Abstract
Abstract. Point cloud produced from technologies such as terrestrial laser scanning (TLS) and photogrammetry (terrestrial and aerial) are widely used in rockfall monitoring applications due to the wealth of data they provide. In such applications, the acquisition and registration of multi-epoch point clouds is necessary. In addition, point clouds can be derived from different sensors (e.g., lasers versus digital cameras) and different platforms (terrestrial versus aerial). Therefore, registration methods should be able to support multi-platform datasets. Currently, registration of multi-platform datasets is done with manual intervention, and automatic registration is difficult. While registration of TLS point clouds can be achieved by targets that are not on the rock surface, this is not the case for photogrammetric methods, as ground control points (GCPs) should be located on the rock surface. Such GCPs can be lost or destroyed with time, and re-establishing them is difficult. Automated registration often relies on feature-based algorithms with refinement using the iterative closest point (ICP) algorithm. This paper presents a novel registration approach of multi-epoch and multi-platform point clouds to support rockfall monitoring applications. The registration method is based on edges that are detected in the different datasets using α-molecules. The paper shows application examples of the novel approach at different rock slopes in Colorado. Results demonstrate that the developed method in many cases performs better than the well-known ICP method and can be used to register point clouds and support rockfall monitoring.
Highlights
Point cloud collection methods such as terrestrial laser scanning (TLS), terrestrial photogrammetry (TP), and aerial photogrammetry from small unmanned aerial systems are widely being used in rockfall monitoring applications, as they offer datasets with high accuracy and spatial resolution
Registration of point clouds often relies on targets and/or ground control points (GCPs) or registration algorithms
Methods that were designed for TLS setups are not suitable for multi-platform points clouds such as those derived from TLS, TP, and small unmanned aerial systems (sUASs) methods, as in the general case the input datasets will not be levelled
Summary
Point cloud collection methods such as terrestrial laser scanning (TLS), terrestrial photogrammetry (TP), and aerial photogrammetry from small unmanned aerial systems (sUASs) are widely being used in rockfall monitoring applications, as they offer datasets with high accuracy and spatial resolution. The input point clouds need to have a good initial registration, as the algorithm can become trapped in local minima (Attia and Slama 2017; Li et al 2020) To address this issue, several ICP variants have been developed over the years in order to enhance registration performance (e.g., Bae and Lichti 2008; Wujanz et al 2018; Kromer et al 2019; Li et al 2020). For registration of rock-surfaces, feature-based methods build feature vectors using the 3D coordinates of points and describing the local surface properties in the vicinity of a point Based on those feature vectors point correspondences are identified, which are used to estimate the transformation between two input point clouds. Some examples of featurebased algorithms are the scale-invariant feature transform (Lowe 1999), intrinsic shape signatures (Zhong 2009), and
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