Hybrid algorithms of laser scanning point cloud for topological analysis

Результат исследований: Публикации в книгах, отчётах, сборниках, трудах конференцийглава/разделнаучнаярецензирование

Выдержка

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.

Язык оригиналаанглийский
Название основной публикацииAdvances in Intelligent Systems and Computing
ИздательSpringer
Страницы223-234
Число страниц12
DOI
СостояниеОпубликовано - 1 янв 2019

Серия публикаций

НазваниеAdvances in Intelligent Systems and Computing
Том797
ISSN (печатное издание)2194-5357

Предметные области Scopus

  • Системотехника
  • Компьютерные науки (все)

Цитировать

Badenko, V., Fedotov, A., & Vinogradov, K. (2019). Hybrid algorithms of laser scanning point cloud for topological analysis. В Advances in Intelligent Systems and Computing (стр. 223-234). (Advances in Intelligent Systems and Computing; Том 797). Springer. https://doi.org/10.1007/978-981-13-1165-9_20
Badenko, Vladimir ; Fedotov, Alexander ; Vinogradov, Konstantin. / Hybrid algorithms of laser scanning point cloud for topological analysis. Advances in Intelligent Systems and Computing. Springer, 2019. стр. 223-234 (Advances in Intelligent Systems and Computing).
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Badenko, V, Fedotov, A & Vinogradov, K 2019, Hybrid algorithms of laser scanning point cloud for topological analysis. в Advances in Intelligent Systems and Computing. Advances in Intelligent Systems and Computing, том. 797, Springer, стр. 223-234. https://doi.org/10.1007/978-981-13-1165-9_20

Hybrid algorithms of laser scanning point cloud for topological analysis. / Badenko, Vladimir; Fedotov, Alexander; Vinogradov, Konstantin.

Advances in Intelligent Systems and Computing. Springer, 2019. стр. 223-234 (Advances in Intelligent Systems and Computing; Том 797).

Результат исследований: Публикации в книгах, отчётах, сборниках, трудах конференцийглава/разделнаучнаярецензирование

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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.

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Badenko V, Fedotov A, Vinogradov K. Hybrid algorithms of laser scanning point cloud for topological analysis. В Advances in Intelligent Systems and Computing. Springer. 2019. стр. 223-234. (Advances in Intelligent Systems and Computing). https://doi.org/10.1007/978-981-13-1165-9_20