Large-Scale Underground Mine Positioning and Mapping with LiDAR-Based Semantic Intersection Detection - Mining, Metallurgy & Exploration (2023)

Various coal mine robots (CMRs) and unmanned aerial vehicles (UAVs) are implemented to explore unknown mines for improving the safety and efficiency of mining. It is challenging for CMRs and UAVs to achieve accurate positioning due to the absence of GPS, poor lighting conditions, and similar geometric features in complex mine scenes. LiDAR-based localization and mapping methods are admittedly more accurate than others, while long-time running in large-scale scenarios will also introduce non-negligible cumulative errors. This study presents a semantic-aided LiDAR simultaneous localization and mapping (SLAM) with loop closure, which leverages the uniqueness of mine intersection structure to establish stable semantic loop closure. Specifically, we propose a semantic intersection descriptor of translation and rotation invariance, which encodes 3D point clouds of the same intersection from different positions and viewpoints into a unified image. By using the semantic descriptor, we can construct a constant loop constraint when the same intersection is revisited from different directions to reduce cumulative drift. We provide experimental validation using large datasets collected in two large underground mines, namely, a simulated Edgar mine deployed in ROS Gazebo and a public underground mine dataset provided by ETH. Experimental results show that the proposed method has higher localization accuracy and outperforms the existing LiDAR-based SLAM strategies.