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

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.