{"title":"An evaluation of 2D indoor localization and mapping using FastSLAM","authors":"M. Amanda, A. N. Jati, U. Sunarya","doi":"10.1109/ICCEREC.2016.7814992","DOIUrl":null,"url":null,"abstract":"This research is purposed to evaluate the exploration of an unknown environment using FastSLAM for virtual mobile robot. Mapping and localization can be done simultaneously by FastSLAM algorithm. FastSLAM is an alternative to solve the previous algorithm - SLAM. The result obtained by simulating the virtual robot for mapping and localization using FastSLAM and RANSAC (Random Sampling Consensus) for its feature extraction. In order to describe the performances of FastSLAM and provides insight of weaknesses and strength of this algorithm.","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.2016.7814992","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
This research is purposed to evaluate the exploration of an unknown environment using FastSLAM for virtual mobile robot. Mapping and localization can be done simultaneously by FastSLAM algorithm. FastSLAM is an alternative to solve the previous algorithm - SLAM. The result obtained by simulating the virtual robot for mapping and localization using FastSLAM and RANSAC (Random Sampling Consensus) for its feature extraction. In order to describe the performances of FastSLAM and provides insight of weaknesses and strength of this algorithm.