methods are compared, the simulation result shows that IEKF is an effective method in localization of mobile robots. |
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| Keywords: | iterated Kalman filtering error analysis mobile robots robot localization |
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