{"title":"MCL中的活性粒子:一个进化的观点","authors":"Huaqing Min, Huan Chen, Ronghua Luo","doi":"10.1109/ICINFA.2009.5205079","DOIUrl":null,"url":null,"abstract":"Mobile robot localization is the task of determining a robot's pose in a known environment, which is one of the most important problems in mobile robotics. The state-of-the-art Monte Carlo Localization (MCL) algorithm requires a large amount of particles and thus converges slowly. Also, MCL performs poorly in low noise sensor input.","PeriodicalId":223425,"journal":{"name":"2009 International Conference on Information and Automation","volume":"11 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2009-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"Active particle in MCL: An evolutionary view\",\"authors\":\"Huaqing Min, Huan Chen, Ronghua Luo\",\"doi\":\"10.1109/ICINFA.2009.5205079\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Mobile robot localization is the task of determining a robot's pose in a known environment, which is one of the most important problems in mobile robotics. The state-of-the-art Monte Carlo Localization (MCL) algorithm requires a large amount of particles and thus converges slowly. Also, MCL performs poorly in low noise sensor input.\",\"PeriodicalId\":223425,\"journal\":{\"name\":\"2009 International Conference on Information and Automation\",\"volume\":\"11 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2009-06-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2009 International Conference on Information and Automation\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICINFA.2009.5205079\",\"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 Information and Automation","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICINFA.2009.5205079","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Mobile robot localization is the task of determining a robot's pose in a known environment, which is one of the most important problems in mobile robotics. The state-of-the-art Monte Carlo Localization (MCL) algorithm requires a large amount of particles and thus converges slowly. Also, MCL performs poorly in low noise sensor input.
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