| 自适应扩展卡尔曼滤波器在移动机器人定位中的应用 |
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| 引用本文： | 孟祥萍,张本法,苑全德.自适应扩展卡尔曼滤波器在移动机器人定位中的应用[J].计算机系统应用,2015,24(12):176-181. |
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| 作者姓名： | 孟祥萍 张本法 苑全德 |
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| 作者单位： | 长春工程学院电气与信息工程学院, 长春 130012,东北电力大学信息工程学院, 吉林 132012,长春工程学院电气与信息工程学院, 长春 130012;哈尔滨工业大学计算机科学与技术学院, 哈尔滨 150001 |
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| 基金项目： | 哈尔滨市科技创新人才研究专项资金(RC2013XK010002);吉林省教育厅"十二五"科学技术研究项目(2014324) |
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| 摘 要： | 针对移动机器人定位过程中存在的误差积累问题,提出了采用自适应扩展卡尔曼滤波算法(AEKF).分析了扩展卡尔曼滤波(EKF)和AEKF两种算法, AEKF取采样时刻的各项泰勒级数,并利用Sage-Husa时变噪声估计器实时估计观测噪声,克服了线性化误差,增强了环境适应性;同时,对AEKF的收敛性及运算复杂度进行分析,并结合算法实验表明AEKF具有良好的速度精度综合性价比;最后对比分析两种算法实现机器人定位的效果并实验完成误差对比.结果表明AEKF具有更优的定位性能.
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| 关 键 词： | 移动机器人 定位 路标 扩展卡尔曼滤波器算法 自适应扩展卡尔曼滤波器算法 |
| 收稿时间： | 4/3/2015 12:00:00 AM |
| 修稿时间： | 2015/5/15 0:00:00 |
Adaptive Extended Kalman Filter in the Application of the Mobile Robot Localization |
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| MENG Xiang-Ping,ZHANG Ben-Fa and YUAN Quan-De.Adaptive Extended Kalman Filter in the Application of the Mobile Robot Localization[J].Computer Systems& Applications,2015,24(12):176-181. |
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| Authors: | MENG Xiang-Ping ZHANG Ben-Fa and YUAN Quan-De |
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| Affiliation: | School of Electrical and Information Engineering, Changchun Institute of Technology, Changchun 130012, China,School of Information Engineering, Northeast Dianli University, Jilin 132012, China and School of Electrical and Information Engineering, Changchun Institute of Technology, Changchun 130012, China;School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China |
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| Abstract: | Concerning the error accumulation problem in mobile robot localization, a adaptive extended Kalman filter (AEKF) algorithm is presented. The extended Kalman filter and adaptive Kalman filter algorithms are analyzed. AEKF use the Taylor series in sampling time and the Sage-Husa time-varying noise estimator to estimate observation noise in real time, it overcomes the linearization error and enhance the environmental adaptability. Meanwhile, the AEKF convergence and complexity of operation are analyzed and combined with experiments show that AEKF has good comprehensive performance in terms of speed and precision. Finally, the effect of robot localization completed by two kinds of algorithm is analyzed and the error comparison by experiment is completed. The results indicate AEKF has better performance on localization. |
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| Keywords: | mobile robot localization landmark extended Kalman filter adaptive extended Kalman filte |
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