Aerial cinematography is revolutionizing industries that require live and
dynamic camera viewpoints such as entertainment, sports, and security. However,
safely piloting a drone while filming a moving target in the presence of
obstacles is immensely taxing, often requiring multiple expert human operators.
Hence, there is demand for an autonomous cinematographer that can reason about
both geometry and scene context in real-time. Existing approaches do not
address all aspects of this problem; they either require high-precision
motion-capture systems or GPS tags to localize targets, rely on prior maps of
the environment, plan for short time horizons, or only follow artistic
guidelines specified before flight.
In this work, we address the problem in its entirety and propose a complete
system for real-time aerial cinematography that for the first time combines:
(1) vision-based target estimation; (2) 3D signed-distance mapping for
occlusion estimation; (3) efficient trajectory optimization for long
time-horizon camera motion; and (4) learning-based artistic shot selection. We
extensively evaluate our system both in simulation and in field experiments by
filming dynamic targets moving through unstructured environments. Our results
indicate that our system can operate reliably in the real world without
restrictive assumptions. We also provide in-depth analysis and discussions for
each module, with the hope that our design tradeoffs can generalize to other
related applications. Videos of the complete system can be found at:
this https URL

本研究提出了一个完整的系统，通过结合基于视觉的目标估计、三维有符号距离映射、高效的轨迹优化以及基于学习的艺术镜头选择，实现了实时的无人机拍摄，成功解决了多种限制性的问题。

基于学习的艺术决策的无人机在非结构化环境下的航拍摄影

Autonomous Aerial Cinematography In Unstructured Environments With  Learned Artistic Decision-Making

The use of drones for aerial cinematography has revolutionized several
applications and industries that require live and dynamic camera viewpoints
such as entertainment, sports, and security. However, safely controlling a
drone while filming a moving target usually requires multiple expert human
operators; hence the need for an autonomous cinematographer. Current approaches
have severe real-life limitations such as requiring fully scripted scenes,
high-precision motion-capture systems or GPS tags to localize targets, and
prior maps of the environment to avoid obstacles and plan for occlusion.
In this work, we overcome such limitations and propose a complete system for
aerial cinematography that combines: (1) a vision-based algorithm for target
localization; (2) a real-time incremental 3D signed-distance map algorithm for
occlusion and safety computation; and (3) a real-time camera motion planner
that optimizes smoothness, collisions, occlusions and artistic guidelines. We
evaluate robustness and real-time performance in series of field experiments
and simulations by tracking dynamic targets moving through unknown,
unstructured environments. Finally, we verify that despite removing previous
limitations, our system achieves state-of-the-art performance. Videos of the
system in action can be seen at this https URL

本文介绍了使用视觉算法进行目标定位、实时增量式 3D 签名距离地图算法进行遮挡和安全计算、实时相机运动规划器进行优化平滑度、碰撞、遮挡和艺术指导的航拍摄影完整系统，旨在克服多个专业领域影响安全控制无人机的局限，并验证该系统在追踪动态目标方面的稳健性和实时性，最终证明该系统达到了当前最佳性能。