Standard

Hybrid algorithms of laser scanning point cloud for topological analysis. / Badenko, V.; Fedotov, A.; Vinogradov, K.

In: Advances in Intelligent Systems and Computing, Vol. 797, 2019, p. 223-234.

Research output: Contribution to journal › Article

Harvard

Badenko, V, Fedotov, A & Vinogradov, K 2019, 'Hybrid algorithms of laser scanning point cloud for topological analysis.', Advances in Intelligent Systems and Computing, vol. 797, pp. 223-234. <http://elibrary.ru/item.asp?id=38646150>

APA

Badenko, V., Fedotov, A., & Vinogradov, K. (2019). Hybrid algorithms of laser scanning point cloud for topological analysis. Advances in Intelligent Systems and Computing, 797, 223-234. http://elibrary.ru/item.asp?id=38646150

Vancouver

Badenko V, Fedotov A, Vinogradov K. Hybrid algorithms of laser scanning point cloud for topological analysis. Advances in Intelligent Systems and Computing. 2019;797:223-234.

Author

Badenko, V. ; Fedotov, A. ; Vinogradov, K. / Hybrid algorithms of laser scanning point cloud for topological analysis. In: Advances in Intelligent Systems and Computing. 2019 ; Vol. 797. pp. 223-234.

BibTeX

@article{2a406d27f45445eb975675f374f5245d,
title = "Hybrid algorithms of laser scanning point cloud for topological analysis.",
abstract = "Laser scanning technologies are widely used to solve civil engineering problems and land use management in a GIS environment including digital terrain models (DTMs) creation. Some gaps in raw laser scanning data processing algorithms for DTM are analyzed. Algorithms for filtration, triangulation, and defragmentation are proposed. Advantages and disadvantages of the algorithms proposed are discussed. Triangulation algorithm can serve to defragment cloud of laser scanning points into semantic component parts. Defragmentation includes recognition of engineering objects and other objects of the terrain and their delineation. Results of applications to real problems show the robustness of algorithms proposed.",
author = "V. Badenko and A. Fedotov and K. Vinogradov",
year = "2019",
language = "English",
volume = "797",
pages = "223--234",
journal = "Advances in Intelligent and Soft Computing",
issn = "1867-5662",
publisher = "Springer Nature",

}

RIS

TY - JOUR

T1 - Hybrid algorithms of laser scanning point cloud for topological analysis.

AU - Badenko, V.

AU - Fedotov, A.

AU - Vinogradov, K.

PY - 2019

Y1 - 2019

N2 - Laser scanning technologies are widely used to solve civil engineering problems and land use management in a GIS environment including digital terrain models (DTMs) creation. Some gaps in raw laser scanning data processing algorithms for DTM are analyzed. Algorithms for filtration, triangulation, and defragmentation are proposed. Advantages and disadvantages of the algorithms proposed are discussed. Triangulation algorithm can serve to defragment cloud of laser scanning points into semantic component parts. Defragmentation includes recognition of engineering objects and other objects of the terrain and their delineation. Results of applications to real problems show the robustness of algorithms proposed.

AB - Laser scanning technologies are widely used to solve civil engineering problems and land use management in a GIS environment including digital terrain models (DTMs) creation. Some gaps in raw laser scanning data processing algorithms for DTM are analyzed. Algorithms for filtration, triangulation, and defragmentation are proposed. Advantages and disadvantages of the algorithms proposed are discussed. Triangulation algorithm can serve to defragment cloud of laser scanning points into semantic component parts. Defragmentation includes recognition of engineering objects and other objects of the terrain and their delineation. Results of applications to real problems show the robustness of algorithms proposed.

M3 - Article

VL - 797

SP - 223

EP - 234

JO - Advances in Intelligent and Soft Computing

JF - Advances in Intelligent and Soft Computing

SN - 1867-5662

ER -

ID: 78444285