An Indoor Localization Method of Image Matching Based on Deep Learning
Guihua Yang, Yu Liang
Available Online March 2018.
- https://doi.org/10.2991/mecae-18.2018.21How to use a DOI?
- indoor localization, deep learning, location algorithm, image matching
- 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.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
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 - https://doi.org/10.2991/mecae-18.2018.21 ID - Yang2018/03 ER -