MagLand: Magnetic Landmarks for Road Vehicle Localization

• Tipo: Artigo em Revista Científica Internacional
• Data: 2020

Autores

Susana B. Cruz (Autor) Outra; Ana Aguiar (Autor) FEUP

Revista

Título: IEEE Transactions on Vehicular Technology

Vol. 69

Páginas: 3654-3667

ISSN: 0018-9545

Editora: IEEE

Indexação

ISI Web of Knowledge - 17 Citações

Scopus - 17 Citações

Outras Informações

ID Authenticus: P-00S-1SM

DOI: 10.1109/tvt.2020.2977599

Abstract (EN): Satellite-based systems are the most widespread solution for outdoor localization. However, they present well-known limitations in multipath environments and non-line-of-sight satellite conditions, e.g. tunnels, underground, urban canyons, and multilevel roads, being frequently combined with dead reckoning techniques. Inertial sensors present cumulative errors, and geomagnetic-field information is often distorted by strong local magnetic fields caused by road infrastructure. We turn this magnetic weakness into strength by proposing MagLand, an approach with detection and matching steps to leverage these anomalies as signatures for localization purposes. For anomaly detection, we adopt a window-based technique and apply supervised binary classification, choosing a random forest. We select one nearest centroid algorithm with dynamic time warping to match input data streams to reference signatures, providing guidelines to define and collect them. Real data experiments with off-the-shelf devices in challenging road scenarios show MagLand's feasibility with anomaly detection accuracy of 91% and matching with lane distinction of up to 93%. Magnetic landmarks can be extremely useful to address limitations of current localization systems and improve their performance, e.g. by providing an alternative in GPS limited areas, anchors for integrity monitoring, or resetting dead reckoning cumulative errors.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 14