Despite significant advancements in indoor navigation technology over recent decades, it still faces challenges due to excessive dependency on external infrastructure and unreliable positioning in complex environments. This paper proposes an autonomous localization system that integrates advanced adaptive pedestrian dead reckoning (APDR) and binocular vision, designed to provide a low-cost, high-reliability, and high-precision solution for rescuers. By analyzing the characteristics of measurement data from various body parts, the chest is identified as the optimal placement for sensors. A chest-mounted advanced APDR method based on dynamic step segmentation detection and adaptive step length estimation has been developed. Furthermore, step length features are innovatively integrated into the visual tracking algorithm to constrain errors. Visual data is fused with dead reckoning data through an extended Kalman filter (EKF), which notably enhances the reliability and accuracy of the positioning system. A wearable autonomous localization vest system was designed and tested in indoor corridors, underground parking lots, and tunnel environments. Results show that the system decreases the average positioning error by 45.14% and endpoint error by 38.6% when compared to visual–inertial odometry (VIO). This low-cost, wearable solution effectively meets the autonomous positioning needs of rescuers in disaster scenarios.

In recent years, multi-criteria group decision-making (MCGDM) methods have attracted widespread attention in the academic community. However, most existing MCGDM approaches suffer from limitations in decision-makers’ expressive capacity and the loss of uncertain information. To address these issues, this study proposes a novel multi-criteria group decision-making (MCGDM) framework. First, we developed an evaluation information representation method called the hybrid probabilistic information set (HPIS), which allows DMs to fully express their opinions based on individual cognition using the most suitable form of representation. Second, the criteria importance through inter-criteria correlation (CRITIC) and the combined compromise solution (CoCoSo) methods are extended into the cloud model environment, ensuring that the rich uncertainty information is fully preserved and transmitted throughout the entire evaluation process. Finally, we apply the proposed MCGDM framework to a practical case study evaluating urban flood resilience within an urban agglomeration, to identify its vulnerable components. The results indicate that Baoding, Zhangjiakou, and Chengde are identified as the most vulnerable cities, necessitating immediate and targeted measures to bolster their flood defense capabilities. At the same time, decision-makers can select both qualitative and quantitative comments simultaneously and carry uncertainty information throughout the entire calculation process. Furthermore, the sensitivity and comparative analyses demonstrate the robustness and practical utility of the proposed method under the tested scenarios.