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Neural network based FastSLAM for autonomous robots in unknown environments
Li, Qing-Ling1; Song, Yu2; Hou, Zeng-Guang3
Source PublicationNEUROCOMPUTING
2015-10-01
Volume2015Issue:165Pages:99-110
SubtypeArticle
AbstractMap 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.
KeywordAutonomous Robot Simultaneous Localization And Mapping (Slam) Neural Network Particle Filter Gaussian Weighted Integral (Gwi) Cubature Rule
WOS HeadingsScience & Technology ; Technology
WOS KeywordEXTENDED KALMAN FILTER ; NONHOLONOMIC MOBILE ROBOT ; SIMULTANEOUS LOCALIZATION ; TRACKING CONTROL ; SLAM PROBLEM ; ODOMETRY ; EFFICIENT ; ALGORITHM ; VISION ; ROBUST
Indexed BySCI
Language英语
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence
WOS IDWOS:000356747700013
Citation statistics
Cited Times:4[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/7907
Collection复杂系统管理与控制国家重点实验室_先进机器人
Affiliation1.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
Recommended Citation
GB/T 7714
Li, Qing-Ling,Song, Yu,Hou, Zeng-Guang. Neural network based FastSLAM for autonomous robots in unknown environments[J]. NEUROCOMPUTING,2015,2015(165):99-110.
APA Li, Qing-Ling,Song, Yu,&Hou, Zeng-Guang.(2015).Neural network based FastSLAM for autonomous robots in unknown environments.NEUROCOMPUTING,2015(165),99-110.
MLA Li, Qing-Ling,et al."Neural network based FastSLAM for autonomous robots in unknown environments".NEUROCOMPUTING 2015.165(2015):99-110.
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