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dc.contributor.authorLi, YSen_US
dc.contributor.authorBaciu, Gen_US
dc.contributor.authorHan, Yen_US
dc.contributor.authorLi, CHen_US
dc.identifier.citationInternational journal of software science and computational intelligence, 2018, v. 10, no. 3, p. 24-40en_US
dc.description.abstractThis article describes a novel 3D image-based indoor localization system integrated with an improved SfM (structure from motion) approach and an obstacle removal component. In contrast with existing state-of-the-art localization techniques focusing on static outdoor or indoor environments, the adverse effects, generated by moving obstacles in busy indoor spaces, are considered in this work. In particular, the problem of occlusion removal is converted into a separation problem of moving foreground and static background. A low-rank and sparse matrix decomposition approach is used to solve this problem efficiently. Moreover, a SfM with RT (re-triangulation) is adopted in order to handle the drifting problem of incremental SfM method in indoor scene reconstruction. To evaluate the performance of the system, three data sets and the corresponding query sets are established to simulate different states of the indoor environment. Quantitative experimental results demonstrate that both query registration rate and localization accuracy increase significantly after integrating the authors' improvements.en_US
dc.description.sponsorshipDepartment of Computingen_US
dc.publisherIGI Globalen_US
dc.relation.ispartofInternational journal of software science and computational intelligenceen_US
dc.subject3D indoor localizationen_US
dc.subjectGoDec algorithmen_US
dc.subjectLow-rank and sparse matrix decompositionen_US
dc.subjectOcclusion removalen_US
dc.subjectSfM with RTen_US
dc.subjectStructure from motionen_US
dc.titleImproved SfM-based indoor localization with occlusion removalen_US
dc.typeJournal/Magazine Articleen_US
dc.description.oaNot applicableen_US
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