《计算机应用研究》|Application Research of Computers

基于自适应渐消无迹粒子滤波的Unscented FastSLAM算法

Unscented FastSLAM algorithm based on adaptive fading unscented particle filter

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作者 王倩,曾庆军,张家敏,姚金艺,周启润,戴晓强
机构 1.江苏科技大学 a.电子信息学院;b.计算机科学与工程学院,江苏 镇江 212003;2.江苏舾普泰克自动化科技有限公司,江苏 镇江 212003
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文章编号 1001-3695(2019)05-008-1315-04
DOI 10.19734/j.issn.1001-3695.2017.11.0742
摘要 针对机器人导航无迹快速同步定位与地图构建(Unscented FastSLAM)算法由于重采样造成样本粒子退化,进而导致估计精度下降的问题,提出一种基于自适应渐消无迹粒子滤波的Unscented FastSLAM算法。该算法将无迹粒子滤波与渐消滤波相融合产生自适应建议分布函数,同时将粒子根据权值进行优化组合,仅对组合后的部分不稳定的粒子进行系统重采样。通过这两方面使得系统在具有高度自适应性的同时保证粒子的多样性,缓解粒子的退化现象。仿真实验表明,提出算法与Unscented FastSLAM算法相比,可以用较少的粒子实现更高的SLAM的估计精度,很大程度上降低了SLAM算法的复杂度。
关键词 同步定位与地图构建; 粒子退化; 自适应渐消无迹粒子滤波; 自适应部分系统重采样
基金项目 国家自然科学基金资助项目(11574120)
江苏省自然科学基金资助项目(BK20160564)
江苏省国际科技合作项目(BZ2016031)
镇江市国际科技合作项目(GJ2015008)
本文URL http://www.arocmag.com/article/01-2019-05-008.html
英文标题 Unscented FastSLAM algorithm based on adaptive fading unscented particle filter
作者英文名 Wang Qian, Zeng Qingjun, Zhang Jiamin, Yao Jinyi, Zhou Qirun, Dai Xiaoqiang
机构英文名 1.a.School of Electronic & Information,b.School of Computer Science & Engineering,Jiangsu University of Science & Technology,Zhenjiang Jiangsu 212003,China;2.Jiangsu Shiptek Automation Technology Co,Ltd,Zhenjiang Jiangsu 212003,China
英文摘要 For the unscented fast simultaneous localization and mapping(Unscented FastSLAM) algorithm in robot navigation, the sample particle was degraded due to resampling, which led to the problem of reduced accuracy. In order to solve the problem, this paper developed an improved Unscented FastSLAM algorithm based on adaptive fading unscented particle filter. The algorithm merged the unscented particle filter with the fading filter to form adaptive proposed distribution function. At the same time, the particles were optimally combined according to their weight, and only unstable particles were resampled. Through these two aspects could make the system highly adaptive, while ensuring the diversity of particles and mitigating the degradation of particles. Simulation experiments show that compared with Unscented FastSLAM algorithm, the proposed algorithm can achieve higher SLAM estimation accuracy with fewer particles, which greatly reduces the complexity of SLAM algorithm.
英文关键词 simultaneous localization and mapping(SLAM); particle degradation; adaptive fading unscented particle filter; adaptive partial systematic resampling
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收稿日期 2017/11/17
修回日期 2017/12/26
页码 1315-1318
中图分类号 TP242.6;TP301.6
文献标志码 A