Abstract Rock bolts are a crucial part of underground infrastructure support; however, current methods to locate and record their positions are manual, time consuming and generally incomplete. This paper describes an effective method to automatically locate supporting rock bolts from a 3D laser scanned point cloud. The proposed method utilises a machine learning classifier combined with point descriptors based on neighbourhood properties to classify all data points as either ‘bolt’ or ‘not-bolt’ before using the Density Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm to divide the results into candidate bolt objects. The centroids of these objects are then computed and output as simple georeferenced 3D coordinates to be used by surveyors, mine managers and automated machines. Two classifiers were tested, a random forest and a shallow neural network, with the neural network providing the more accurate results. Alongside the different classifiers, different input feature types were also examined, including the eigenvalue based geometric features popular in the remote sensing community and the point histogram based features more common in the mobile robotics community. It was found that a combination of both feature sets provided the strongest results. The obtained precision and recall scores were 0.59 and 0.70 for the individual laser points and 0.93 and 0.86 for the bolt objects. This demonstrates that the model is robust to noise and misclassifications, as the bolt is still detected even if edge points are misclassified, provided that there are enough correct points to form a cluster. In some cases, the model can detect bolts which are not visible to the human interpreter.

中文翻译：

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一种基于激光扫描数据检测地下矿井支护岩栓的机器学习方法

摘要 锚杆是地下基础设施支护的重要组成部分；然而，目前定位和记录他们的位置的方法是手动的、耗时的并且通常不完整。本文介绍了一种从 3D 激光扫描点云自动定位支撑岩石锚杆的有效方法。所提出的方法利用机器学习分类器结合基于邻域属性的点描述符将所有数据点分类为“螺栓”或“非螺栓”，然后使用基于密度的噪声应用空间聚类（DBSCAN）算法划分结果转化为候选螺栓对象。然后计算这些对象的质心并将其输出为简单的地理参考 3D 坐标，供测量员、矿山经理和自动化机器使用。测试了两个分类器，随机森林和浅层神经网络，神经网络提供更准确的结果。除了不同的分类器，还检查了不同的输入特征类型，包括遥感社区中流行的基于特征值的几何特征和移动机器人社区中更常见的基于点直方图的特征。发现两个特征集的组合提供了最强的结果。获得的精度和召回分数分别为 0.59 和 0.70，对于单个激光点，螺栓对象为 0.93 和 0.86。这表明该模型对噪声和错误分类具有鲁棒性，因为即使边缘点被错误分类，螺栓仍然被检测到，前提是有足够的正确点来形成一个集群。在某些情况下，