Fusion of Radio and Camera Sensor Data for Accurate Indoor Positioning
Savvas Papaioannou, Hongkai Wen, Andrew Markham, Niki Trigoni

TL;DR
This paper introduces RAVEL, a novel indoor positioning system that fuses camera visual detections with WiFi radio data to achieve sub-meter accuracy in complex environments, overcoming occlusion and ambiguity issues.
Contribution
The paper presents a new sensor fusion approach combining visual and radio data for indoor positioning, enhancing accuracy and robustness over existing lightweight solutions.
Findings
Achieved sub-meter positioning accuracy in a museum environment.
Effectively resolved visual ambiguities using radio measurements.
Demonstrated robustness in cluttered, dimly lit indoor spaces.
Abstract
Indoor positioning systems have received a lot of attention recently due to their importance for many location-based services, e.g. indoor navigation and smart buildings. Lightweight solutions based on WiFi and inertial sensing have gained popularity, but are not fit for demanding applications, such as expert museum guides and industrial settings, which typically require sub-meter location information. In this paper, we propose a novel positioning system, RAVEL (Radio And Vision Enhanced Localization), which fuses anonymous visual detections captured by widely available camera infrastructure, with radio readings (e.g. WiFi radio data). Although visual trackers can provide excellent positioning accuracy, they are plagued by issues such as occlusions and people entering/exiting the scene, preventing their use as a robust tracking solution. By incorporating radio measurements, visually…
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