Indoor localization using deep-learning and smartphone

Abstract EN

Robust and accurate indoor localization has been the goal of several research efforts over the past decade.

In the building where the GPS is not available, this project can be utilized.

Indoor localization based on image matching techniques related to deep learning was achieved in a hard environment.

So, if it wanted to raise the precision of indoor classification, the number of image dataset of the indoor environment should be as large as possible to satisfy and cover the underlying area.

In this work, a smartphone camera is used to build the image-based dataset of the investigated building.

In addition, captured images in real time are taken to be processed with the proposed model as a test set.

The proposed indoor localization includes two phases the first one is the offline learning phase and the second phase is the online processing phase.

In the offline learning phase, here we propose a convolutional neural network (CNN) model that sequences the features of image data from some classis's dataset composed with a smartphone camera.

In the online processing phase, an image is taken by the camera of a smartphone in real–time to be tested by the proposed model.

The obtained results of the prediction can appoint the expected indoor location.

The proposed system has been tested over various experiments and the obtained experimental results show that the accuracy of the prediction is almost 98.0%.