Beyond Euclidean Distance for Error Measurement in Pedestrian Indoor Location

Abstract

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.