An Indoor Localization Method of Image Matching Based on Deep Learning
Guihua Yang, Yu Liang · Proceedings of the 2018 International Conference on Mechanical, Electronic, Control and Automation Engineering (MECAE 2018) · 2018
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.