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Visual landmark sequence-based indoor localization

Li, Qing; Zhu, Jiasong; Liu, Tao; Garibaldi, Jon; Li, Qingquan; Qiu, Guoping

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Authors

Qing Li

Jiasong Zhu

Tao Liu

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

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