Automated Aerial Triangulation for UAV-Based Mapping

• Published in: Remote sensing (Basel, Switzerland) Vol. 10; no. 12; p. 1952
• Main Authors: He, Fangning, Zhou, Tian, Xiong, Weifeng, Hasheminnasab, Seyyed, Habib, Ayman
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.12.2018; MDPI
• Subjects: 3D reconstruction; Aerial triangulation; Applications programs; Automation; Bundle adjustment; Comparative analysis; Computer programs; Coordinates; Digital cameras; exterior orientation parameters; Image acquisition; Image processing; Image reconstruction; International conferences; Mapping; Mathematical models; OTHER INSTRUMENTATION; Parameter estimation; Pattern recognition; Photogrammetry; Recovery; Registration; relative orientation; Remote Sensing; Remote sensing systems; Software; structure from motion; Three dimensional models; unmanned aerial vehicle; Unmanned aerial vehicles; United States > US; Germany
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs10121952

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.