{"title":"基于SIFT特征的FastSLAM在线视觉运动估计","authors":"T. Barfoot","doi":"10.1109/IROS.2005.1545444","DOIUrl":null,"url":null,"abstract":"This paper describes a technique to estimate the 3D motion of a vehicle using odometric sensors and a stereo camera. The algorithm falls into the category of simultaneous localization and mapping as a large database of visual landmarks is created. The algorithm has been field tested online on a rover traversing loose terrain in the presence of obstacles. The resulting position estimation errors are between 0.5% and 4% of distance travelled, a significant improvement over odometry alone.","PeriodicalId":189219,"journal":{"name":"2005 IEEE/RSJ International Conference on Intelligent Robots and Systems","volume":"146 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2005-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"111","resultStr":"{\"title\":\"Online visual motion estimation using FastSLAM with SIFT features\",\"authors\":\"T. Barfoot\",\"doi\":\"10.1109/IROS.2005.1545444\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper describes a technique to estimate the 3D motion of a vehicle using odometric sensors and a stereo camera. The algorithm falls into the category of simultaneous localization and mapping as a large database of visual landmarks is created. The algorithm has been field tested online on a rover traversing loose terrain in the presence of obstacles. The resulting position estimation errors are between 0.5% and 4% of distance travelled, a significant improvement over odometry alone.\",\"PeriodicalId\":189219,\"journal\":{\"name\":\"2005 IEEE/RSJ International Conference on Intelligent Robots and Systems\",\"volume\":\"146 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2005-12-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"111\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2005 IEEE/RSJ International Conference on Intelligent Robots and Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IROS.2005.1545444\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2005 IEEE/RSJ International Conference on Intelligent Robots and Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IROS.2005.1545444","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Online visual motion estimation using FastSLAM with SIFT features
This paper describes a technique to estimate the 3D motion of a vehicle using odometric sensors and a stereo camera. The algorithm falls into the category of simultaneous localization and mapping as a large database of visual landmarks is created. The algorithm has been field tested online on a rover traversing loose terrain in the presence of obstacles. The resulting position estimation errors are between 0.5% and 4% of distance travelled, a significant improvement over odometry alone.
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