Neural network based FastSLAM for autonomous robots in unknown environments

CORC > 自动化研究所 > 中国科学院自动化研究所 > 复杂系统管理与控制国家重点实验室 > 先进机器人控制团队

	Neural network based FastSLAM for autonomous robots in unknown environments
	Li, Qing-Ling 1; Song, Yu 2; Hou, Zeng-Guang3
刊名	NEUROCOMPUTING
	2015-10-01
卷号	165 页码:99-110
关键词	Autonomous robot Simultaneous Localization and Mapping (SLAM) Neural network Particle filter Gaussian Weighted Integral (GWI) Cubature rule
英文摘要	Map learning and self-localization based on perception of the environment's structure are fundamental capacities required for intelligent robots to realize true autonomy. Simultaneous Localization and Mapping (SLAM) is an effective technique for such robots, as it addresses the problem of incrementally building an environment map from noisy sensory data and tracking the robot's path with the built map. As a popular SLAM solution, FastSLAM suffers from limitation on error accumulation introduced by incorrect odometry model and inaccurate linearization of the SLAM nonlinear functions. To overcome the problem, a new Jacobian free neural network (NN) based FastSLAM algorithm is derived and discussed in this paper. The main contribution of the algorithm is twofold: on the one hand, the odometry error is online compensated by using a multilayer NN, and the NN is online trained during the SLAM process; on the other hand, the third-degree Cubature rule for Gaussian weighted integral, which calculates nonlinear transition density of Gaussian prior up to the 3rd order nonlinearity, is utilized to estimate the SLAM state (i.e., the robot path and environment map) and to online train the NN compensator. The performance of proposed SLAM is investigated and compared with that of popular FastSLAM2.0 in simulations and experiments. Results show that the proposed method improves the SLAM performance. (C) 2015 Elsevier B.V. All rights reserved.
WOS标题词	Science & Technology ; Technology
类目[WOS]	Computer Science, Artificial Intelligence
研究领域[WOS]	Computer Science
关键词[WOS]	EXTENDED KALMAN FILTER ; NONHOLONOMIC MOBILE ROBOT ; SIMULTANEOUS LOCALIZATION ; TRACKING CONTROL ; SLAM PROBLEM ; ODOMETRY ; EFFICIENT ; ALGORITHM ; VISION ; ROBUST
收录类别	SCI
语种	英语
WOS记录号	WOS:000356747700013
公开日期	2015-09-22
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/7907]
专题	自动化研究所_复杂系统管理与控制国家重点实验室_先进机器人控制团队
作者单位	1.China Univ Min & Technol, Sch Mech Elect & Informat Engn, Beijing, Peoples R China 2.Beijing Jiaotong Univ, Sch Elect & Informat Engn, Beijing, Peoples R China 3.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing, Peoples R China
推荐引用方式 GB/T 7714	Li, Qing-Ling,Song, Yu,Hou, Zeng-Guang. Neural network based FastSLAM for autonomous robots in unknown environments[J]. NEUROCOMPUTING,2015,165:99-110.
APA	Li, Qing-Ling,Song, Yu,&Hou, Zeng-Guang.(2015).Neural network based FastSLAM for autonomous robots in unknown environments.NEUROCOMPUTING,165,99-110.
MLA	Li, Qing-Ling,et al."Neural network based FastSLAM for autonomous robots in unknown environments".NEUROCOMPUTING 165(2015):99-110.