Localization of avalanche victims using robocentric SLAM

A person buried by a snow avalanche can be found by measuring the magnetic field generated by an avalanche beacon or ARVA carried by the victim. However, the signals received are difficult to interpret and require people with good training on the actual searching techniques. In this paper we show that the search can be automated using SLAM techniques. The rescuer is equipped with an inertial sensor to estimate its own motion and a triple antenna to obtain 3D measurements of the magnetic field generated by the victim's ARVA. Both measurements are used to build a "robocentric" map that contains the location of the victim relative to the rescuer. To solve this highly non-linear SLAM problem we propose and compare two alternative solutions based on a sum of Gaussians (SOGs) filter and a particle filter. We present simulation results showing that, for comparable computing times, the SOGs solution gives more accurate results