Automatic and Semantically-Aware 3D UAV Flight Planning for Image-Based 3D Reconstruction
Automatic and Semantically-Aware 3D UAV Flight Planning for Image-Based 3D Reconstruction
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DOI：
10.3390/rs11131550
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发表时间：
2019-07-01
期刊：
影响因子：
5
通讯作者：
Fraundorfer, Friedrich
中科院分区：
文献类型：
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作者：
Koch, Tobias;Koerner, Marco;Fraundorfer, Friedrich
小型无人机（UAV）因其高机动性而成为理想的图像采集平台，即使在复杂和紧凑的环境中也是如此。所获取的图像可以用于使用当前的多视图立体方法来生成高质量的3D模型。然而，生成的3D模型的质量在很大程度上取决于之前的飞行计划，这仍然需要人类的专业知识，特别是在复杂的城市和危险的环境中。在安全飞行计划方面，实际考虑往往界定禁止和限制飞行器进入的空域。我们提出了一个3D无人机路径规划框架，旨在详细和完整的小规模的3D重建考虑到环境的语义属性，允许用户指定的限制空域。生成的轨迹占所需的模型分辨率和成功的摄影测量重建的要求。我们利用语义从初始飞行中提取目标对象，并定义在路径规划过程中必须避免的限制和禁止空域，以确保安全和短的无人机路径，同时仍然旨在最大限度地提高对象重建质量。将路径规划问题转化为定向运动问题，并利用子模块化和摄影测量相关的几何学进行离散优化。定制的合成场景和户外实验的方法的评估表明，我们的方法的真实世界的能力，提供可行的，短的和安全的飞行计划，生成详细的三维重建模型。
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.