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Open AccessArticle

Automatic and Semantically-Aware 3D UAV Flight Planning for Image-Based 3D Reconstruction

1
Chair of Remote Sensing Technology, Technical University of Munich, 80333 Munich, Germany
2
Institute for Computer Graphics and Vision, Graz University of Technology, 8010 Graz, Austria
3
Remote Sensing Technology Institute, German Aerospace Center, 82234 Wessling, Germany
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(13), 1550; https://doi.org/10.3390/rs11131550
Received: 29 May 2019 / Revised: 20 June 2019 / Accepted: 24 June 2019 / Published: 29 June 2019
(This article belongs to the Section Urban Remote Sensing)
Small-scaled unmanned aerial vehicles (UAVs) emerge as ideal image acquisition platforms due to their high maneuverability even in complex and tightly built environments. The acquired images can be utilized to generate high-quality 3D models using current multi-view stereo approaches. However, the quality of the resulting 3D model highly depends on the preceding flight plan which still requires human expert knowledge, especially in complex urban and hazardous environments. In terms of safe flight plans, practical considerations often define prohibited and restricted airspaces to be accessed with the vehicle. We propose a 3D UAV path planning framework designed for detailed and complete small-scaled 3D reconstructions considering the semantic properties of the environment allowing for user-specified restrictions on the airspace. The generated trajectories account for the desired model resolution and the demands on a successful photogrammetric reconstruction. We exploit semantics from an initial flight to extract the target object and to define restricted and prohibited airspaces which have to be avoided during the path planning process to ensure a safe and short UAV path, while still aiming to maximize the object reconstruction quality. The path planning problem is formulated as an orienteering problem and solved via discrete optimization exploiting submodularity and photogrammetrical relevant heuristics. An evaluation of our method on a customized synthetic scene and on outdoor experiments suggests the real-world capability of our methodology by providing feasible, short and safe flight plans for the generation of detailed 3D reconstruction models. View Full-Text
Keywords: UAV; trajectory optimization; path planning; discrete optimization; 3D reconstruction; semantics; urban mapping UAV; trajectory optimization; path planning; discrete optimization; 3D reconstruction; semantics; urban mapping
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MDPI and ACS Style

Koch, T.; Körner, M.; Fraundorfer, F. Automatic and Semantically-Aware 3D UAV Flight Planning for Image-Based 3D Reconstruction. Remote Sens. 2019, 11, 1550.

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