Using natural footstep-accurate traces for indoor positioning evaluation

Schwartz, Tim; Stahl, Christoph; Mahr, Angela; Müller, Christian; Saliba, Bahjat

doi:10.1109/ipin.2012.6418866

Cited by 3 publications

(1 citation statement)

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“…de la Osa et al [16] deals with the lack of a predominant solution for defining the ground truth when comparing indoor position estimates. Anagnostopoulos et al [17] defines a novel dynamic evaluation procedure with predefined geometrical paths (see [18]) to capture real-life usage of the scenarios, as well as other features such as ease of deployment and cost-efficiency. Even if discussion about evaluation strategies is open, the distance between the position 1 With DOI: 10.5281/zenodo.3741390 Fig.…”

Section: Positioning Error In Indoor Positioning Systemsmentioning

confidence: 99%

Beyond Euclidean Distance for Error Measurement in Pedestrian Indoor Location

Mendoza-Silva

Torres-Sospedra²,

Potortì

et al. 2021

IEEE Trans. Instrum. Meas.

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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. Multifloor and multi-building paths use information on vertical inbuilding 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 naturallooking paths at the expense of longer computation time and memory size. The 75 th percentile of the measured error differs among the methods from 2.2 m to 3.7 m across the evaluation sets.

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