Version 1
: Received: 21 June 2022 / Approved: 22 June 2022 / Online: 22 June 2022 (03:37:45 CEST)
How to cite:
Štroner, M.; Urban, R.; Línková, L. Multi-View and Shift Rasterization Algorithm (MVSR) for Effective Identification of Ground in Dense Point Clouds. Preprints2022, 2022060300. https://doi.org/10.20944/preprints202206.0300.v1
Štroner, M.; Urban, R.; Línková, L. Multi-View and Shift Rasterization Algorithm (MVSR) for Effective Identification of Ground in Dense Point Clouds. Preprints 2022, 2022060300. https://doi.org/10.20944/preprints202206.0300.v1
Štroner, M.; Urban, R.; Línková, L. Multi-View and Shift Rasterization Algorithm (MVSR) for Effective Identification of Ground in Dense Point Clouds. Preprints2022, 2022060300. https://doi.org/10.20944/preprints202206.0300.v1
APA Style
Štroner, M., Urban, R., & Línková, L. (2022). Multi-View and Shift Rasterization Algorithm (MVSR) for Effective Identification of Ground in Dense Point Clouds. Preprints. https://doi.org/10.20944/preprints202206.0300.v1
Chicago/Turabian Style
Štroner, M., Rudolf Urban and Lenka Línková. 2022 "Multi-View and Shift Rasterization Algorithm (MVSR) for Effective Identification of Ground in Dense Point Clouds" Preprints. https://doi.org/10.20944/preprints202206.0300.v1
Abstract
With the ever-increasing popularity of unmanned aerial vehicles and other platforms providing dense point clouds, universal filters for accurate identification of ground points in such dense clouds are needed. Many filters have been proposed and are widely used, usually based on the determination of an original surface approximation and subsequent identification of points within a predefined distance from such surface. In this paper, we present a new filter. This Multi-view and shift rasterization algorithm (MVSR) is based on an entirely different principle, i.e., on the identification of just the lowest points in individual grid cells, shifting the grid along both planar axis and subsequent tilting of the entire grid – after each of these steps, one lowest point per cell is detected. The principle is presented in detail and compared both visually and numerically to other commonly used ground filters (PMF, SMRF, CSF, ATIN) on three sites with different ruggedness and vegetation density. Visually, the MVSR filter showed the smoothest and thinnest ground profiles, with ATIN the only filter performing comparably (although the profiles were somewhat thicker and not as complete as MVSR-acquired ground). The same was confirmed when comparing ground filtered by other filters with the MVSR-based surface. The goodness of fit with the original cloud is demonstrated by the root mean square deviations (RMSD) of the points from the original cloud found below the MVSR-generated surface (ranging, depending on site, between 0.6-2.5 cm). ATIN again performed closest to MVSR, with RMSDs of ground filtered points found above MVSR-based surface at individual sites ranging between 4.5-7.4 cm. The remaining filters performed comparable in the simplest flat area but poorly in rugged and much-vegetated sites, with RMSDs above the MVSR surface ranging at such sites from 21 to 95 cm. In conclusion, the novel filter presented in this paper performed outstandingly at all sites, identifying the ground points with great accuracy while filtering out the maximum of vegetation/above-ground points. The filter dilutes the cloud somewhat; in such dense point clouds, however, this can be perceived rather as a benefit than as a disadvantage.
Keywords
Point cloud; Ground filtering; Classification
Subject
Engineering, Control and Systems Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
