Evaluation of An Indoor Localization Engine

TL;DR

This paper presents a comprehensive fusion engine for indoor localization using multiple smartphone sensors, evaluated across diverse scenarios to demonstrate robustness and feasibility of lightweight implementation.

Contribution

It introduces a novel sensor fusion approach that integrates various data sources for robust indoor localization, tested extensively in real-world conditions.

Findings

Effective localization under poor RSS coverage

Robust performance despite device position changes

Feasible lightweight implementation demonstrated

Abstract

Pedestrian Indoor localization based on modalities available in modern smartphones have been widely studied in literature and many of the specific challenges have been addressed. However, very few approaches consider the whole problem and proposed solutions are very often evaluated under very limited scenarios. We propose a fusion engine for localization that makes use of various data provided by a smartphone (Inertial sensors, pressure sensors, Wi-Fi, BLE, GNSS, map etc.) to provide a fused localization that is robust under harsh conditions (poor RSS coverage, device position change etc.). Moreover, our solution has been evaluated for hardware integration and tested over a large database including more than 250 experiments representing different scenarios, showing feasibility of lightweight implementation and good results over various conditions.