Construction sites with deep excavation in urban areas can induce ground deformation, potentially harming nearby infrastructure. Therefore, monitoring construction sites is crucial. Typically, a sidewalk is located adjacent to the construction site, and ground deformation can cause the displacement of paving blocks. Accurate measurement of paving block displacement and cracks is essential. This paper proposes an efficient automated detection and measurement method using a 3D laser line sensor on Unmanned Ground Vehicles (UGVs), emphasizing online measurement capabilities. The method involves two steps: detecting target objects via 2D projection from 3D point cloud data and measuring object features by reducing unnecessary data with the Clustered Piecewise Linear Fitting (CPLF) algorithm. This two-step process enhances parallelism between edge servers and devices, thereby reducing total processing time. Prototype implementation and experiments show that our method achieves low errors of accuracy and is suitable for automated online detection and measurement on UGVs.

中文翻译：

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在无人地面车辆上使用 3D 激光传感器进行铺路砖位移检测和测量


在城市地区进行深挖的建筑工地会引起地面变形，从而可能损害附近的基础设施。因此，监控建筑工地至关重要。通常，人行道位于施工现场附近，地面变形会导致铺路砖移位。准确测量铺路砖的位移和裂缝是必不可少的。本文提出了一种在无人地面车辆 （UGV） 上使用 3D 激光线传感器的高效自动化检测和测量方法，强调在线测量能力。该方法包括两个步骤：通过 3D 点云数据的 2D 投影来检测目标对象，以及通过使用聚类分段线性拟合 （CPLF） 算法减少不必要的数据来测量对象特征。这个两步过程增强了边缘服务器和设备之间的并行性，从而减少了总处理时间。原型实现和实验表明，该方法的准确率误差较低，适用于无人驾驶车辆的自动化在线检测和测量。