An Indoor Localization Method of Image Matching Based on Deep Learning

DOI

10.2991/mecae-18.2018.21How to use a DOI?

Keywords

indoor localization, deep learning, location algorithm, image matching

Abstract

To overcome the problems of low accuracy and poor stability brought by the complexity of scenarios, an indoor localization method of image matching based on Deep Learning is proposed. The method includes taking images of indoor surroundings with cameras of mobile devices, setting up a dataset of images containing information on position and direction, and training a Convolutional Neural Network (CNN) with the image data. Then use the trained CNN to match the current images taken by the cameras of mobile devices to estimate precise location. The results of experiments show that the accuracy rate of CNN can reach up to 99.2%, positioning accuracy rate is up to 90%, and positioning precision is within 2 metres of diameter. This algorithm can achieve sound robustness, and fairly excellent generalization capabilities.

Copyright

© 2018, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Guihua Yang
AU  - Yu Liang
PY  - 2018/03
DA  - 2018/03
TI  - An Indoor Localization Method of Image Matching Based on Deep Learning
BT  - Proceedings of the 2018 International Conference on Mechanical, Electronic, Control and Automation Engineering (MECAE 2018)
PB  - Atlantis Press
SP  - 116
EP  - 121
SN  - 2352-5401
UR  - https://doi.org/10.2991/mecae-18.2018.21
DO  - 10.2991/mecae-18.2018.21
ID  - Yang2018/03
ER  -