An automatic method for the measurement of coarse particle movement in a mountain riverbed. (April 2021)

• Record Type: Journal Article
• Authors: Walicka, Agata; Pfeifer, Norbert; Borkowski, Andrzej; Jóźków, Grzegorz
• Is Part Of: Measurement. Volume 174(2021)
• Publication Date: 2021-04
• Subjects: Iterative closest point algorithm -- Terrestrial laser scanning -- Morphodynamics -- Displacement; Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2021.109029
• Languages: English
• ISSNs: 0263-2241

Abstract

Highlights: We propose a new method of matching the point clouds representing natural objects. 3D displacement and rotation parameters of particles are determined as a result. The method was tested on TLS point cloud and used for fluvial transport monitoring. The method is based on the ICP algorithm and the moving window approach. The algorithm exploits key points representing a locally maximum Gaussian curvature. Abstract: In this paper, we propose an automatic approach that is able to recognize the corresponding particles in multi-temporal point clouds and to determine 3D displacement vectors and the rotation parameters between them. The core of this method is a moving window approach combined with the Iterative Closest Point (ICP) algorithm. The particle-wise ICP algorithm is supplemented by initial transformation parameters obtained by key point matching. The routine is designed to be applied to natural objects, which are characterized by their complex geometry. The method's performance is verified using terrestrial laser scanning data sets representing a mountain riverbed. The experiments performed show that the method enables us to recognize corresponding particles with an effectiveness of 85%. The mean absolute distances between the tile of a point cloud and the particle after alignment are used as the accuracy of the alignment. The median of these values is equal to 2 mm.