DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kim, Hyongjin | ko |
dc.contributor.author | Lee, Donghwa | ko |
dc.contributor.author | Oh, Taek Jun | ko |
dc.contributor.author | Myung, Hyun | ko |
dc.date.accessioned | 2016-04-14T02:56:00Z | - |
dc.date.available | 2016-04-14T02:56:00Z | - |
dc.date.created | 2015-09-22 | - |
dc.date.created | 2015-09-22 | - |
dc.date.created | 2015-09-22 | - |
dc.date.issued | 2015-04 | - |
dc.identifier.citation | Journal of Institute of Control, Robotics and Systems, v.21, no.4, pp.367 - 371 | - |
dc.identifier.issn | 1976-5622 | - |
dc.identifier.uri | http://hdl.handle.net/10203/203712 | - |
dc.description.abstract | In this paper, a novel localization method for a monocular camera is proposed by using a feature-based probabilistic map. The localization of a camera is generally estimated from 3D-to-2D correspondences between a 3D map and an image plane through the PnP algorithm. In the computer vision communities, an accurate 3D map is generated by optimization using a large number of image dataset for camera pose estimation. In robotics communities, a camera pose is estimated by probabilistic approaches with lack of feature. Thus, it needs an extra system because the camera system cannot estimate a full state of the robot pose. Therefore, we propose an accurate localization method for a monocular camera using a probabilistic approach in the case of an insufficient image dataset without any extra system. In our system, features from a probabilistic map are projected into an image plane using linear approximation. By minimizing Mahalanobis distance between the projected features from the probabilistic map and extracted features from a query image, the accurate pose of the monocular camera is estimated from an initial pose obtained by the PnP algorithm. The proposed algorithm is demonstrated through simulations in a 3D space. | - |
dc.language | English | - |
dc.publisher | Institute of Control, Robotics and Systemsjournal@ijcas.com | - |
dc.title | Localization of a monocular camera using a feature-based probabilistic map | - |
dc.type | Article | - |
dc.identifier.scopusid | 2-s2.0-84928028735 | - |
dc.type.rims | ART | - |
dc.citation.volume | 21 | - |
dc.citation.issue | 4 | - |
dc.citation.beginningpage | 367 | - |
dc.citation.endingpage | 371 | - |
dc.citation.publicationname | Journal of Institute of Control, Robotics and Systems | - |
dc.identifier.doi | 10.5302/J.ICROS.2015.14.8035 | - |
dc.identifier.kciid | ART001978696 | - |
dc.contributor.localauthor | Myung, Hyun | - |
dc.description.isOpenAccess | N | - |
dc.subject.keywordAuthor | Camera pose estimation | - |
dc.subject.keywordAuthor | Feature projection | - |
dc.subject.keywordAuthor | Mahalanobis distance | - |
dc.subject.keywordAuthor | Probabilistic feature map | - |
dc.subject.keywordAuthor | Camera pose estimation | - |
dc.subject.keywordAuthor | Feature projection | - |
dc.subject.keywordAuthor | Mahalanobis distance | - |
dc.subject.keywordAuthor | Probabilistic feature map | - |
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