{"title":"基于未知里程统计量的移动机器人定位自适应滤波","authors":"R. Caballero, D. Rodríguez-Losada, F. Matía","doi":"10.1109/CONIELECOMP.2009.53","DOIUrl":null,"url":null,"abstract":"One of the most important tasks in mobile robotics is the vehicle self localization from a reference frame system. In this sense, most of the mobile robots fuse odometry sensors with laser range finders or sonar sensors. Nevertheless, the odometry and kinematic model error statistics are usually unknown and time variant. An Adaptive Extended Kalman Filter is proposed for Mobile Robot Localization and the first and second moment of odometry sensors noise estimation.","PeriodicalId":292855,"journal":{"name":"2009 International Conference on Electrical, Communications, and Computers","volume":"42 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2009-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Adaptive Filtering for Mobile Robot Localization with Unknown Odometry Statistics\",\"authors\":\"R. Caballero, D. Rodríguez-Losada, F. Matía\",\"doi\":\"10.1109/CONIELECOMP.2009.53\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"One of the most important tasks in mobile robotics is the vehicle self localization from a reference frame system. In this sense, most of the mobile robots fuse odometry sensors with laser range finders or sonar sensors. Nevertheless, the odometry and kinematic model error statistics are usually unknown and time variant. An Adaptive Extended Kalman Filter is proposed for Mobile Robot Localization and the first and second moment of odometry sensors noise estimation.\",\"PeriodicalId\":292855,\"journal\":{\"name\":\"2009 International Conference on Electrical, Communications, and Computers\",\"volume\":\"42 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2009-02-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2009 International Conference on Electrical, Communications, and Computers\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CONIELECOMP.2009.53\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2009 International Conference on Electrical, Communications, and Computers","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CONIELECOMP.2009.53","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Adaptive Filtering for Mobile Robot Localization with Unknown Odometry Statistics
One of the most important tasks in mobile robotics is the vehicle self localization from a reference frame system. In this sense, most of the mobile robots fuse odometry sensors with laser range finders or sonar sensors. Nevertheless, the odometry and kinematic model error statistics are usually unknown and time variant. An Adaptive Extended Kalman Filter is proposed for Mobile Robot Localization and the first and second moment of odometry sensors noise estimation.
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