Visual-inertial RGB-D SLAM for mobile augmented reality
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Williem, | - |
dc.contributor.author | Ivan, Andre | - |
dc.contributor.author | Seok, Hochang | - |
dc.contributor.author | Lim, Jongwoo | - |
dc.contributor.author | Yoon, Kuk-Jin | - |
dc.contributor.author | Cho, Ikhwan | - |
dc.contributor.author | Park, In Kyu | - |
dc.date.accessioned | 2022-07-12T00:18:53Z | - |
dc.date.available | 2022-07-12T00:18:53Z | - |
dc.date.created | 2021-05-11 | - |
dc.date.issued | 2018-05 | - |
dc.identifier.issn | 0302-9743 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/150145 | - |
dc.description.abstract | This paper presents a practical framework for occlusion-aware augmented reality application using visual-inertial RGB-D SLAM. First, an efficient visual SLAM framework with map merging based relocalization is introduced. When the pose estimation fails, a new environment map is generated. Then, a map merging is performed to merge the current and previous environment maps if a loop closure is detected. The framework is then integrated with the inertial information to solve the missing environment map problem. Camera pose is approximated using the angular velocity and translational acceleration value when the pose estimation fails. Experimental results show that the proposed method can perform well in the presence of missing pose. Finally, an occlusion-aware augmented reality application is built over the SLAM framework. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | Springer Verlag | - |
dc.title | Visual-inertial RGB-D SLAM for mobile augmented reality | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Lim, Jongwoo | - |
dc.identifier.doi | 10.1007/978-3-319-77383-4_91 | - |
dc.identifier.scopusid | 2-s2.0-85047444903 | - |
dc.identifier.bibliographicCitation | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v.10736 LNCS, pp.928 - 938 | - |
dc.relation.isPartOf | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | - |
dc.citation.title | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | - |
dc.citation.volume | 10736 LNCS | - |
dc.citation.startPage | 928 | - |
dc.citation.endPage | 938 | - |
dc.type.rims | ART | - |
dc.type.docType | Conference Paper | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scopus | - |
dc.subject.keywordPlus | Merging | - |
dc.subject.keywordPlus | Augmented reality applications | - |
dc.subject.keywordPlus | Environment maps | - |
dc.subject.keywordPlus | Loop closure | - |
dc.subject.keywordPlus | Map merging | - |
dc.subject.keywordPlus | Mobile augmented reality | - |
dc.subject.keywordPlus | Pose estimation | - |
dc.subject.keywordPlus | Re-localization | - |
dc.subject.keywordPlus | Visual SLAM | - |
dc.subject.keywordPlus | Augmented reality | - |
dc.identifier.url | https://link.springer.com/chapter/10.1007/978-3-319-77383-4_91 | - |
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