Robust Localization for Underground Mining Vehicles: An Application in a Room and Pillar Mine

Abstract

Most autonomous navigation systems used in underground mining vehicles such as load–haul–dump (LHD) vehicles and trucks use 2D light detection and ranging (LIDAR) sensors and 2D representations/maps of the environment. In this article, we propose the use of 3D LIDARs and existing 3D simultaneous localization and mapping (SLAM) jointly with 2D mapping methods to produce or update 2D grid maps of underground tunnels that may have significant elevation changes. Existing mapping methods that only use 2D LIDARs are shown to fail to produce accurate 2D grid maps of the environment. These maps can be used for robust localization and navigation in different mine types (e.g., sublevel stoping, block/panel caving, room and pillar), using only 2D LIDAR sensors. The proposed methodology was tested in the Werra Potash Mine located at Philippsthal, Germany, under real operational conditions. The obtained results show that the enhanced 2D map-building method produces a superior mapping performance compared with a 2D map generated without the use of the 3D LIDAR-based mapping solution. The 2D map generated enables robust 2D localization, which was tested during the operation of an autonomous LHD, performing autonomous navigation and autonomous loading over extended periods of time.

LiDAR Dynamic Target Detection Based on Multidimensional Features

“…Among them, simultaneous localization and mapping based on light detection and ranging (LiDAR SLAM) has the advantages of intuitive mapping, high precision, and the ability to work in all weather conditions [ 2 ]. It is widely used in resource exploration, urban planning, agricultural and forestry development, mining inspections, atmospheric detection, and other fields [ 3 , 4 , 5 , 6 ]. However, practical application scenes often contain dynamic targets.…”