全部 标题 作者
关键词 摘要

OALib Journal期刊
ISSN: 2333-9721
费用:99美元

查看量下载量

相关文章

更多...

一种改进的粒子滤波SLAM算法

, PP. 537-542

Keywords: 同时定位与地图创建(SLAM),历史信息,粒子滤波

Full-Text   Cite this paper   Add to My Lib

Abstract:

传统的粒子滤波SLAM算法中,由于历史信息未被利用而导致估计精度较低。文中结合精确稀疏滞后状态信息滤波具有自然稀疏的信息矩阵因而估计精度高以及精确稀疏扩展信息滤波计算效率高的优点,将二者混合应用于粒子滤波SLAM算法中。不但充分应用信息矩阵记录的机器人位姿与特征间关系的历史信息从而提高估计的精度,而且克服机器人转动状态及环境特征疏密带来的应用缺陷。仿真与真实机器人实验的实验结果均表明文中算法的有效性与可行性。

References

[1]  Arulampalam S M,Maskell S,Gordon N,et al. A Tutorial on Particle Filters for Online Non-Linear/Non-Gaussian Bayesian Tracking. IEEE Trans on Signal Processing,2002,50(2): 174-188
[2]  Pit M K,Shephard N. Filtering via Simulation: Auxiliary Particle Filters. Journal of the American Statistical Association,1999,94(446): 590-599
[3]  Musso C,Oudjane N,Legland F. Improving Regularized Particle Filters[EB/OL].[2012-09-30].http://www.irisa.fr/aspi/legland/pub/smc-book.ps.gz
[4]  Martinez-Cantim R,de Freitas N,Castellanos J A. Analysis of Particle Methods for Simultaneous Robot Localization and Mapping and a New Algorithm: Marginal-SLAM // Proc of the IEEE International Conference on Robotics and Automation. Roma,Italy,2007: 2415-2420
[5]  Zhou Wu. A Study on Simultaneous Localization and Map Building for Intelligent Mobile Robots. Ph.D Dissertation.Nanjing,China: Nanjing University of Science Technology,2009(in Chinese)(周 武.面向智能移动机器人的同时定位和地图创建研究.博士学位论文.南京:南京理工大学,2009)
[6]  Li Maohai,Hong Bingrong,Luo Ronghua. Improved Rao-Blackwellized Particle Filters for Mobile Robot Simultaneous Localization and Mapping. Journal of Jilin University: Engineering and Technology Edition,2007,37(2): 401-406(in Chinese)(厉茂海,洪炳熔,罗荣华.用改进的Rao-Blackwellized粒子滤波器实现移动机器人同时定位和地图创建.吉林大学学报:工学版,2007,37(2): 401-406)
[7]  Zhou Wu,Zhao Chunxia. A FastSLAM 2.0 Algorithm Based on Genetic Algorithm. Robots,2009,31(1): 25-32(in Chinese)(周 武,赵春霞.一种基于遗传算法的FastSLAM2.0算法.机器人,2009,31(1): 25-32)[8]Zhu Daixian,Wang Xiaohua. SLAM Algorithm Based on Sparse Extended Information Filter and Particle Filter. Journal of Computer Applications,2012,32(5): 1325-1328(in Chinese) (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.计算机应用,2012,32(5): 1325-1328)
[8]  Zhu Daixian,Wang Xiaohua. Research on the Particle Filter SLAM Algorithm Based on Exactly Sparse Extended Information Filter. Computer Engineering and Science,2012,34(7): 140-145(in Chinese) (朱代先,王晓华.基于精确稀疏扩展信息滤波和粒子滤波的SLAM算法研究.计算机工程与科学,2012,34(7): 140-145)
[9]  Walter M R,Eustice R M,Leonard J J. Exactly Sparse Extended Information Filters for Feature-Based SLAM. The International Journal of Robotics Research,2007,26(4): 335-339
[10]  Eustice 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
[11]  Guo Jianhui,Zhao Chunxia. An Improved SLAM Algorithm with Sparse Extended Information Filters. Pattern Recognition and Artificial Intelligence,2009,22(2): 263-269(in Chinese)(郭剑辉,赵春霞.一种改进的稀疏扩展信息滤波SLAM算法.模式识别与人工智能,2009,22(2): 263-269)
[12]  Guo Jianhui,Zhao Chunxia,Shi Xingxi.Sparsification Rules of Sparse Extended Information Filters SLAM Algorithms. Journal of System Simulation,2008,20(24): 1132-1136(in Chinese)(郭剑辉,赵春霞,石杏喜.稀疏扩展信息滤波SLAM算法的稀疏规则研究.系统仿真学报,2008,20(24): 1132-1136)
[13]  Estrada C,Neira J,Tardos J D. Hierarchical SLAM: Real-Time Accurate Mapping of Large Environments. IEEE Trans on Robotics,2005,21(4): 588-596
[14]  Wang Xiaohua,Fu Weiping. Data Association Method of SLAM Based on Improved Minimal Connected Dominating Set. Journal of Computer Applications, 2010,30(9): 294-296(in Chinese)(王晓华,傅卫平.改进的极小连通支配集SLAM 数据关联方法.计算机应用,2010,30(9): 294-296)
[15]  Lowe D G.Distinctive Image Features from Scale-Invariant Keypoints. International Journal on Computer Vision,2004,60(2):91-110

Full-Text

Contact Us

service@oalib.com

QQ:3279437679

WhatsApp +8615387084133