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一种改进的稀疏扩展信息滤波SLAM算法 被引量：6

An Improved SLAM Algorithm with Sparse Extended Information Filters
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摘要 如何得到精确一致的稀疏信息矩阵是稀疏扩展信息滤波同时定位与地图创建(SLAM)算法的关键.在对相关性进行详细深入分析的基础上,提出一种改进的信息矩阵稀疏规则.该规则利用稀疏时刻的观测信息,从全局上保留了与机器人相关性最强的特征.在不增加计算负担的情况下,提高算法的精度及一致性.最后,通过大量的Monte-Carlo仿真实验,验证该方法的有效性. How to achieve a sparse information matrix exactly is a key issue in sparse extended information filter （SEIF） simultaneous localization and map building （SLAM）. A sparsification rule is put forward based on the deep analysis of correlation. The rule can utilize observation information of sparsification time, observe the correlation globally and reserve the features with the strongest correlation. The precision and consistency of the algorithm are improved without an increase of computational burden. Results of Monte-Carlo simulation experiments indicate the validity of the improved algorithm.
作者 郭剑辉 赵春霞
机构地区 南京理工大学计算机科学与技术学院
出处 《模式识别与人工智能》 EI CSCD 北大核心 2009年第2期263-269,共7页 Pattern Recognition and Artificial Intelligence
基金 国家863计划资助项目(No.2006AA04Z238)
关键词 稀疏扩展信息滤波(SEIF) 同时定位与地图创建(SLAM) 稀疏规则 相关性 Sparse Extended Information Filter （SEIF） （SLAM）, Sparsification Rule, Correlation , Simultaneous Localization and Map Building
分类号 TP242 [自动化与计算机技术—检测技术与自动化装置]
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参考文献14

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二级参考文献33

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共引文献75

同被引文献37

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  • 6Eustice R M, Singh H, Leonard J J. Exactly Sparse Delayed-State Filters for View-Based SLAM. IEEE Trans on Robotics, 2006, 22 (6) : 1100-1114.
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  • 10Thrun S, Liu Y, Koller D, et al. Simultaneous localization and mapping with sparse extended information filters[J].International Journal of Robotics Research, 2004, 23(7/8) 693-716.

引证文献6

二级引证文献7

模式识别与人工智能
2009年 第2期

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