Beyond Euclidean Distance for Error Measurement in Pedestrian Indoor Location
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Título
Beyond Euclidean Distance for Error Measurement in Pedestrian Indoor LocationAutoría
Fecha de publicación
2020-09-04Editor
Institute of Electrical and Electronics EngineersISSN
0018-9456Cita bibliográfica
MENDOZA-SILVA, Germán Martín, et al. Beyond Euclidean Distance for Error Measurement in Pedestrian Indoor Location. IEEE Transactions on Instrumentation and Measurement, 2020, vol. 70, p. 1-11.Tipo de documento
info:eu-repo/semantics/articleVersión de la editorial
https://ieeexplore.ieee.org/document/9186638Versión
info:eu-repo/semantics/publishedVersionPalabras clave / Materias
Resumen
Indoor positioning systems (IPSs) suffer from a
lack of standard evaluation procedures enabling credible comparisons: this is one of the main challenges hindering their
widespread market adoption. Traditionally, ... [+]
Indoor positioning systems (IPSs) suffer from a
lack of standard evaluation procedures enabling credible comparisons: this is one of the main challenges hindering their
widespread market adoption. Traditionally, accuracy evaluation
is based on positioning errors defined as the Euclidean distance
between the true positions and the estimated positions. While
Euclidean is simple, it ignores obstacles and floor transitions.
In this article, we describe procedures that measure a positioning error defined as the length of the pedestrian path
that connects the estimated position to the true position. The
procedures apply pathfinding on floor maps using visibility
graphs (VGs) or navigational meshes (NMs) for vector maps and
fast marching (FM) for raster maps. Multifloor and multibuilding
paths use the information on vertical in-building communication
ways and outdoor paths. The proposed measurement procedures
are applied to position estimates provided by the IPSs that
participated in the EvAAL-ETRI 2015 competition. Procedures
are compared in terms of pedestrian path realism, indoor model
complexity, path computation time, and error magnitudes. The
VGs algorithm computes shortest distance paths; NMs produce
very similar paths with significantly shorter computation time;
and FM computes longer, more natural-looking paths at the
expense of longer computation time and memory size. The 75th
percentile of the measured error differs among the methods from
2.2 to 3.7 m across the evaluation sets. [-]
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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, VOL. 70, 2021Derechos de acceso
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