Autonomous underground navigation of an LHD using a combined ICP-EKF approach

Abstract

A new approach for the autonomous navigation of a load-haul-dump (LHD) truck in an underground mine is presented. The development of a minimal-structure combined ICP-EKF algorithm utilizing a scanning-laser range-finder for the localization of the vehicle is described. The iterative closest point (ICP) algorithm is employed for matching the scanned data to an existing map in the form of a poly-line. This combined approach efficiently deals with the uncertainty present in the range data. An extended Kalman filter (EKF) algorithm is employed, that exploits a nonlinear kinematic model incorporating the vehicle-slip, a nonlinear observation model based on the vertices of the poly-line map, and the bearing of the laser-observations. This provides reliable vehicle estimates. Real data gathered during a trial run in the mine is employed in testing the efficiency of this approach which is found to be robust with respect to occlusions and outliers, demonstrating the successful navigation of the LHD.