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A Comparative Analysis of Point Clouds Acquired from UAV Photogrammetry and UAV-based LiDAR in Built Environment

9 pagesPublished: May 26, 2024

Abstract

Advancements in reality capture techniques have transformed the use of 3D datasets into a crucial resource for surveying and documentation in the Architectural, Engineering, and Construction (AEC) field. Unmanned aerial vehicles (UAVs) are widely employed in this domain, offering high- quality mapping data. However, issues related to volumetric measurement accuracy in UAV-based 3D models have been observed. In contrast, UAV-based Light Detection and Ranging (LiDAR) technology has emerged as a promising alternative, with superior coverage and accuracy. This study aims to conduct a comparative analysis of point clouds acquired from a camera-equipped UAV and a LiDAR-equipped UAV. The findings reveal that UAV-based photogrammetry demonstrates less relative accuracy compared to UAV-based LiDAR, thus providing valuable insights for professionals in the AEC industry. The results of the study may help small and medium-sized construction companies that have limited resources for LiDAR investment.

Keyphrases: drone, lidar, photogrammetry, point cloud comparison, uav

In: Tom Leathem, Wes Collins and Anthony Perrenoud (editors). Proceedings of 60th Annual Associated Schools of Construction International Conference, vol 5, pages 584-592.

BibTeX entry
@inproceedings{ASC2024:Comparative_Analysis_Point_Clouds,
  author    = {Busra Yucel and Junshan Liu and Shadi Alathamneh},
  title     = {A Comparative Analysis of Point Clouds Acquired from UAV Photogrammetry and UAV-based LiDAR in Built Environment},
  booktitle = {Proceedings of 60th Annual Associated Schools of Construction International Conference},
  editor    = {Tom Leathem and Wes Collins and Anthony Perrenoud},
  series    = {EPiC Series in Built Environment},
  volume    = {5},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2632-881X},
  url       = {/publications/paper/nl6h},
  doi       = {10.29007/hr8w},
  pages     = {584-592},
  year      = {2024}}
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