| 基于稀疏扩展信息滤波和粒子滤波的SLAM算法 |
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| 引用本文： | 朱代先,王晓华.基于稀疏扩展信息滤波和粒子滤波的SLAM算法[J].计算机应用,2012,32(5):1325-1328. |
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| 作者姓名： | 朱代先 王晓华 |
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| 作者单位： | 1. 西安科技大学 通信与信息工程学院，西安 710054
2. 西安工程大学 电子信息学院，西安 710048 |
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| 基金项目： | 国家自然科学基金资助项目(61101146);陕西省教育厅自然科学专项(2010JK666) |
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| 摘 要： | 针对传统粒子滤波算法单次迭代过程中仅应用到当前的信息，且小权值粒子代表的信息在重采样中被删除而导致信息不能充分利用的问题，提出了稀疏扩展信息滤波和粒子滤波相结合的同时定位与地图创建（SLAM）算法，信息矩阵记忆了机器人位姿的历史信息，应用Gibbs采样重新获得粒子集，使粒子集能够更好地描述后验分布，提高算法的状态估计精度。大量的Monte-Carlo仿真实验验证了该算法中机器人定位精度较FastSLAM2.0算法提高80%左右。
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| 关 键 词： | 同时定位与地图创建 稀疏扩展信息滤波 粒子滤波 Gibbs采样 |
| 收稿时间： | 2011-10-24 |
| 修稿时间： | 2011-12-02 |
SLAM algorithm based on sparse extended information filter and particle filter |
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| ZHU Dai-xian
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WANG Xiao-hua.SLAM algorithm based on sparse extended information filter and particle filter[J].journal of Computer Applications,2012,32(5):1325-1328. |
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| Authors: | ZHU Dai-xian WANG Xiao-hua |
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| Affiliation: | 1. Communication and Information Engineering College, Xi'an University of Science and Technology, Xi'an Shaanxi 710054, China
2. School of Electronics and Information, Xi'an Polytechic University, Xi'an Shaanxi 710048, China |
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| Abstract: | In the traditional particle filter algorithm,there is a problem that historical information can not be fully utilized because of small weight particle removed in resample and single iteration.A Simultaneous Localization And Mapping(SLAM) algorithm based on sparse extended information filter combing particle filter was brought forward.Information matrix maintained historical information,particles containing historical information obtained by Gibbs sampling,particles set described the posterior distribution well,and the accuracy of state estimation SLAM algorithm was improved.Plentiful Monte-Carlo simulations were carried out to show the positioning accuracy of this method is about 80% more than FastSLAM2.0 algorithm’s. |
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| Keywords: | Simultaneous Localization And Mapping(SALM) sparse extended information filter particle filter Gibbs sampling |
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