Visual landmark sequence-based indoor localization
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 employ...
| Main Authors: | , , , , , |
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| Format: | Conference or Workshop Item |
| Published: |
2017
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| Online Access: | https://eprints.nottingham.ac.uk/50171/ |
| _version_ | 1848798174617010176 |
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| author | Li, Qing Zhu, Jiasong Liu, Tao Garibaldi, Jon Li, Qingquan Qiu, Guoping |
| author_facet | Li, Qing Zhu, Jiasong Liu, Tao Garibaldi, Jon Li, Qingquan Qiu, Guoping |
| author_sort | Li, Qing |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T20:15:35Z |
| format | Conference or Workshop Item |
| id | nottingham-50171 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T20:15:35Z |
| publishDate | 2017 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-501712020-05-04T19:16:39Z https://eprints.nottingham.ac.uk/50171/ Visual landmark sequence-based indoor localization Li, Qing Zhu, Jiasong Liu, Tao Garibaldi, Jon Li, Qingquan Qiu, Guoping 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. 2017-11-07 Conference or Workshop Item PeerReviewed Li, Qing, Zhu, Jiasong, Liu, Tao, Garibaldi, Jon, Li, Qingquan and Qiu, Guoping (2017) Visual landmark sequence-based indoor localization. In: 1st Workshop on Artificial Intelligence and Deep Learning for Geographic Knowledge Discovery, 7-10 November, 2017, Los Angeles, California, USA. https://dl.acm.org/citation.cfm?id=3149808.3149812 10.1145/3149808.3149812 10.1145/3149808.3149812 10.1145/3149808.3149812 |
| spellingShingle | Li, Qing Zhu, Jiasong Liu, Tao Garibaldi, Jon Li, Qingquan Qiu, Guoping Visual landmark sequence-based indoor localization |
| title | Visual landmark sequence-based indoor localization |
| title_full | Visual landmark sequence-based indoor localization |
| title_fullStr | Visual landmark sequence-based indoor localization |
| title_full_unstemmed | Visual landmark sequence-based indoor localization |
| title_short | Visual landmark sequence-based indoor localization |
| title_sort | visual landmark sequence-based indoor localization |
| url | https://eprints.nottingham.ac.uk/50171/ https://eprints.nottingham.ac.uk/50171/ https://eprints.nottingham.ac.uk/50171/ |