Dahlke, Dennis, Drakoulis, Petros, Fernández García, Anaida, Kaiser, Susanna, Karavarsamis, Sotiris, Mallis, Michail, Oliff, William, Sakellari, Georgia ORCID: 0000-0001-7238-8700 , Belmonte-Hernández, Alberto, Alvarez, Federico and Zarpalas, Dimitrios (2024) Seamless fusion: multi-modal localization for first responders in challenging environments. Sensors, 24 (9):2864. pp. 1-27. ISSN 1424-8220 (Online) (doi:https://doi.org/10.3390/s24092864)

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Abstract

In dynamic and unpredictable environments, the precise localization of first responders and rescuers is crucial for effective incident response. This paper introduces a novel approach leveraging three complementary localization modalities: visual-based, Galileo-based, and inertial-based. Each modality contributes uniquely to the final Fusion tool, facilitating seamless indoor and outdoor localization, offering a robust and accurate localization solution without reliance on pre-existing infrastructure, essential for maintaining responder safety and optimizing operational effectiveness. The visual-based localization method utilizes an RGB camera coupled with a modified implementation of the ORB-SLAM2 method, enabling operation with or without prior area scanning. The Galileo-based localization method employs a lightweight prototype equipped with a high-accuracy GNSS receiver board, tailored to meet the specific needs of first responders. The inertial-based localization method utilizes sensor fusion, primarily leveraging smartphone inertial measurement units, to predict and adjust first responders’ positions incrementally, compensating for the GPS signal attenuation indoors. A comprehensive validation test involving various environmental conditions was carried out to demonstrate the efficacy of the proposed fused localization tool. Our results show that our proposed solution always provides a location regardless of the conditions (indoors, outdoors, etc.), with an overall mean error of 1.73 m.

Item Type:	Article
Additional Information:	This article belongs to the Special Issue Multimodal Sensing Technologies for IoT and AI-Enabled Systems.
Uncontrolled Keywords:	multi-modal localization; self-localization; seamless fusion; sensor fusion; first responders; visual localization; Galileo satellite navigation; inertial navigation
Subjects:	Q Science > Q Science (General) Q Science > QA Mathematics > QA75 Electronic computers. Computer science T Technology > T Technology (General)
Faculty / School / Research Centre / Research Group:	Faculty of Engineering & Science Faculty of Engineering & Science > School of Computing & Mathematical Sciences (CMS)
Last Modified:	30 Apr 2024 13:37
URI:	http://gala.gre.ac.uk/id/eprint/46950

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