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DeepNav: A scalable and plug-and-play indoor navigation system based on visual CNN

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Abstract

With the proliferation of smartphones, recent years have witnessed the rapid development of smartphone-based indoor navigation systems. However, existing solutions either bear high deployment cost or cannot support large-scale navigation. A scalable and plug-and-play indoor navigation system is still highly desirable. In this paper, we propose DeepNav, a new indoor navigation system that fully uses visual CNN to realize large-scale navigation. DeepNav adopts a single-pilot deployment scheme to realize fast deployment. It divides the indoor area into dense sub-areas to simplify image-based location matching while ensuring reasonable resolution. Practical realization of DeepNav entails a set of key challenges, e.g., invalid image recognition, classification of thousands of labels and under-fitting. In order to solve these challenges, we propose invalid image filter, subgroup sigmoid layer and movable object filter, respectively, for DeepNav. Finally, we implement a prototype of DeepNav on commercial smartphones. Experimental results demonstrate that DeepNav can be quickly deployed (e.g., within an hour in a 4-storey building) with an average localization error of 2.3 meters.

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Authors and Affiliations

  1. School of Computer Science and Engineering, Central South University, Changsha, China

    Jian Gong & Ju Ren

  2. Department of Computer Science and Technology, Tsinghua University, Beijing, China

    Yaoxue Zhang

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  1. Jian Gong
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  2. Ju Ren
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  3. Yaoxue Zhang
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Correspondence to Jian Gong.

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Gong, J., Ren, J. & Zhang, Y. DeepNav: A scalable and plug-and-play indoor navigation system based on visual CNN. Peer-to-Peer Netw. Appl. 14, 3718–3736 (2021). https://doi.org/10.1007/s12083-021-01216-0

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