Name: Smartphone-Based 3d Indoor Localization and Navigation (Human Data Understanding - Sensors, Models, Knowledge) (en Inglés)
SKU: 62068401
Price: 149.41 USD
Availability: InStock
Author: Frank Ebner
ISBN: 9783832552329

portada Smartphone-Based 3d Indoor Localization and Navigation (Human Data Understanding - Sensors, Models, Knowledge) (en Inglés)

Smartphone-Based 3d Indoor Localization and Navigation (Human Data Understanding - Sensors, Models, Knowledge) (en Inglés)

Formato

Libro Físico

Editorial

Logos Verlag Berlin

Autor

Frank Ebner

Idioma

Inglés

N° páginas

351

Encuadernación

Tapa Blanda

ISBN13

9783832552329

Smartphone-Based 3d Indoor Localization and Navigation (Human Data Understanding - Sensors, Models, Knowledge) (en Inglés)

Frank Ebner (Autor) · Logos Verlag Berlin · Tapa Blanda

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Reseña del libro "Smartphone-Based 3d Indoor Localization and Navigation (Human Data Understanding - Sensors, Models, Knowledge) (en Inglés)"

During the last century, navigation systems have become ubiquitous and guide drivers, cyclists, and pedestrians towards their desired destinations. While operating worldwide, they rely on line-of-sight conditions towards satellites and are thus limited to outdoor areas. However, finding a gate within an airport, a ward within a hospital, or a university's auditorium also represent navigation problems. To provide navigation within such indoor environments, new approaches are required. This thesis examines pedestrian 3D indoor localization and navigation using commodity smartphones: A desirable target platform, always at hand and equipped with a multitude of sensors. The IMU (accelerometer, gyroscope, magnetometer) and barometer allow for pedestrian dead reckoning, that is, estimating relative location changes. Absolute whereabouts can be determined via Wi-Fi, an infrastructure present within most public buildings, or by using Bluetooth Low Energy Beacons as inexpensive supplement. The building's 3D floorplan not only enables navigation, but also increases accuracy by preventing impossible movements, and serves as a visual reference for the pedestrian. All aforementioned information is fused by recursive density estimation based on a particle filter. The conducted experiments cover both, theoretical backgrounds and real-world use-cases. All discussed approaches utilize the infrastructure found within most public buildings, are easy to set up, and maintain. Overall, this thesis results in an indoor localization and navigation system that can be easily deployed, without requiring any special hardware components.