University of Oxford
Accurate and robust tracking and mapping of first responders is key to improved situational awareness, efficiency and enhanced safety. Conventional positioning techniques e.g. based on GPS, do not work in complex indoor environments. Through the NIST-funded IPSER project, the University of Oxford and its first responder partners have been working towards tackling this problem, using a combination of novel sensor modalities and algorithmic innovations e.g. deep learning. We have identified three modalities which are particularly robust to the challenges faced in dark, smoke-filled indoor environments, namely, thermal imaging, inertial tracking and millimeter-wave radar. Combining this data together, we show progress towards providing accurate and robust tracking and mapping and results from simulated and real fire trials. - July 2019
Principle Investigator: Niki Trigoni
University of Oxford
Location based services (LBS) for GPS-denied environments have gained significant maturity in recent years. Although they have started being used in commercial environments, there are still several challenges that prevent their immediate adoption in emergency scenarios. For example, emergency scenarios preclude the use of LBS that require intensive survey or infrastructure deployment. Yet, the requirements for coverage and availability are paramount; it is not acceptable to have blind areas where people cannot be positioned, or their positions cannot be communicated to the incident commander. The requirements for reliability and accuracy are also particularly strict - sub 3-meter accuracy (95% of the time). Floorplan information may not always be available, and even if it is, it may have changed as a result of the incident. Finally, adverse conditions like smoke may hinder the use of some sensors (e.g. cameras).
The high-level aim of this project is to develop fit-for-purpose location based services for emergency responders, addressing the unique challenges discussed above.
The objectives are to develop:
1. pervasive LBS systems that work everywhere - from high rise buildings to deep basements, and from steel/concrete frame structures to smaller wood framed or load bearing masonry buildings
2. accurate LBS that can locate responders in 3D with meter level accuracy
3. reliable LBS that can ensure such accuracy can be achieved even in adverse conditions (smoke, fire, wall collapse) and is reliably communicated to the incident commander with low latency.
The team plans to adopt three distinct approaches to addressing these objectives. For pervasiveness, the team will use inherently portable technologies, such as inertial tracking and vision-based egocentric localization, which require no prior survey or deployment. For accuracy, the team will leverage novel fusion methods that play on the strengths of each technology, and can mitigate the impact of noisy sensors and challenging building layouts. For reliability, the team will use a novel robust communication and positioning modality - low frequency magnetic fields - that can reach responders even if they are trapped under thick layers of rubble. This project will further boost reliability by designing novel adaptive techniques that can dynamically change sensor sampling to respond to changing conditions and dynamically evolving user requirements.
Reliable LBS have been the holy grail for emergency responders for decades. If the project is successful, it will become a key safety tool for emergency teams, enabling better coordination and faster incident resolution, and preventing fatalities and injuries.