E. Mouragnon, M. Lhuillier, M. Dhome, F. Dekeyser, P. Sayd
{"title":"Monocular Vision Based SLAM for Mobile Robots","authors":"E. Mouragnon, M. Lhuillier, M. Dhome, F. Dekeyser, P. Sayd","doi":"10.1109/ICPR.2006.810","DOIUrl":null,"url":null,"abstract":"This paper describes a new vision based method for the simultaneous localization and mapping of mobile robots. The only data used is a video input from a moving calibrated monocular camera. From the detection and matching of interest points in images at video rate, robust estimates of the camera poses are computed in real-time and a 3D map of the environment is reconstructed. The computed 3D structure is constantly refined thanks to the introduction of a fast and local bundle adjustment method that makes this approach particularly accurate and reliable. Actually, this method can be seen as a new visual tool that may be used in conjunction with usual systems (GPS, inertia sensors, etc) in SLAM applications","PeriodicalId":236033,"journal":{"name":"18th International Conference on Pattern Recognition (ICPR'06)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2006-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"69","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"18th International Conference on Pattern Recognition (ICPR'06)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICPR.2006.810","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 69
Abstract
This paper describes a new vision based method for the simultaneous localization and mapping of mobile robots. The only data used is a video input from a moving calibrated monocular camera. From the detection and matching of interest points in images at video rate, robust estimates of the camera poses are computed in real-time and a 3D map of the environment is reconstructed. The computed 3D structure is constantly refined thanks to the introduction of a fast and local bundle adjustment method that makes this approach particularly accurate and reliable. Actually, this method can be seen as a new visual tool that may be used in conjunction with usual systems (GPS, inertia sensors, etc) in SLAM applications
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