Seamless Fusion: Multi-Modal Localization for First Responders in Challenging Environments

Author:

Dahlke Dennis1,Drakoulis Petros2ORCID,Fernández García Anaida3ORCID,Kaiser Susanna4ORCID,Karavarsamis Sotiris2,Mallis Michail2ORCID,Oliff William5,Sakellari Georgia5,Belmonte-Hernández Alberto3ORCID,Alvarez Federico3ORCID,Zarpalas Dimitrios2ORCID

Affiliation:

1. German Aerospace Center (DLR), 12489 Berlin, Germany

2. Visual Computing Lab, Information Technologies Institute, Centre for Research and Technology Hellas (CERTH), 57001 Thermi, Greece

3. Señales, Sistemas y Radiocomunicaciones, ETSI Telecomunicación, Universidad Politécnica de Madrid (UPM), 28040 Madrid, Spain

4. German Aerospace Center (DLR), 82234 Wessling, Germany

5. CS2 Research Centre, School of Computing and Mathematical Sciences, University of Greenwich, London SE10 9LS, UK

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.

Funder

European Union’s Horizon 2020 Research and Innovation Programme

Publisher

MDPI AG

Reference98 articles.

1. (2024, February 22). U.S. Coast Guard Search and Rescue Statistics, Available online: https://www.bts.gov/content/us-coast-guard-search-and-rescue-statistics-fiscal-year.

2. (2024, April 21). IFARI Capability Gaps International Forum to Advance First Responder Innovation. Available online: https://www.internationalresponderforum.org/services/capability-gaps.

3. Occupancy prediction using deep learning approaches across multiple space types: A minimum sensing strategy;Tekler;Build. Environ.,2022

4. Occupancy-based HVAC control systems in buildings: A state-of-the-art review;Haghighat;Build. Environ.,2021

5. A Survey of Indoor Localization Systems and Technologies;Zafari;IEEE Commun. Surv. Tutor.,2017

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