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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.
摘要：
在动态和不可预测的环境中，急救人员和救援人员的精确定位对于有效的事件响应至关重要。本文介绍了一种利用三种互补定位模式的新方法：基于视觉的定位，基于伽利略，基于惯性。每种模态对最终的融合工具都有独特的贡献，促进室内和室外无缝定位，提供强大而准确的本地化解决方案，而不依赖现有的基础架构，对于维护响应者安全和优化运营有效性至关重要。基于视觉的定位方法利用与ORB-SLAM2方法的修改实现耦合的RGB相机，使操作有或没有事先区域扫描。基于伽利略的定位方法采用配备高精度GNSS接收器板的轻型原型，为满足急救人员的具体需求而量身定制。基于惯性的定位方法利用传感器融合，主要利用智能手机惯性测量单元，以增量方式预测和调整第一响应者的位置，补偿室内GPS信号衰减。进行了涉及各种环境条件的全面验证测试，以证明所提出的融合定位工具的有效性。我们的结果表明，我们提出的解决方案总是提供一个位置，无论条件如何(室内，户外,等。),总体平均误差为1.73m。
