Robust Iterated Sigma Point FastSLAM Algorithm for Mobile Robot Simultaneous Localization and Mapping

	Robust Iterated Sigma Point FastSLAM Algorithm for Mobile Robot Simultaneous Localization and Mapping
	Song Yu 1,2; Song Yongduan 1; Li Qingling 3
发表期刊	CHINESE JOURNAL OF MECHANICAL ENGINEERING
	2011-07-01
卷号	24 期号:4 页码:693-700
文章类型	Article
摘要	Simultaneous localization and mapping (SLAM) is a key technology for mobile robots operating under unknown environment. While FastSLAM algorithm is a popular solution to the SLAM problem, it suffers from two major drawbacks: one is particle set degeneracy due to lack of observation information in proposal distribution design of the particle filter; the other is errors accumulation caused by linearization of the nonlinear robot motion model and the nonlinear environment observation model. For the purpose of overcoming the above problems, a new iterated sigma point FastSLAM (ISP-FastSLAM) algorithm is proposed. The main contribution of the algorithm lies in the utilization of iterated sigma point Kalman filter (ISPKF), which minimizes statistical linearization error through Gaussian-Newton iteration, to design an optimal proposal distribution of the particle filter and to estimate the environment landmarks. On the basis of Rao-Blackwellized particle filter, the proposed ISP-FastSLAM algorithm is comprised by two main parts: in the first part, an iterated sigma point particle filter (ISPPF) to localize the robot is proposed, in which the proposal distribution is accurately estimated by the ISPKF; in the second part, a set of ISPKFs is used to estimate the environment landmarks. The simulation test of the proposed ISP-FastSLAM algorithm compared with FastSLAM2.0 algorithm and Unscented FastSLAM algorithm is carried out, and the performances of the three algorithms are compared. The simulation and comparing results show that the proposed ISP-FastSLAM outperforms other two algorithms both in accuracy and in robustness. The proposed algorithm provides reference for the optimization research of FastSLAM algorithm.
关键词	Mobile Robot Simultaneous Localization And Mapping (Slam) Particle Filter Kalman Filter Unscented Transformation
WOS标题词	Science & Technology ; Technology
关键词[WOS]	SLAM
收录类别	SCI
语种	英语
WOS研究方向	Engineering
WOS类目	Engineering, Mechanical
WOS记录号	WOS:000293252100024
引用统计	被引频次：8[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/3456
专题	复杂系统认知与决策实验室_先进机器人
作者单位	1.Beijing Jiaotong Univ, Ctr Intelligent Syst & Renewable Energy, Beijing 100044, Peoples R China 2.Harbin Inst Technol, State Key Lab Robot & Syst, Harbin 150080, Peoples R China 3.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Song Yu,Song Yongduan,Li Qingling. Robust Iterated Sigma Point FastSLAM Algorithm for Mobile Robot Simultaneous Localization and Mapping[J]. CHINESE JOURNAL OF MECHANICAL ENGINEERING,2011,24(4):693-700.
APA	Song Yu,Song Yongduan,&Li Qingling.(2011).Robust Iterated Sigma Point FastSLAM Algorithm for Mobile Robot Simultaneous Localization and Mapping.CHINESE JOURNAL OF MECHANICAL ENGINEERING,24(4),693-700.
MLA	Song Yu,et al."Robust Iterated Sigma Point FastSLAM Algorithm for Mobile Robot Simultaneous Localization and Mapping".CHINESE JOURNAL OF MECHANICAL ENGINEERING 24.4(2011):693-700.