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Indoor Topological Localization Based on a Novel Deep Learning Technique

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Abstract

Millions of people in the world suffer from vision impairment or vision loss. Traditionally, they rely on guide sticks or dogs to move around and avoid potential obstacles. However, both guide sticks and dogs are passive. They are unable to provide conceptual knowledge or semantic contents of an environment. To address this issue, this paper presents a vision-based cognitive system to support the independence of visually impaired people. More specifically, a 3D indoor semantic map is firstly constructed with a hand-held RGB-D sensor. The constructed map is then deployed for indoor topological localization. Convolutional neural networks are used for both semantic information extraction and location inference. Semantic information is used to further verify localization results and eliminate errors. The topological localization performance can be effectively improved despite significant appearance changes within an environment. Experiments have been conducted to demonstrate that the proposed method can increase both localization accuracy and recall rates. The proposed system can be potentially deployed by visually impaired people to move around safely and have independent life.

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Acknowledgments

We thank Robin Dowling and Ian Dukes from the University of Essex for their technical support. Our thanks also go to Poppy Rees-Smith from Oxford Brookes University for her valuable comments.

Funding

The first two authors have been financially supported by the China Scholarship Council and University of Essex joint scholarship.

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Authors and Affiliations

  1. School of Computer Science and Electric Engineering, University of Essex, Colchester, CO4 3SQ, UK

    Qiang Liu, Ruihao Li, Huosheng Hu & Dongbing Gu

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  1. Qiang Liu
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  2. Ruihao Li
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  3. Huosheng Hu
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  4. Dongbing Gu
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Corresponding author

Correspondence to Qiang Liu.

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Cite this article

Liu, Q., Li, R., Hu, H. et al. Indoor Topological Localization Based on a Novel Deep Learning Technique. Cogn Comput 12, 528–541 (2020). https://doi.org/10.1007/s12559-019-09693-5

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