{"title":"基于视觉的同步定位和地图构建:立体和单声道SLAM","authors":"Adnan Kalay, ilkay Ulusoy","doi":"10.1109/SIU.2009.5136348","DOIUrl":null,"url":null,"abstract":"The simultaneous operation of localization capability which serves to navigation of an autonomous robot and map building mechanism which provides an environmental model is called SLAM (Simultaneous Localization and Map Building). While various sensors are used for this algorithm, vision-based algorithms are relatively new and have attracted more attention in recent years. In this work, while a Visual SLAM algorithm utilizing Extended Kalman Filter is introduced, the two main branches, mono and stereo SLAM algorithms, are also compared.","PeriodicalId":219938,"journal":{"name":"2009 IEEE 17th Signal Processing and Communications Applications Conference","volume":"17 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2009-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Vision-based Simultaneous Localization and Map Building: Stereo and mono SLAM\",\"authors\":\"Adnan Kalay, ilkay Ulusoy\",\"doi\":\"10.1109/SIU.2009.5136348\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The simultaneous operation of localization capability which serves to navigation of an autonomous robot and map building mechanism which provides an environmental model is called SLAM (Simultaneous Localization and Map Building). While various sensors are used for this algorithm, vision-based algorithms are relatively new and have attracted more attention in recent years. In this work, while a Visual SLAM algorithm utilizing Extended Kalman Filter is introduced, the two main branches, mono and stereo SLAM algorithms, are also compared.\",\"PeriodicalId\":219938,\"journal\":{\"name\":\"2009 IEEE 17th Signal Processing and Communications Applications Conference\",\"volume\":\"17 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2009-04-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2009 IEEE 17th Signal Processing and Communications Applications Conference\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SIU.2009.5136348\",\"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 IEEE 17th Signal Processing and Communications Applications Conference","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SIU.2009.5136348","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
摘要
同时运行为自主机器人导航服务的定位能力和提供环境模型的地图构建机制被称为SLAM (simultaneous localization and map building)。虽然该算法使用了各种传感器,但基于视觉的算法相对较新,近年来受到越来越多的关注。本文在介绍一种基于扩展卡尔曼滤波的视觉SLAM算法的同时,对单声道SLAM算法和立体声SLAM算法这两个主要分支进行了比较。
Vision-based Simultaneous Localization and Map Building: Stereo and mono SLAM
The simultaneous operation of localization capability which serves to navigation of an autonomous robot and map building mechanism which provides an environmental model is called SLAM (Simultaneous Localization and Map Building). While various sensors are used for this algorithm, vision-based algorithms are relatively new and have attracted more attention in recent years. In this work, while a Visual SLAM algorithm utilizing Extended Kalman Filter is introduced, the two main branches, mono and stereo SLAM algorithms, are also compared.