Drones equipped with cameras are emerging as a powerful tool for large-scale
aerial 3D scanning, but existing automatic flight planners do not exploit all
available information about the scene, and can therefore produce inaccurate and
incomplete 3D models. We present an automatic method to generate drone
trajectories, such that the imagery acquired during the flight will later
produce a high-fidelity 3D model. Our method uses a coarse estimate of the
scene geometry to plan camera trajectories that: (1) cover the scene as
thoroughly as possible; (2) encourage observations of scene geometry from a
diverse set of viewing angles; (3) avoid obstacles; and (4) respect a
user-specified flight time budget. Our method relies on a mathematical model of
scene coverage that exhibits an intuitive diminishing returns property known as
submodularity. We leverage this property extensively to design a trajectory
planning algorithm that reasons globally about the non-additive coverage reward
obtained across a trajectory, jointly with the cost of traveling between views.
We evaluate our method by using it to scan three large outdoor scenes, and we
perform a quantitative evaluation using a photorealistic video game simulator.

该论文介绍了一种利用数学模型自动生成无人机飞行轨迹，全面准确地扫描物体表面，从而生成高保真的 3D 模型。作者使用了覆盖度等数学原理，进行路径规划，可以自动权衡机器人特性和覆盖度，有效避免撞墙等情况。