This paper aims at exploring a novel approach for indoor localisation by exploiting data fusion. Specifically, personnel localisation in rescue scenarios is addressed: the key idea is to increase the situation awareness of rescuers. A pedestrian dead reckoning algorithm based on waist mounted inertial sensors is designed to cope with different human activities. The drifting estimate is re-calibrated by using information gathered from the environment. The outcomes of experimental trials performed in a real scenario are reported.

De Cillis, F., De Simio, F., Inderst, F., Faramondi, L., Pascucci, F., & Setola, R. (2016). Improving situational awareness for first responders. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp.355-361). Springer Verlag [10.1007/978-3-319-31664-2_36].

Improving situational awareness for first responders

INDERST, FEDERICA;FARAMONDI, LUCA;PASCUCCI, Federica;
2016

Abstract

This paper aims at exploring a novel approach for indoor localisation by exploiting data fusion. Specifically, personnel localisation in rescue scenarios is addressed: the key idea is to increase the situation awareness of rescuers. A pedestrian dead reckoning algorithm based on waist mounted inertial sensors is designed to cope with different human activities. The drifting estimate is re-calibrated by using information gathered from the environment. The outcomes of experimental trials performed in a real scenario are reported.
9783319316635
9783319316635
De Cillis, F., De Simio, F., Inderst, F., Faramondi, L., Pascucci, F., & Setola, R. (2016). Improving situational awareness for first responders. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp.355-361). Springer Verlag [10.1007/978-3-319-31664-2_36].
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11590/300569
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