The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIII-B2-2021
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2021, 693–700, 2021
https://doi.org/10.5194/isprs-archives-XLIII-B2-2021-693-2021
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2021, 693–700, 2021
https://doi.org/10.5194/isprs-archives-XLIII-B2-2021-693-2021

  28 Jun 2021

28 Jun 2021

A NOVEL APPROACH TO REGISTER MULTI-PLATFORM POINT CLOUDS FOR ROCKFALL MONITORING

D. Bolkas1, G. Walton2, R. Kromer3, T. Sichler4, and L. Weidner2 D. Bolkas et al.
  • 1Department of Surveying Engineering, Pennsylvania State University, Wilkes-Barre Campus, Dallas, PA, USA
  • 2Department of Geology and Geological Engineering, Colorado School of Mines, Golden, CO, USA
  • 3School of Earth and Environment, University of Leeds, Leeds, UK
  • 4Department of Electrical Engineering Technology, Pennsylvania State University, Wilkes-Barre Campus, Dallas, PA, USA

Keywords: point clouds, registration, rockfall monitoring, UAS, laser scanning, edge detection

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.