Empirical Study of a Room-Level Localization System Based on Bluetooth Low Energy Beacons.

The ability to locate an object or a person at room-level inside a building or a house could have multiple applications. In this study, we adapt the fingerprint technique using Bluetooth Low Energy to locate the exact room of a person, seeking a simple and low-cost solution. The system is based on BLE beacons deployed at fixed positions and a person carrying a BLE scanner that generates fingerprints from the BLE beacons in coverage. We formulate it as a classification problem where each room is a class; the objective is to estimate the exact room, trying to maximize the area and number of rooms, but also trying to minimize the number of BLE beacons. The room estimation engine is based on a kNN (k-nearest neighbors) classifier. We evaluate the accuracy in two real scenarios and empirically measure the room estimation success related to the number of BLE beacons. As a proof-of-concept, a laptop and a Raspberry Pi are used as BLE scanners to test different hardware. We follow a measurement campaign for several days at different times to evaluate the stability and repeatability of the system. With just a few beacons an accuracy between 70 and 90% is achieved for house and university scenarios.

Design, Implementation and Evaluation of an Indoor Navigation System for Visually Impaired People.

Indoor navigation is a challenging task for visually impaired people. Although there are guidance systems available for such purposes, they have some drawbacks that hamper their direct application in real-life situations. These systems are either too complex, inaccurate, or require very special conditions (i.e., rare in everyday life) to operate. In this regard, Ultra-Wideband (UWB) technology has been shown to be effective for indoor positioning, providing a high level of accuracy and low installation complexity. This paper presents SUGAR, an indoor navigation system for visually impaired people which uses UWB for positioning, a spatial database of the environment for pathfinding through the application of the A* algorithm, and a guidance module. The interaction with the user takes place using acoustic signals and voice commands played through headphones. The suitability of the system for indoor navigation has been verified by means of a functional and usable prototype through a field test with a blind person. In addition, other tests have been conducted in order to show the accuracy of different relevant parts of the system.

Integration of Directional Antennas in an RSS Fingerprinting-Based Indoor Localization System.

In this paper, the integration of directional antennas in a room-level received signal strength (RSS) fingerprinting-based indoor localization system (ILS) is studied. The sensor reader (SR), which is in charge of capturing the RSS to infer the tag position, can be attached to an omnidirectional or directional antenna. Unlike commonly-employed omnidirectional antennas, directional antennas can receive a stronger signal from the direction in which they are pointed, resulting in a different RSS distributions in space and, hence, more distinguishable fingerprints. A simulation tool and a system management software have been also developed to control the system and assist the initial antenna deployment, reducing time-consuming costs. A prototype was mounted in a real scenario, with a number of SRs with omnidirectional and directional antennas properly positioned. Different antenna configurations have been studied, evidencing a promising capability of directional antennas to enhance the performance of RSS fingerprinting-based ILS, reducing the number of required SRs and also increasing the localization success.

Garment counting in a textile warehouse by means of a laser imaging system.

Textile logistic warehouses are highly automated mechanized places where control points are needed to count and validate the number of garments in each batch. This paper proposes and describes a low cost and small size automated system designed to count the number of garments by processing an image of the corresponding hanger hooks generated using an array of phototransistors sensors and a linear laser beam. The generated image is processed using computer vision techniques to infer the number of garment units. The system has been tested on two logistic warehouses with a mean error in the estimated number of hangers of 0.13%.