Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/81178
Title: Improved SfM-based indoor localization with occlusion removal
Authors: Li, YS 
Baciu, G 
Han, Y 
Li, CH 
Keywords: 3D indoor localization
GoDec algorithm
Low-rank and sparse matrix decomposition
Occlusion removal
SfM with RT
Structure from motion
Issue Date: 2018
Publisher: IGI Global
Source: International journal of software science and computational intelligence, 2018, v. 10, no. 3, p. 24-40 How to cite?
Journal: International journal of software science and computational intelligence 
Abstract: This 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.
URI: http://hdl.handle.net/10397/81178
ISSN: 1942-9045
DOI: 10.4018/IJSSCI.2018070102
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