{"title":"基于地标定位的EKF、SPKF和Bayes滤波器的比较","authors":"Chi Hay Tong, T. Barfoot","doi":"10.1109/CRV.2010.33","DOIUrl":null,"url":null,"abstract":"The conventional approach to nonlinear state estimation, the Extended Kalman Filter (EKF), is quantitatively compared to the performance of the relative newcomer, the Sigma-Point Kalman Filter (SPKF). These approaches are applied to the problem of localization of a mobile robot using a known map, and compared under the context of the practical best performance of a Bayes Filter-type method using a particle filter with a very large number of particles.","PeriodicalId":358821,"journal":{"name":"2010 Canadian Conference on Computer and Robot Vision","volume":"60 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"9","resultStr":"{\"title\":\"A Comparison of the EKF, SPKF, and the Bayes Filter for Landmark-Based Localization\",\"authors\":\"Chi Hay Tong, T. Barfoot\",\"doi\":\"10.1109/CRV.2010.33\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The conventional approach to nonlinear state estimation, the Extended Kalman Filter (EKF), is quantitatively compared to the performance of the relative newcomer, the Sigma-Point Kalman Filter (SPKF). These approaches are applied to the problem of localization of a mobile robot using a known map, and compared under the context of the practical best performance of a Bayes Filter-type method using a particle filter with a very large number of particles.\",\"PeriodicalId\":358821,\"journal\":{\"name\":\"2010 Canadian Conference on Computer and Robot Vision\",\"volume\":\"60 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-05-31\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"9\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2010 Canadian Conference on Computer and Robot Vision\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CRV.2010.33\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 Canadian Conference on Computer and Robot Vision","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CRV.2010.33","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A Comparison of the EKF, SPKF, and the Bayes Filter for Landmark-Based Localization
The conventional approach to nonlinear state estimation, the Extended Kalman Filter (EKF), is quantitatively compared to the performance of the relative newcomer, the Sigma-Point Kalman Filter (SPKF). These approaches are applied to the problem of localization of a mobile robot using a known map, and compared under the context of the practical best performance of a Bayes Filter-type method using a particle filter with a very large number of particles.
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