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利用移动激光雷达测绘和点云配准在地下矿山环境中进行三维全局定位

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

作者信息

Baek Jieun, Park Junhyeok, Cho Seongjun, Lee Changwon

机构信息

Mineral Resources Division, Korea Institute of Geoscience and Mineral Resources, Daejeon 34132, Korea.

出版信息

Sensors (Basel). 2022 Apr 8;22(8):2873. doi: 10.3390/s22082873.

DOI:10.3390/s22082873
PMID:35458856
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9029966/
Abstract

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.

摘要

本研究提出了一种三维全局定位方法，该方法通过实施移动激光雷达测绘和点云配准来识别地下矿井中物体的位置。利用移动激光雷达为整个地下矿区构建了初始全局点云地图；在需要进行地下定位的点生成局部激光雷达扫描（局部点云）。我们为全局和局部点云计算快速点特征直方图（FPFH）描述符以提取点特征。使用随机抽样一致性（RANSAC）和迭代最近点（ICP）配准来搜索和对齐全局与局部点云之间的匹配区域。利用激光雷达传感器轨迹测量物体在全局坐标系上的位置。在观音地下矿井使用三个移动激光雷达系统进行了现场实验。无论使用何种类型的移动激光雷达系统，为地下矿井六个区域形成的局部点云数据集都与全局点云精确匹配，平均误差约为0.13米，较低。此外，激光雷达传感器轨迹在全局坐标系上对齐，以确认动态物体位置随时间的变化。

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DOPNet: Achieving Accurate and Efficient Point Cloud Registration Based on Deep Learning and Multi-Level Features.DOPNet：基于深度学习和多层次特征的精确高效点云配准。
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