Automated UAV path-planning for high-quality photogrammetric 3D bridge reconstruction

被引:22
作者
Wang, Feng [1 ,2 ]
Zou, Yang [1 ,2 ]
Castillo, Enrique del Rey [2 ]
Ding, Youliang [1 ,3 ]
Xu, Zhao [1 ,3 ]
Zhao, Hanwei [1 ,3 ]
Lim, James B. P. [2 ]
机构
[1] Southeast Univ, Key Lab Concrete Prestressed Concrete Struct, Minist Educ, Nanjing, Peoples R China
[2] Univ Auckland, Dept Civil & Environm Engn, Auckland, New Zealand
[3] Southeast Univ, Dept Civil Engn, Nanjing, Peoples R China
基金
国家重点研发计划;
关键词
Bridge Inspection; path planning; photogrammetric 3D reconstruction; UAV; INSPECTION; SYSTEM;
D O I
10.1080/15732479.2022.2152840
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
The bridge models reconstructed from unmanned aerial vehicle (UAV) images via photogrammetry are often reported to have quality issues (e.g., high noise, insufficient resolution and precision loss) and thus restrict their application in bridge inspection. To address this problem, this paper proposes a novel Building Information Model (BIM)-based 3D path planning method for improving the quality of photogrammetric bridge models by optimising the UAV flight plan. This method firstly uses a simple BIM model as input and considers inspection and photogrammetry requirements to generate effective UAV viewpoints. Then it adjusts inaccessible UAV viewpoints that are in occupied space or cannot meet the flight safety rules. Finally, a feasible flight trajectory is generated through all valid viewpoints. To evaluate the performance of the proposed method, two prototypes were developed to automate the path planning and on-site image acquisition, respectively, and were tested on a real girder bridge. The results showed that, compared with the common UAV flight plan used in current practice, the proposed method could: (1) generate a more precise bridge model with fewer noise points and higher visual quality to support damage detection; and (2) significantly improve the efficiency of photogrammetric 3D bridge reconstruction with reduced human interventions in image collection and processing.
引用
收藏
页码:1595 / 1614
页数:20
相关论文
共 56 条
[1]   Analysis of edge-detection techniques for crack identification in bridges [J].
Abdel-Qader, L ;
Abudayyeh, O ;
Kelly, ME .
JOURNAL OF COMPUTING IN CIVIL ENGINEERING, 2003, 17 (04) :255-263
[2]   UAV PHOTOGRAMMETRY WITH OBLIQUE IMAGES: FIRST ANALYSIS ON DATA ACQUISITION AND PROCESSING [J].
Aicardi, I. ;
Chiabrando, F. ;
Grasso, N. ;
Lingua, A. M. ;
Noardo, F. ;
Spano, A. .
XXIII ISPRS CONGRESS, COMMISSION I, 2016, 41 (B1) :835-842
[3]   Real-time multiple damage mapping using autonomous UAV and deep faster region-based neural networks for GPS-denied structures [J].
Ali, Rahmat ;
Kang, Dongho ;
Suh, Gahyun ;
Cha, Young-Jin .
AUTOMATION IN CONSTRUCTION, 2021, 130
[4]  
[Anonymous], 1985, P 1 INT C GENETIC
[5]   A METHOD FOR REGISTRATION OF 3-D SHAPES [J].
BESL, PJ ;
MCKAY, ND .
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 1992, 14 (02) :239-256
[6]   Vision and Deep Learning-Based Algorithms to Detect and Quantify Cracks on Concrete Surfaces from UAV Videos [J].
Bhowmick, Sutanu ;
Nagarajaiah, Satish ;
Veeraraghavan, Ashok .
SENSORS, 2020, 20 (21) :1-19
[7]   Receding horizon path planning for 3D exploration and surface inspection [J].
Bircher, Andreas ;
Kamel, Mina ;
Alexis, Kostas ;
Oleynikova, Helen ;
Siegwart, Roland .
AUTONOMOUS ROBOTS, 2018, 42 (02) :291-306
[8]  
Bircher A, 2015, IEEE INT CONF ROBOT, P6423, DOI 10.1109/ICRA.2015.7140101
[9]   ORB-SLAM3: An Accurate Open-Source Library for Visual, Visual-Inertial, and Multimap SLAM [J].
Campos, Carlos ;
Elvira, Richard ;
Gomez Rodriguez, Juan J. ;
Montiel, Jose M. M. ;
Tardos, Juan D. .
IEEE TRANSACTIONS ON ROBOTICS, 2021, 37 (06) :1874-1890
[10]   On the accuracy of UAV photogrammetric survey for the evaluation of historic masonry structural damages [J].
Cavalagli, Nicola ;
Gioffre, Massimiliano ;
Grassi, Silvia ;
Gusella, Vittorio ;
Pepi, Chiara ;
Volpi, Gian Marco .
ART COLLECTIONS 2020, SAFETY ISSUE (ARCO 2020, SAFETY), 2020, 29 :165-174