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;
2016-01-01
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
