Qing Li
Visual landmark sequence-based indoor localization
Li, Qing; Zhu, Jiasong; Liu, Tao; Garibaldi, Jon; Li, Qingquan; Qiu, Guoping
Authors
Jiasong Zhu
Tao Liu
Prof. JONATHAN GARIBALDI JON.GARIBALDI@NOTTINGHAM.AC.UK
Provost and Pvc Unnc
Qingquan Li
GUOPING QIU GUOPING.QIU@NOTTINGHAM.AC.UK
Vice Provost For Education and Studentexperience
Abstract
This paper presents a method that uses common objects as landmarks for smartphone-based indoor localization and navigation. First, a topological map marking relative positions of common objects such as doors, stairs and toilets is generated from floor plan. Second, a computer vision technique employing the latest deep learning technology has been developed for detecting common indoor objects from videos captured by smartphone. Third, second order Hidden Markov model is applied to match detected indoor landmark sequence to topological map. We use videos captured by users holding smartphones and walking through corridors of an office building to evaluate our method. The experiment shows that computer vision technique is able to accurately and reliably detect 10 classes of common indoor objects and that second order hidden Markov model can reliably match the detected landmark sequence with the topological map. This work demonstrates that computer vision and machine learning techniques can play a very useful role in developing smartphone-based indoor positioning applications.
Citation
Li, Q., Zhu, J., Liu, T., Garibaldi, J., Li, Q., & Qiu, G. (2017). Visual landmark sequence-based indoor localization. In Proceedings of the 1st Workshop on Artificial Intelligence and Deep Learning for Geographic Knowledge Discovery - GeoAI '17 (14-23). https://doi.org/10.1145/3149808.3149812
Presentation Conference Type | Edited Proceedings |
---|---|
Conference Name | 1st Workshop on Artificial Intelligence and Deep Learning for Geographic Knowledge Discovery |
Start Date | Nov 10, 2017 |
End Date | Nov 10, 2017 |
Acceptance Date | Nov 7, 2017 |
Publication Date | Nov 7, 2017 |
Deposit Date | Mar 5, 2018 |
Publicly Available Date | Mar 5, 2018 |
Peer Reviewed | Peer Reviewed |
Pages | 14-23 |
Book Title | Proceedings of the 1st Workshop on Artificial Intelligence and Deep Learning for Geographic Knowledge Discovery - GeoAI '17 |
ISBN | 9781450354981 |
DOI | https://doi.org/10.1145/3149808.3149812 |
Public URL | https://nottingham-repository.worktribe.com/output/893406 |
Publisher URL | https://dl.acm.org/citation.cfm?id=3149808.3149812 |
Additional Information | Published in: Proceedings of the 1st Workshop on Artificial Intelligence and Deep Learning for Geographic Knowledge Discovery, Los Angeles, California, November 7- 10, 2017 New York : ACM, ©2017.ISBN: 978-1-4503-5498-1 doi:10.1145/3149808.3149812 |
Contract Date | Mar 5, 2018 |
Files
GeoAI2017_paper_14(2).pdf
(9.5 Mb)
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