The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLII-2/W13
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W13, 1149–1154, 2019
https://doi.org/10.5194/isprs-archives-XLII-2-W13-1149-2019
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W13, 1149–1154, 2019
https://doi.org/10.5194/isprs-archives-XLII-2-W13-1149-2019

  05 Jun 2019

05 Jun 2019

TLS POINT CLOUD REGISTRATION FOR DETECTING CHANGE IN INDIVIDUAL ROCKS OF A MOUNTAIN RIVER BED

A. Walicka1, N. Pfeifer2, G. Jóźków1, and A. Borkowski1 A. Walicka et al.
  • 1Institute of Geodesy and Geoinformatics, Wrocław University of Environmental and Life Sciences, Wrocław, Poland
  • 2Department of Geodesy and Geoinformation, TU Wien, Vienna, Austria

Keywords: terrestrial laser scanning, Iterative Closest Point algorithm, ICP, point cloud registration, sediment transport monitoring

Abstract. Remote sensing techniques are an important tool in fluvial transport monitoring, since they allow for effective evaluation of the volume of transported material. Nevertheless, there is no methodology for automatic calculation of movement parameters of individual rocks. These parameters can be determined by point cloud registration. Hence, the goal of this study is to develop a robust algorithm for terrestrial laser scanning point cloud registration. The registration is based on Iterative Closest Point algorithm, which requires well established initial parameters of transformation. Thus, we propose to calculate the initial parameters based on key points representing the maximum of Gaussian curvature. For each key point the set of geometrical features is calculated. The key points are then matched between two point clouds as a nearest neighbor in feature domain. Different combinations of neighborhood sizes, feature subsets, metrics and number of nearest neighbors were tested to obtain the highest ratio between properly and improperly matched key points. Finally, RANSAC algorithm was used to calculate the initial transformation parameters between the point clouds and the ICP algorithm was used for calculation of final transformation parameters. The investigations carried out on sample point clouds representing rocks enabled the adjustment of parameters of the algorithm and showed that the Gaussian curvature can be used as a 3-dimentional key point detector for such objects. The proposed algorithm enabled to register point clouds with the mean distance between point clouds equal to 3 mm.