Image-tag-based indoor localization using end-to-end learning

Abstract

Image or feature matching-based indoor localization still faces many technical challenges. Image-tag-based schemes using pose estimation are accurate and robust, but they still cannot be deployed widely because their performance degrades significantly when the tag-camera distance is large, which requires densely distributed tags, and the designed system generally is specific to some special tags and lenses. Also, the lens distortion degrades the performance appreciably and is difficult to correct, especially for the wide-angle lenses. This article develops an image-tag-based indoor localization system using end-to-end learning to overcome these issues. It is a deep learning–based system that can learn the mapping from the original tag image to the final 2D location directly from training examples through self-learned features. It achieves consistent performance even when the tag-camera distance is large or when the image has a low resolution. The mapping learned by the deep learning model factors in all kinds of distortions without requiring any distortion estimation. The tag design is based on shape features to make it robust to lighting changes. The system can be easily adapted to new lenses/cameras and/or new tags. Thus, it facilitates easy and rapid deployment without requiring knowledge from domain experts. A drawback of the general deep learning model is its high computational requirements. We discuss practical solutions to enable real-time applications of the proposed scheme even when it is running on a mobile or embedded device. The performance of the proposed scheme is evaluated via a set of experiments in a real setting and has achieved less than 20 cm of positioning errors.