This study introduces a global re-localization method for mobile mining vehicles equipped with solid-state LiDAR in underground haulage roadways. The method includes feature extraction from LiDAR data, an improved fast Euclidean clustering algorithm for point cloud classification, and a descriptor based on Delaunay and Extended Delaunay Triangles centered on roadway features. A global re-localization process is established using a historical keyframe search strategy, enabling swift re-localization of mining equipment. 21 experiments were conducted with a load-haul-dump vehicle fitted with solid-state LiDAR across three underground mine haulage roadways. The proposed method achieves rapid global re-localization with an accuracy of within 0.2 m in under 40 ms, demonstrating the significant advantages and practicality of the proposed global re-localization method.

中文翻译：

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一种新型的地下采矿车辆在运输巷道中的全局重新定位方法：配备固态 LiDAR 的负载-运输-自卸车的案例研究


该文介绍了一种在地下运输巷道中配备固态 LiDAR 的移动采矿车辆的全局再定位方法。该方法包括从 LiDAR 数据中提取特征、用于点云分类的改进的快速欧几里得聚类算法，以及基于以道路特征为中心的 Delaunay 和扩展 Delaunay 三角形的描述符。使用历史关键帧搜索策略建立全局重新定位过程，从而能够快速重新定位采矿设备。使用装有固态 LiDAR 的负载-运输-自卸车在三条地下矿井运输道路上进行了 21 次实验。所提方法在 40 ms 内以 0.2 m 以内的精度实现了快速全局重定位，证明了所提全局重定位方法的显著优势和实用性。