Quantifying relationship between relative position error of localization algorithms and object identification

Positioning of things, devices and people is the fundamental technology in ubiquitous computing. However, few literature has discussed the impact of positioning errors due to localization algorithm properties such as ranging noise and deployment of anchors on people’s identification of objects. Since several factors such as relative distance, relative angles and grouping of objects are intricately related with each other in such identification, it is not an easy task to investigate its characteristics. In this paper, we propose criteria to assess the “accuracy” of the estimated positions in identifying the objects. The criteria are helpful to design, develop and evaluate localization algorithms that are used to tell people the location of objects. Augmented reality is a typical example that needs such localization algorithms. To model the criteria without ambiguity, we prove that the Delaunay triangulation well-captures natural human behavior of finding similarity between estimated and true positions. We have examined different localization algorithms to observe how the proposed model quantifies the properties of those algorithms. Subjective testing has also been conducted using questionnaires to justify our quantification sufficiently renders human intuition.