Visual Localizer: Outdoor Localization Based on ConvNet Descriptor and Global Optimization for Visually Impaired Pedestrians
State Key Laboratory of Modern Optical Instrumentation, Zhejiang University, Hangzhou 310027, China
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These authors contributed equally to this work.
Sensors 2018, 18(8), 2476; https://doi.org/10.3390/s18082476
Received: 13 June 2018
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Revised: 25 July 2018
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Accepted: 26 July 2018
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Published: 31 July 2018
(This article belongs to the Special Issue Sensor Technologies for Caring People with Disabilities)
Localization systems play an important role in assisted navigation. Precise localization renders visually impaired people aware of ambient environments and prevents them from coming across potential hazards. The majority of visual localization algorithms, which are applied to autonomous vehicles, are not adaptable completely to the scenarios of assisted navigation. Those vehicle-based approaches are vulnerable to viewpoint, appearance and route changes (between database and query images) caused by wearable cameras of assistive devices. Facing these practical challenges, we propose Visual Localizer, which is composed of ConvNet descriptor and global optimization, to achieve robust visual localization for assisted navigation. The performance of five prevailing ConvNets are comprehensively compared, and GoogLeNet is found to feature the best performance on environmental invariance. By concatenating two compressed convolutional layers of GoogLeNet, we use only thousands of bytes to represent image efficiently. To further improve the robustness of image matching, we utilize the network flow model as a global optimization of image matching. The extensive experiments using images captured by visually impaired volunteers illustrate that the system performs well in the context of assisted navigation.
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Keywords:
assisted navigation; place recognition; topological localization; impaired vision; convolutional neural networks; deep feature; network flow; data association graph
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
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MDPI and ACS Style
Lin, S.; Cheng, R.; Wang, K.; Yang, K. Visual Localizer: Outdoor Localization Based on ConvNet Descriptor and Global Optimization for Visually Impaired Pedestrians. Sensors 2018, 18, 2476. https://doi.org/10.3390/s18082476
AMA Style
Lin S, Cheng R, Wang K, Yang K. Visual Localizer: Outdoor Localization Based on ConvNet Descriptor and Global Optimization for Visually Impaired Pedestrians. Sensors. 2018; 18(8):2476. https://doi.org/10.3390/s18082476
Chicago/Turabian StyleLin, Shufei, Ruiqi Cheng, Kaiwei Wang, and Kailun Yang. 2018. "Visual Localizer: Outdoor Localization Based on ConvNet Descriptor and Global Optimization for Visually Impaired Pedestrians" Sensors 18, no. 8: 2476. https://doi.org/10.3390/s18082476
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