Map-building and map-based localization in an underground-mine by statistical pattern matching

This paper reports on the map-building and map-based localization of a load-haul-dump (LHD) truck in an underground mine using statistical pattern-matching techniques utilizing range images obtained from a scanning laser range-finder The map-building approach is based on an extended Kalman filter (EKF) and the resulting map is composed of poly-lines. Three approaches are proposed for the localization of the vehicle, namely the iterative closest point (ICP) approach, a reflective beacon based approach and the combined ICP-EKF approach, wherein, the last two approaches explicitly take into account the uncertainty associated with the observation data. These approaches are then compared using data gathered from an underground mine in Queensland, Australia for their relative merits subject to various factors and the corresponding results are presented