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Efficient indoor positioning with visual experiences via lifelong learning
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Wen, Hongkai, Clark, Ronald, Wang, Sen, Lu, Xiaoxuan, Du, Bowen, Hu, Wen and Trigoni, Niki (2019) Efficient indoor positioning with visual experiences via lifelong learning. IEEE Transactions on Mobile Computing, 18 (4). pp. 814-829. doi:10.1109/TMC.2018.2852645 ISSN 1536-1233.
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WRAP-efficient-indoor-positioning-visual-experiences-lifelong-learning-Wen-2018.pdf - Accepted Version - Requires a PDF viewer. Download (4Mb) | Preview |
Official URL: https://doi.org/10.1109/TMC.2018.2852645
Abstract
Positioning with visual sensors in indoor environments has many advantages: it doesn't require infrastructure or accurate maps, and is more robust and accurate than other modalities such as WiFi. However, one of the biggest hurdles that prevents its practical application on mobile devices is the time-consuming visual processing pipeline. To overcome this problem, this paper proposes a novel lifelong learning approach to enable efficient and real-time visual positioning. We explore the fact that when following a previous visual experience for multiple times, one could gradually discover clues on how to traverse it with much less effort, e.g. which parts of the scene are more informative, and what kind of visual elements we should expect. Such second-order information is recorded as parameters, which provide key insights of the context and empower our system to dynamically optimise itself to stay localised with minimum cost. We implement the proposed approach on an array of mobile and wearable devices, and evaluate its performance in two indoor settings. Experimental results show our approach can reduce the visual processing time up to two orders of magnitude, while achieving sub-metre positioning accuracy.
| Item Type: | Journal Article | ||||||||
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| Subjects: | Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software | ||||||||
| Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science | ||||||||
| Library of Congress Subject Headings (LCSH): | Indoor positioning systems (Wireless localization), Wearable technology, Human-computer interaction, Location-based services, Computer vision | ||||||||
| Journal or Publication Title: | IEEE Transactions on Mobile Computing | ||||||||
| Publisher: | IEEE | ||||||||
| ISSN: | 1536-1233 | ||||||||
| Official Date: | 1 April 2019 | ||||||||
| Dates: |
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| Volume: | 18 | ||||||||
| Number: | 4 | ||||||||
| Page Range: | pp. 814-829 | ||||||||
| DOI: | 10.1109/TMC.2018.2852645 | ||||||||
| Status: | Peer Reviewed | ||||||||
| Publication Status: | Published | ||||||||
| Date of first compliant deposit: | 7 August 2018 | ||||||||
| Date of first compliant Open Access: | 7 August 2018 | ||||||||
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