Remote sensing methods which produce point clouds, such as terrestrial laser scanning (TLS), terrestrial photogrammetry (TP), and small unmanned aerial systems (sUAS) have become an integral component of geotechnical monitoring programs. Applications such as rock-slope monitoring benefit from multi-platform datasets to be acquired in two or more different epochs. Accurate registration of these datasets in a common coordinate system is essential for detecting slope changes. Their registration often relies on initial feature-based alignment followed by fine alignment with the iterative closest point (ICP) algorithm. When practical, ground control points (GCPs) and other surveying targets with well-defined coordinates are used. However, establishing such GCPs on rock surfaces can be difficult, expensive and dangerous. In addition, GCPs and targets can be lost or destroyed with time and re-establishing them is difficult. This paper develops an automated registration algorithm based on edge detection that can register multi-platform and multi-epoch point clouds. Rock-surface edges are expected to remain largely the same and be captured in point clouds collected in two different epochs. For edge detection, we use α-molecules that offer a unified framework of most multi-scale transforms that can be adapted to any rock-surface. Then the algorithm identifies edge correspondences based on the discrete Fréchet distance. From the corresponding edges we derive matching points between datasets. Transformation parameters are then derived through Procrustes analysis. Using real and simulated scenarios, we demonstrate the utility and performance of the proposed algorithm. For example, sUAS scenarios with 0 and 1 GCPs show that initial root mean square error (RMSE) values of a few decimeters drop to a few centimeters. Scenarios with simulated translations, rotations, and scale showed that the developed algorithm registers multi-platform point clouds with mm differences from their original RMSE values. Results demonstrate that the algorithm can successfully register multi-platform point clouds and support rockfall monitoring.

产生点云的遥感方法，例如地面激光扫描（TLS），地面摄影测量（TP）和小型无人航空系统（sUAS），已成为岩土工程监测计划不可或缺的组成部分。诸如岩石坡度监控之类的应用受益于在两个或更多个不同时期中获取的多平台数据集。这些数据集在公共坐标系中的准确配准对于检测坡度变化至关重要。它们的注册通常依赖于基于初始特征的对齐方式，然后依赖于迭代最近点（ICP）算法的精细对齐方式。在可行的情况下，将使用地面控制点（GCP）和其他具有明确定义坐标的测量目标。然而，在岩石表面上建立这样的GCP可能是困难，昂贵和危险的。此外，GCP和目标可能会随着时间的流逝而丢失或销毁，而重新建立它们很困难。本文开发了一种基于边缘检测的自动配准算法，该算法可以配准多平台和多历时的点云。预计岩石表面边缘将保持大致相同，并会在两个不同时期收集的点云中捕获。对于边缘检测，我们使用α-分子提供了可以适应任何岩石表面的大多数多尺度转换的统一框架。然后，该算法根据离散Fréchet距离识别边缘对应关系。从相应的边缘，我们得出数据集之间的匹配点。然后通过Procrustes分析得出转换参数。使用真实和模拟的场景，我们演示了该算法的实用性和性能。例如，具有0个GCP和1个GCP的sUAS方案显示，最初的均方根误差（RMSE）值从几分米下降到几厘米。带有模拟平移，旋转和比例的场景表明，开发的算法记录的多平台点云与其原始RMSE值有mm的差异。