Smart Structures and Systems

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Vision-based full-field panorama generation by UAV using GPS data and feature points filtering

  • Guo, Yapeng (School of Transportation Science and Engineering, Harbin Institute of Technology) ;
  • Xu, Yang (School of Civil Engineering, Harbin Institute of Technology) ;
  • Niu, Haowei (School of Transportation Science and Engineering, Harbin Institute of Technology) ;
  • Li, Zhonglong (School of Transportation Science and Engineering, Harbin Institute of Technology) ;
  • E., Yuhui (Liaoning Transportation Development Center) ;
  • Jiao, Xinghua (Liaoning Transportation Development Center) ;
  • Li, Shunlong (School of Transportation Science and Engineering, Harbin Institute of Technology)
  • Received : 2019.09.19
  • Accepted : 2019.12.05
  • Published : 2020.05.25
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Abstract

To meet the urgent requirements of safety surveillance from civil engineering management authorities, this study proposes a refined and efficient approach to generate full-field high-resolution panorama of construction sites using camera-amounted UAV (Unmanned Aerial Vehicle). GPS (Global Position System) information extraction for pre-registration, feature points filtering for efficient registration and optimal seaming line seeking for fusion are performed in sequence to form the full-field panorama generation framework. Advantages of the proposed method are as follows. First, GPS information can sort images for pre-registration, avoiding inefficient repeated pairwise calculations and matching. Second, the feature points are filtered according to the characteristics of the construction site images to reduce the amount of calculation. The proposed framework is validated on a road construction site and results demonstrate that it can generate an accurate and high-quality full-site panorama for the safety supervision in a much efficient manner.

Keywords

Acknowledgement

The research described in this paper was financially supported by National Key Rand D Program of China [2018YFB1600202, 2018YFC0705606], NSFC [Grant No. 51678204, 51638007, 51922034] and Guangxi Science Base and Talent Program [Grand No. 710281886032].

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