{"title":"基于kld采样和SAMCL的混合自适应粒子滤波器","authors":"A. W. Li, G. S. Bastos","doi":"10.1109/ICAR.2017.8023503","DOIUrl":null,"url":null,"abstract":"The purpose of this paper is to present a hybrid method of a particle filter for localization in mobile robotics. The main references are the particle filter based on Kullback-Leibler divergence and a self-adaptive particle filter using grid-energy. Gains and drawbacks of each method are discussed and compared with the developed algorithm. This hybrid particle filter explores the best quality of each method and the final result brings a solution to the localization problem: position tracking, global localization and kidnapping in a deterministic environment. This work was developed using the ROS framework (Robot Operating System) and tested with a Pioneer 3DX robot in real and simulation environments.","PeriodicalId":198633,"journal":{"name":"2017 18th International Conference on Advanced Robotics (ICAR)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":"{\"title\":\"A hybrid self-adaptive particle filter through KLD-sampling and SAMCL\",\"authors\":\"A. W. Li, G. S. Bastos\",\"doi\":\"10.1109/ICAR.2017.8023503\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The purpose of this paper is to present a hybrid method of a particle filter for localization in mobile robotics. The main references are the particle filter based on Kullback-Leibler divergence and a self-adaptive particle filter using grid-energy. Gains and drawbacks of each method are discussed and compared with the developed algorithm. This hybrid particle filter explores the best quality of each method and the final result brings a solution to the localization problem: position tracking, global localization and kidnapping in a deterministic environment. This work was developed using the ROS framework (Robot Operating System) and tested with a Pioneer 3DX robot in real and simulation environments.\",\"PeriodicalId\":198633,\"journal\":{\"name\":\"2017 18th International Conference on Advanced Robotics (ICAR)\",\"volume\":\"6 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-07-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"8\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 18th International Conference on Advanced Robotics (ICAR)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICAR.2017.8023503\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 18th International Conference on Advanced Robotics (ICAR)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICAR.2017.8023503","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A hybrid self-adaptive particle filter through KLD-sampling and SAMCL
The purpose of this paper is to present a hybrid method of a particle filter for localization in mobile robotics. The main references are the particle filter based on Kullback-Leibler divergence and a self-adaptive particle filter using grid-energy. Gains and drawbacks of each method are discussed and compared with the developed algorithm. This hybrid particle filter explores the best quality of each method and the final result brings a solution to the localization problem: position tracking, global localization and kidnapping in a deterministic environment. This work was developed using the ROS framework (Robot Operating System) and tested with a Pioneer 3DX robot in real and simulation environments.
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