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Title: Automated Aerial Triangulation for UAV-Based Mapping

Abstract

Accurate 3D reconstruction/modelling from unmanned aerial vehicle (UAV)-based imagery has become the key prerequisite in various applications. Although current commercial software has automated the process of image-based reconstruction, a transparent system, which can be incorporated with different user-defined constraints, is still preferred by the photogrammetric research community. In this regard, this paper presents a transparent framework for the automated aerial triangulation of UAV images. The proposed framework is conducted in three steps. In the first step, two approaches, which take advantage of prior information regarding the flight trajectory, are implemented for reliable relative orientation recovery. Then, initial recovery of image exterior orientation parameters (EOPs) is achieved through either an incremental or global approach. Finally, a global bundle adjustment involving Ground Control Points (GCPs) and check points is carried out to refine all estimated parameters in the defined mapping coordinate system. Four real image datasets, which are acquired by two different UAV platforms, have been utilized to evaluate the feasibility of the proposed framework. In addition, a comparative analysis between the proposed framework and the existing commercial software is performed. The derived experimental results demonstrate the superior performance of the proposed framework in providing an accurate 3D model, especially when dealingmore » with acquired UAV images containing repetitive pattern and significant image distortions.« less

Authors:
ORCiD logo [1];  [1];  [1];  [1];  [1]
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  1. Purdue Univ., West Lafayette, IN (United States)
Publication Date:
Research Org.:
Purdue Univ., West Lafayette, IN (United States)
Sponsoring Org.:
USDOE Advanced Research Projects Agency - Energy (ARPA-E)
OSTI Identifier:
1613657
Grant/Contract Number:  
AR0000593
Resource Type:
Accepted Manuscript
Journal Name:
Remote Sensing
Additional Journal Information:
Journal Volume: 10; Journal Issue: 12; Journal ID: ISSN 2072-4292
Publisher:
MDPI
Country of Publication:
United States
Language:
English
Subject:
47 OTHER INSTRUMENTATION; Remote Sensing; unmanned aerial vehicle; 3D reconstruction; structure from motion; relative orientation; exterior orientation parameters; bundle adjustment

Citation Formats

He, Fangning, Zhou, Tian, Xiong, Weifeng, Hasheminnasab, Seyyed, and Habib, Ayman. Automated Aerial Triangulation for UAV-Based Mapping. United States: N. p., 2018. Web. doi:10.3390/rs10121952.
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He, Fangning, Zhou, Tian, Xiong, Weifeng, Hasheminnasab, Seyyed, & Habib, Ayman. Automated Aerial Triangulation for UAV-Based Mapping. United States. https://doi.org/10.3390/rs10121952
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He, Fangning, Zhou, Tian, Xiong, Weifeng, Hasheminnasab, Seyyed, and Habib, Ayman. Tue . "Automated Aerial Triangulation for UAV-Based Mapping". United States. https://doi.org/10.3390/rs10121952. https://www.osti.gov/servlets/purl/1613657.
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@article{osti_1613657,
title = {Automated Aerial Triangulation for UAV-Based Mapping},
author = {He, Fangning and Zhou, Tian and Xiong, Weifeng and Hasheminnasab, Seyyed and Habib, Ayman},
abstractNote = {Accurate 3D reconstruction/modelling from unmanned aerial vehicle (UAV)-based imagery has become the key prerequisite in various applications. Although current commercial software has automated the process of image-based reconstruction, a transparent system, which can be incorporated with different user-defined constraints, is still preferred by the photogrammetric research community. In this regard, this paper presents a transparent framework for the automated aerial triangulation of UAV images. The proposed framework is conducted in three steps. In the first step, two approaches, which take advantage of prior information regarding the flight trajectory, are implemented for reliable relative orientation recovery. Then, initial recovery of image exterior orientation parameters (EOPs) is achieved through either an incremental or global approach. Finally, a global bundle adjustment involving Ground Control Points (GCPs) and check points is carried out to refine all estimated parameters in the defined mapping coordinate system. Four real image datasets, which are acquired by two different UAV platforms, have been utilized to evaluate the feasibility of the proposed framework. In addition, a comparative analysis between the proposed framework and the existing commercial software is performed. The derived experimental results demonstrate the superior performance of the proposed framework in providing an accurate 3D model, especially when dealing with acquired UAV images containing repetitive pattern and significant image distortions.},
doi = {10.3390/rs10121952},
journal = {Remote Sensing},
number = 12,
volume = 10,
place = {United States},
year = {Tue Dec 04 00:00:00 EST 2018},
month = {Tue Dec 04 00:00:00 EST 2018}
}
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https://doi.org/10.3390/rs10121952
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Cited by: 36 works
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