What localizes beneath: A metric multisensor localization and mapping system for autonomous underground mining vehicles

Tightly Coupled LiDAR-Inertial Odometry and Mapping for Underground Environments

The demand for autonomous exploration and mapping of underground environments has significantly increased in recent years. However, accurately localizing and mapping robots in subterranean settings presents notable challenges. This paper presents a tightly coupled LiDAR-Inertial odometry system that combines the NanoGICP point cloud registration method with IMU pre-integration using incremental smoothing and mapping. Specifically, a point cloud affected by dust particles is first filtered out and separated into ground and non-ground point clouds (for ground vehicles). To maintain accuracy in environments with spatial variations, an adaptive voxel filter is employed, which reduces computation time while preserving accuracy. The estimated motion derived from IMU pre-integration is utilized to correct point cloud distortion and provide an initial estimation for LiDAR odometry. Subsequently, a scan-to-map point cloud registration is executed using NanoGICP to obtain a more refined pose estimation. The resulting LiDAR odometry is then employed to estimate the bias of the IMU. We comprehensively evaluated our system on established subterranean datasets. These datasets were collected by two separate teams using different platforms during the DARPA Subterranean (SubT) Challenge. The experimental results demonstrate that our system achieved performance enhancements as high as 50-60% in terms of root mean square error (RMSE).

3D Global Localization in the Underground Mine Environment Using Mobile LiDAR Mapping and Point Cloud Registration

This study proposes a 3D global localization method that implements mobile LiDAR mapping and point cloud registration to recognize the locations of objects in an underground mine. An initial global point cloud map was built for an entire underground mine area using mobile LiDAR; a local LiDAR scan (local point cloud) was generated at the point where underground positioning was required. We calculated fast point feature histogram (FPFH) descriptors for the global and local point clouds to extract point features. The match areas between the global and the local point clouds were searched and aligned using random sample consensus (RANSAC) and iterative closest point (ICP) registration. The object’s location on the global coordinate system was measured using the LiDAR sensor trajectory. Field experiments were performed at the Gwan-in underground mine using three mobile LiDAR systems. The local point cloud dataset formed for the six areas of the underground mine precisely matched the global point cloud, with a low average error of approximately 0.13 m, regardless of the type of mobile LiDAR system used. In addition, the LiDAR senor trajectory was aligned on the global coordinate system to confirm the change in the dynamic object’s position over time.

Robotic autonomous systems for earthmoving equipment operating in volatile conditions and teaming capacity: a survey

There has been an increasing interest in the application of robotic autonomous systems (RASs) for construction and mining, particularly the use of RAS technologies to respond to the emergent issues for earthmoving equipment operating in volatile environments and for the need of multiplatform cooperation. Researchers and practitioners are in need of techniques and developments to deal with these challenges. To address this topic for earthmoving automation, this paper presents a comprehensive survey of significant contributions and recent advances, as reported in the literature, databases of professional societies, and technical documentation from the Original Equipment Manufacturers (OEM). In dealing with volatile environments, advances in sensing, communication and software, data analytics, as well as self-driving technologies can be made to work reliably and have drastically increased safety. It is envisaged that an automated earthmoving site within this decade will manifest the collaboration of bulldozers, graders, and excavators to undertake ground-based tasks without operators behind the cabin controls; in some cases, the machines will be without cabins. It is worth for relevant small- and medium-sized enterprises developing their products to meet the market demands in this area. The study also discusses on future directions for research and development to provide green solutions to earthmoving.