Abstract The estimation accuracy of the conventional particle filter algorithm is low because the historical information is not fully utilized. Combining the high estimation accuracy of exactly sparse delayed-state filter(ESDF) and the high efficiency of exactly sparse extended information filter(ESEIF),an improved particle filter SLAM algorithm is proposed. In this algorithm,the information matrix of ESDF,maintaining the historical relationship of robot pose and characteristics,improves the accuracy of the estimate,and ESEIF overcomes the defects of robot rotational state and characteristics density.Results of both emulational and factual experiments show that the proposed algorithm is valid and feasible.
[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) [9] 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) [10] 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 [11] 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 [12] 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) [13] 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) [14] 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 [15] 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) [16] Lowe D G.Distinctive Image Features from Scale-Invariant Keypoints. International Journal on Computer Vision,2004,60(2):91-110
2013, Vol. 26 
