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基于稀疏扩展信息滤波的同步定位与地图创建算法研究

发表时间:2008-05-31  浏览量:2102  下载量:844
全部作者: 李久胜,王要强,李永强
作者单位: 哈尔滨工业大学电气工程及自动化学院
摘 要: 本文针对传统扩展卡尔曼滤波(extended Kalman filter,EKF)方法对机器人同步定位与地图创建(simultaneous localization and map building,SLAM)中计算复杂度大的问题,提出了一种基于稀疏扩展信息滤波(sparse extended information filter,SEIF)的SLAM算法。通过稀疏化信息矩阵,使复杂度得到有效降低。仿真结果表明该算法计算复杂度与地图中的环境特征个数无关,可以实现恒时执行,在计算时间和占用内存上远远优于EKF,尤其适用于处理复杂环境下大地图的自主机器人SLAM的问题。
关 键 词: 电力电子与电力传动;机器人导航;同步定位与地图创建;稀疏扩展信息滤波
Title: SLAM based on sparse extended information filter
Author: LI Jiusheng, WANG Yaoqiang, LI Yongqiang
Organization: School of Electrical Engineering & Automation, Harbin Institute of Technology
Abstract: Aiming at the significant computational burden of the traditional EKF-SLAM, a novel algorithm, SLAM based on SEIF(sparse extended information filter) is proposed. Through sparsification of the information matrix, computational complexity can be reduced notably. Simulation results show that SEIF-SLAM can be executed in constant time, irrespective of the size of the map, and have a better performance than EKF-SLAM on CPU time and memory usage, especially in the large map and complex environment.
Key words: power electronics and electric drives; robot navigation; simultaneous localization and map building(SLAM); sparse extended information filter(SEIF)
发表期数: 2008年9月第10期
引用格式: 李久胜,王要强,李永强. 基于稀疏扩展信息滤波的同步定位与地图创建算法研究[J]. 中国科技论文在线精品论文,2008,1(10):1200-1206.
 
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