Abstract
Unmanned aerial vehicles (UAVs) can capture high-quality aerial photos and have been widely used for large-scale urban 3D reconstruction. However, even with the help of commercial flight control software, it is still a challenging task for non-professional users to capture full-coverage aerial photos in complex urban environments, which normally leads to incomplete 3D reconstruction. In this paper, we propose a novel path planning method for the high-quality aerial 3D reconstruction of urban scenes. The proposed approach first captures aerial photos, following an initial path to generate a coarse 3D model as prior knowledge. Then, 3D viewpoints with constrained location and orientation are generated and evaluated, according to the completeness and accuracy of the corresponding visible regions of the prior model. Finally, an optimized path is produced by smoothly connecting the optimal viewpoints. We perform an extensive evaluation of our method on real and simulated data sets, in comparison with a state-of-the-art method. The experimental results indicate that the optimized trajectory generated by our method can lead to a significant boost in the performance of aerial 3D urban reconstruction.
Highlights
With the advantages of small size and affordable price, Unmanned aerial vehicles (UAVs) have been widely used in various fields, such as urban monitoring [1], intelligent transportation [2], precision agriculture [3], and industrial inspection [4]
Human factors can lead to UAV accidents in complex urban scenes
For comparison with a state-of-the-art method, we conducted our main experiments on thrual scenes (NY-1, GOTH-1, and UK-1) from the benchmark of [28], which consists of urban environments at different scale
Summary
With the advantages of small size and affordable price, UAVs have been widely used in various fields, such as urban monitoring [1], intelligent transportation [2], precision agriculture [3], and industrial inspection [4]. Camera-mounted consumer UAVs have the ability to capture aerial photos in complex urban environments [5], which can be used to reconstruct accurate 3D structures with the aid of state-of-the-art structure from motion (SfM) and multi-view stereo (MVS) algorithms [6,7,8]. UAV-based 3D scanning is typically performed either under manual control or automated navigation operation; neither can fully satisfy the requirement of achieving complete coverage for urban scenes. Human factors can lead to UAV accidents in complex urban scenes
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