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Map−aided Navigation for Emergency Searches

J. Wahlström‚ P. Porto Buarque de Gusmão‚ A. Markham and N. Trigoni


Real-time positioning of emergency personnel has been an active research topic for many years. However, studies on how to improve navigation accuracy by using prior information on the idiosyncratic motion characteristics of firefighters are scarce. This paper presents an algorithm for generating pseudo observations of position and orientation based on standard search patterns used by fire-fighters. The iterative closest point algorithm is used to compare walking trajectories estimated from inertial odometry with search patterns generated from digital maps. The resulting fitting errors are then used to integrate the pseudo observations into a map-aided navigation filter. Specifically, we present a sequential Monte Carlo solution where the pattern comparison is used to both update particle weights and create new particle samples. Experimental results involving professional firefighters demonstrate that the proposed pseudo observations can achieve a stable localization error of about one meter, and offer increased robustness in the presence of map errors.

Book Title
2019 15th International Conference on Distributed Computing in Sensor Systems (DCOSS)
cartography;distance measurement;emergency management;emergency services;inertial navigation;iterative methods;Monte Carlo methods;particle filtering (numerical methods);position control;pattern comparison;professional firefighters;pseudoobservations;map errors;emergency searches;real−time positioning;emergency personnel;standard search patterns;fire−fighters;iterative closest point algorithm;inertial odometry;digital maps;map−aided navigation filter;sequential Monte Carlo solution;particle weights;Navigation;Firefighter localization;Indoor Navigation