Dahlke, D., Kaiser, S., & Bayer, S. (2023). Self-Localization: A proposal to equip first responders with a robust and accurate GNSS device. In Jaziar Radianti, Ioannis Dokas, Nicolas Lalone, & Deepak Khazanchi (Eds.), Proceedings of the 20th International ISCRAM Conference (pp. 242–251). Omaha, USA: University of Nebraska at Omaha.
Abstract: In this paper we explore the GNSS positioning capabilities in the context of search and rescue operations. Our contribution is a tool that robustly receives and precisely evaluates GNSS signals. The final positioning information is then transmitted to an orchestrator where other tools like augmented reality utilities or the command and control have access to. During the time from the project start in September 2021 to December 2022 the components have been chosen, and the design and software of the tool have been developed. Furthermore, some of the tool’s capabilities have been tested and compared during field trials with first responders and measurement campaigns. The developed tool outperforms the commonly used smartphone localization in terms of accuracy, operation time and time to get a GNSS fix. This reliability improvement helps to identify someones position in adverse conditions.
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Gkika, I., Pattas, D., Konstantoudakis, K., & Zarpalas, D. (2023). Object detection and augmented reality annotations for increased situational awareness in light smoke conditions. In Jaziar Radianti, Ioannis Dokas, Nicolas Lalone, & Deepak Khazanchi (Eds.), Proceedings of the 20th International ISCRAM Conference (pp. 231–241). Omaha, USA: University of Nebraska at Omaha.
Abstract: Innovative technologies powered by Computer Vision algorithms can aid first responders, increasing their situ ational awareness. However, adverse conditions, such as smoke, can reduce the efficacy of such algorithms by degrading the input images. This paper presents a pipeline of image de-smoking, object detection, and augmented reality display that aims to enhance situational awareness in smoky conditions. A novel smoke-reducing deep learning algorithm is applied as a preprocessing step, before state-of-the-art object detection. The detected objects and persons are highlighted in the user’s augmented reality display. The proposed method is shown to increase detection accuracy and confidence. Testing in realistic environments provides an initial evaluation of the method, both in terms of image processing and of usefulness to first responders.
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Tolt, G., Rydell, J., Tulldahl, M., Holmberg, M., Karlsson, O., & Bissmarck, F. (2023). The MAX Drone for Autonomous Indoor Exploration. In Jaziar Radianti, Ioannis Dokas, Nicolas Lalone, & Deepak Khazanchi (Eds.), Proceedings of the 20th International ISCRAM Conference (pp. 220–230). Omaha, USA: University of Nebraska at Omaha.
Abstract: This paper presents the concept and prototype implementation of a drone for Multi-purpose Autonomous eXploration of indoor environments – MAX. The purpose of MAX is to support first responders in the difficult task of assessing unknown and potentially dangerous or hostile situations in indoor or underground environments. The approach for addressing challenges associated with this task has been to construct a custom-designed drone based on requirements and conditions of first responder missions. This paper reports on the first phase of development of the MAX drone, aimed for experimentation with autonomy functionality in first responder contexts and for enabling further development of advanced higher-level planning functions. It describes the overall design of the MAX drone, its capabilities in terms of robust positioning and autonomous mission execution, along with the status of key enabling algorithms for exploration, such as target point selection and path planning.
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Paulini, M. S., Duran, D., Rice, M., Andrekanic, A., & Suri, N. (2023). KENNEL Threat Detection Boxes for First Responder Situational Awareness and Risk Management. In Jaziar Radianti, Ioannis Dokas, Nicolas Lalone, & Deepak Khazanchi (Eds.), Proceedings of the 20th International ISCRAM Conference (pp. 208–219). Omaha, USA: University of Nebraska at Omaha.
Abstract: KENNEL is a deployable IoT-based system consisting of a network of unattended ground sensors, known as Threat Detection Boxes (TDBs), which may be outfitted with any variety of custom and commercial-off-the-shelf sensors for hazard detection. The KENNEL system fills a technological gap for sensor fusion, interpretation, and real-time alerting via existing information management systems, such as Team Awareness Kit (TAK). First responders face a critical need for improved situational awareness, detection, and response to hazardous events. KENNEL provides a first of its kind, low-cost sensing & data fusion platform that is highly extensible, configurable, and self-sustaining, opening a world of modernization and innovation possibilities across the first responder domain. TDBs may also be statically or ad hoc deployed, improving flexibility, stand-off hazard detection, and resilience in the operational domain. From critical infrastructure monitoring to wearables, the system affords timeliness of critical information for effective risk management and increased personnel safety.
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Holzhüter, M., Huhle, G., Reuter-Oppermann, M., Hellriegel, J., & Klafft, M. (2023). Acceptance study on application systems to improve situational incident management through bi-directional communication between citizens and decision-makers in emergencies and crises situations. In Jaziar Radianti, Ioannis Dokas, Nicolas Lalone, & Deepak Khazanchi (Eds.), Proceedings of the 20th International ISCRAM Conference (pp. 197–207). Omaha, USA: University of Nebraska at Omaha.
Abstract: Efficient hazard prevention and disaster control depend on situational awareness. Situational information is – among others – provided by citizens on the ground. Disaster managers are often reluctant to use such information on a large scale or in a systematic way for fear of being overwhelmed by information overload in a stressful crisis. New information technologies for crisis management are strongly dependent on the acceptance of the people using them and can only be successful as socio-technical systems. Therefore, 354 employees of public and private emergency operation centres as well as members of crisis management teams were asked to assess different information sharing technologies. 504 people from the public responded to an online survey about their willingness to use such technologies. The results indicate a high level of acceptance by both user groups for bi directional communication technologies for situation management and the improvement of situational awareness.
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