Beyond Euclidean distance for error measurement in pedestrian indoor location

Indoor Positioning Systems 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 paper, 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 or Navigational Meshes for vector maps, and Fast Marching for raster maps. Multi-floor and multi-building paths use information on vertical in-building communication ways and outdoor paths. The proposed measurement procedures are applied to position estimates provided by the Indoor Positioning Systems 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 Visibility Graphs algorithm computes shortest distance paths; Navigational Meshes produces very similar paths with significantly shorter computation time; Fast Marching 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 m to 3.7 m across the evaluation sets.