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Algorithms analysis of mobile robot SLAM based on Kalman and particle filter
Zhang, Feng1; Li, Siqi1; Yuan S(袁帅)1,2; Sun, Enze1; Zhao, Languang1
Department机器人学研究室
Conference Name9th International Conference on Modelling, Identification and Control, ICMIC 2017
Conference DateJuly 10-12, 2017
Conference PlaceKunming, China
Source PublicationProceedings of 2017 9th International Conference On Modelling, Identification and Control, ICMIC 2017
PublisherIEEE
Publication PlaceNew York
2017
Pages1050-1055
Indexed ByEI
EI Accession number20183105618015
Contribution Rank2
ISBN978-1-5090-6573-8
Keywordmobile robot Simultaneous Localization and Mapping EKF-SLAM UKF-SLAM FastSLAM UFastSLAM
AbstractMany algorithms of mobile robot SLAM (Simultaneous Localization and Mapping) have been researched at present, however, the SLAM algorithm of mobile robot based on probability is often used in the unknown environment. In this paper, two kinds of SLAM algorithms based on probability are analyzed and compared. One kind is the SLAM algorithm based on Kalman filter: extended Kalman filter SLAM (EKF-SLAM) and unscented Kalman filter SLAM algorithm (UKF-SLAM). Another kind is the SLAM algorithm based on Particle filter: FastSLAM and unscented FastSLAM (UFastSLAM) algorithm. The difference from the four algorithms of SLAM is illustrated in terms of principle and calculation accuracy. Finally, the simulation results show that the UFastSLAM algorithm is superior to other algorithms in robot path and landmark estimation.
Language英语
Document Type会议论文
Identifierhttp://ir.sia.cn/handle/173321/22354
Collection机器人学研究室
Corresponding AuthorLi, Siqi
Affiliation1.Information and Control Engineering Faculty, Shenyang Jianzhu University, Shenyang 110168, China
2.State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
Recommended Citation
GB/T 7714
Zhang, Feng,Li, Siqi,Yuan S,et al. Algorithms analysis of mobile robot SLAM based on Kalman and particle filter[C]. New York:IEEE,2017:1050-1055.
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