{"title":"Integration of Vision based SLAM and Nonlinear Filter for Simple Mobile Robot Navigation","authors":"D. Won, Young Jae Lee, S. Sung, T. Kang","doi":"10.1109/NAECON.2008.4806575","DOIUrl":null,"url":null,"abstract":"Simultaneous Localization and Mapping (SLAM) is a widely used technique which can build up a map at unknown environment and figure out its position without prior map information. By applying SLAM method, this paper presents simple integrated navigation system for planar mobile robot equipped with vision sensor and encoders. Because of the nonlinearity of the measurement model, it is used a particle filter which has powerful performance under nonlinear and multi-modal conditions. Finally, the DR (dead reckoning) performance is demonstrated by analyzing the variance of estimated position error when the number of particles and feature points are changed.","PeriodicalId":254758,"journal":{"name":"2008 IEEE National Aerospace and Electronics Conference","volume":"64 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2008-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2008 IEEE National Aerospace and Electronics Conference","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/NAECON.2008.4806575","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 6
Abstract
Simultaneous Localization and Mapping (SLAM) is a widely used technique which can build up a map at unknown environment and figure out its position without prior map information. By applying SLAM method, this paper presents simple integrated navigation system for planar mobile robot equipped with vision sensor and encoders. Because of the nonlinearity of the measurement model, it is used a particle filter which has powerful performance under nonlinear and multi-modal conditions. Finally, the DR (dead reckoning) performance is demonstrated by analyzing the variance of estimated position error when the number of particles and feature points are changed.
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