{"title":"基于FastSLAM 2.0的二维室内定位与制图研究","authors":"Dwi Kumiawan, A. N. Jati, U. Sunarya","doi":"10.1109/ICCEREC.2016.7814991","DOIUrl":null,"url":null,"abstract":"FastSLAM 2.0 is a Simultaneous Localization And Mapping (SLAM) framework that provides an algorithm for calculating robot's pose and map the environment concurrently. It consists of calculation that include getting data from sensor, associate the data, and update the map. SLAM is a challenging problem in Robotics because it is considered a chicken-and-egg problem. In this paper, ite will be studied the FastSLAM 2.0 algorithm that will be using RANSAC (Random Sampling Consensus) for its feature extraction.","PeriodicalId":431878,"journal":{"name":"2016 International Conference on Control, Electronics, Renewable Energy and Communications (ICCEREC)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"A study of 2D indoor localization and mapping using FastSLAM 2.0\",\"authors\":\"Dwi Kumiawan, A. N. Jati, U. Sunarya\",\"doi\":\"10.1109/ICCEREC.2016.7814991\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"FastSLAM 2.0 is a Simultaneous Localization And Mapping (SLAM) framework that provides an algorithm for calculating robot's pose and map the environment concurrently. It consists of calculation that include getting data from sensor, associate the data, and update the map. SLAM is a challenging problem in Robotics because it is considered a chicken-and-egg problem. In this paper, ite will be studied the FastSLAM 2.0 algorithm that will be using RANSAC (Random Sampling Consensus) for its feature extraction.\",\"PeriodicalId\":431878,\"journal\":{\"name\":\"2016 International Conference on Control, Electronics, Renewable Energy and Communications (ICCEREC)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2016 International Conference on Control, Electronics, Renewable Energy and Communications (ICCEREC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICCEREC.2016.7814991\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 International Conference on Control, Electronics, Renewable Energy and Communications (ICCEREC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCEREC.2016.7814991","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A study of 2D indoor localization and mapping using FastSLAM 2.0
FastSLAM 2.0 is a Simultaneous Localization And Mapping (SLAM) framework that provides an algorithm for calculating robot's pose and map the environment concurrently. It consists of calculation that include getting data from sensor, associate the data, and update the map. SLAM is a challenging problem in Robotics because it is considered a chicken-and-egg problem. In this paper, ite will be studied the FastSLAM 2.0 algorithm that will be using RANSAC (Random Sampling Consensus) for its feature extraction.
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