Smartphone-based Indoor Localization for Blind Navigation across Building Complexes

Abstract

Continuous and accurate smartphone-based localization is a promising technology for supporting independent mobility of people with visual impairments. However, despite extensive research on indoor localization techniques, they are still not ready for deployment in large and complex environments, like shopping malls and hospitals, where navigation assistance is needed. To achieve accurate, continuous, and real-time localization with smartphones in such environments, we present a series of key techniques enhancing a probabilistic localization algorithm. The algorithm is designed for smartphones and employs inertial sensors on a mobile device and Received Signal Strength (RSS) from Bluetooth Low Energy (BLE) beacons. We evaluate the proposed system in a 21,000 m 2 shopping mall which includes three multi-story buildings and a large open underground passageway. Experiments in this space validate the effect of the proposed technologies to improve localization accuracy. Field experiments with visually impaired participants confirm the practical performance of the proposed system in realistic use cases.

Data Augmentation Schemes for Deep Learning in an Indoor Positioning Application

“…Therefore, to expand the size of the data set, data augmentation can be employed. The complex indoor environment and APs may cause problems [9,10] because of the limited coverage of Wi-Fi APs and faulty RSSI measurements. The purpose of data augmentation in this case is to detect and remove faulty measurement data or to remove invalid data, thus improving the accuracy and efficiency of the entire positioning system by creating a database representation that is more suitable for downstream deep learning classifiers.…”

Airport Accessibility and Navigation Assistance for People with Visual Impairments

“…We then conducted a site survey to collect fngerprints of iBeacons' radio wave signals for about 17.5 hours to be used as training data of the localization model; and an additional 2.1 hours to evaluate the accuracy of the localization model. Average localization error is 2.2 meters, and 4.5 meters at the 95 percentile using the method developed by Murata et al [45].…”

“…Large open spaces negatively impact the localization model, since longer distances between beacons result in a lower localization accuracy [45]. In addition, changes in the environment may require additional eforts to install the system.…”

A Joint TDOA-PDOA Localization Approach Using Particle Swarm Optimization

“…Existing and future wireless communication technologies require accurate real-time localization and tracking methods. Location information is important for numerous applications such as target monitoring [1], wireless sensor network [2], navigation [3], drones localization [4] and connected vehicles [5]. A survey of positioning techniques and systems can be found in [6], [7].…”