/Deep Learning and Geometry-Based Image Localization Enhanced by Bluetooth Signals

Deep Learning and Geometry-Based Image Localization Enhanced by Bluetooth Signals

Tatsuya Ishihara, Kris M. Kitani, Chieko Asakawa and Michitaka Hirose
Journal Article, Journal of Information Processing, Vol. 26, pp. 707 - 717, October, 2018

Copyright notice: This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author’s copyright. These works may not be reposted without the explicit permission of the copyright holder.

Abstract

For many automated navigation applications, the underlying localization algorithm must be able to continuously produce results that are both accurate and stable. To date, various types of localization approaches including GPS, Wi-Fi, Bluetooth and cameras have been studied extensively. Image-based localization approaches have been developed by using commodity devices, such as smartphones, and these have been shown to produce accurate localization systems. However, image-based localization approaches do not work well in environments that lack visual features. Therefore, we propose a novel approach that combines the use of radio-wave information with computer vision-based localization. In particular, we assume that Bluetooth low energy (BLE) devices are already installed in the environment. We integrate radio-wave information with two types of well-known image-based localization approaches: a Structure from Motion (SfM) based approach and a deep convolutional neural network (CNN) based approach. Our experimental results show that both image-based localization approaches can be more accurate when combined with radio-wave signals. The results also show that the localization accuracy of the proposed deep CNN approach is comparable to that of SfM and significantly more robust than it. In addition, the proposed deep CNN approach was found to be robust to BLE device failures.

BibTeX Reference
@article{Ishihara-2018-109766,
author = {Tatsuya Ishihara and Kris M. Kitani and Chieko Asakawa and Michitaka Hirose},
title = {Deep Learning and Geometry-Based Image Localization Enhanced by Bluetooth Signals},
journal = {Journal of Information Processing},
year = {2018},
month = {October},
volume = {26},
pages = {707 - 717},
}
2018-11-06T14:58:37+00:00