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一种改进的FastSLAM算法

Improved FastSLAM algorithm
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摘要 同时定位与建图是移动机器人实现真正自治的必要前提,FastSLAM作为一种成功的SLAM方法受到研究者的广泛青睐,FastSLAM将SLAM问题分为一个定位问题和一个建图问题,其中用扩展卡尔曼滤波器(EKF)实现地图陆标的估计与更新,提出了一种改进的FastSLAM方法,用UKF滤波器代替EKF实现FastSLAM中的陆标估计,使得陆标的估计精度提高,该方法同时具有UKF滤波器无需求解观测模型的雅克比矩阵的优点。 Simultaneous localization and mapping,for short SLAM,is a necessary prerequisite to make mobile robot truly autonomous,which is a hot research topic today.FastSLAM as a successful SLAM method abstracts many researchers＇ attentions. FastSLAM factors the SLAM problem into a localization problem and a mapping problem in which the landmark position is estimated by EKF.A modified FastSLAM is p.resented,using the UKF to replace the EKF to estimate the landmark position.So we can improve the estimation precision,at the same time no need to linearize the sensor observation mode and to compute its Jacobian matrix.
作者 康叶伟 黄亚楼 孙凤池 苑晶
机构地区 南开大学信息技术科学学院 南开大学软件学院
出处 《计算机工程与应用》 CSCD 北大核心 2007年第36期4-6,67,共4页 Computer Engineering and Applications
基金 国家自然科学基金(the National Natural Science Foundation of China under Grant No.60605021) 国家高技术研究发展计划(The National High Technology Research and Development Program of China under Grant No.2006AA04Z223)
关键词 同时定位与建图 UKF 扩展卡尔曼滤波器 FASTSLAM Simultaneous Localization and Mapping（SLAM） Unscented Kalman Filter（UKF） Extended Kalman Fiher（EKF） Fast- SLAM
分类号 TP301 [自动化与计算机技术—计算机系统结构]
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参考文献9

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二级参考文献25

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共引文献34

计算机工程与应用
2007年 第36期

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