RESCUE-AR: Responsive Sensor-Fusion for Civil-Protection and Disaster Response Using Mobile Augmented Reality

Abstract

Rapid situational awareness is a cornerstone of effective civil protection and disaster response, demanding highly resilient localization and decision-support capabilities under challenging environmental conditions. This paper presents RESCUE-AR (Responsive Sensor-Fusion for Civil-Protection and Disaster Response Using Mobile Augmented Reality), a conceptual and methodological framework for mobile Augmented Reality (AR) systems designed to support emergency operations through heterogeneous and adaptive sensor fusion. This work formalizes the mathematical and algorithmic architecture of the RESCUE-AR framework, establishing an adaptive multi-sensor measurement fusion strategy, integrating Visual SLAM, IMU, depth sensing, and GNSS data to enhance robustness in environments affected by occlusion, signal degradation, and dynamic change. The framework is grounded in a systematic literature review that identifies the limitations of single-sensor and fixed-weight fusion approaches in disaster scenarios and motivates the adoption of adaptive fusion strategies. An initial user-centered evaluation is conducted through a structured approach, employing standardized questionnaires to assess usability, cognitive workload, anticipated operational trust, and framework suitability for first responders. The results indicate strong perceived utility, reduced cognitive load, and high confidence in AR-assisted decision-making, supporting the potential of adaptive sensor-fusion-based mobile AR as a viable and scalable decision-support tool for civil protection and disaster response.