{"title":"基于EKF的移动机器人定位","authors":"Ling Chen, Huosheng Hu, K. Mcdonald-Maier","doi":"10.1109/EST.2012.19","DOIUrl":null,"url":null,"abstract":"Localization plays a significant role in the autonomous navigation of a mobile robot. This paper investigates mobile robot localization based on Extended Kalman Filter(EKF) algorithm and a feature based map. Corner angles in the environment are detected as the features, and the detailed processes of feature extraction are described. Then the motion model and odometry information are elaborated, and the EKF localization algorithm is presented. Finally, the experimental result is given to verify the feasibility and performance of the proposed localization algorithm.","PeriodicalId":314247,"journal":{"name":"2012 Third International Conference on Emerging Security Technologies","volume":"125 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"18","resultStr":"{\"title\":\"EKF Based Mobile Robot Localization\",\"authors\":\"Ling Chen, Huosheng Hu, K. Mcdonald-Maier\",\"doi\":\"10.1109/EST.2012.19\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Localization plays a significant role in the autonomous navigation of a mobile robot. This paper investigates mobile robot localization based on Extended Kalman Filter(EKF) algorithm and a feature based map. Corner angles in the environment are detected as the features, and the detailed processes of feature extraction are described. Then the motion model and odometry information are elaborated, and the EKF localization algorithm is presented. Finally, the experimental result is given to verify the feasibility and performance of the proposed localization algorithm.\",\"PeriodicalId\":314247,\"journal\":{\"name\":\"2012 Third International Conference on Emerging Security Technologies\",\"volume\":\"125 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-09-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"18\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2012 Third International Conference on Emerging Security Technologies\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/EST.2012.19\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 Third International Conference on Emerging Security Technologies","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/EST.2012.19","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Localization plays a significant role in the autonomous navigation of a mobile robot. This paper investigates mobile robot localization based on Extended Kalman Filter(EKF) algorithm and a feature based map. Corner angles in the environment are detected as the features, and the detailed processes of feature extraction are described. Then the motion model and odometry information are elaborated, and the EKF localization algorithm is presented. Finally, the experimental result is given to verify the feasibility and performance of the proposed localization algorithm.
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