Abstract
The use of drones for aerial cinematography has revolutionized several applications and industries requiring 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 scripted scenes that can be solved offline, 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 visual pose estimation 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 still matches state-of-the-art performance. Videos of the system in action can be seen at https://youtu.be/ZE9MnCVmumc
Abstract (translated)
无人机在航空摄影中的应用已经彻底改变了一些需要实时和动态摄像机视角的应用和行业，如娱乐、体育和安全。然而，在拍摄移动目标的同时安全地控制无人机通常需要多个专业的人工操作人员；因此需要一个自主摄影师。当前的方法具有严重的现实限制，例如需要可以离线解决的脚本场景、高精度运动捕获系统或GPS标记来定位目标，以及以前的环境地图以避免障碍并计划遮挡。
URL
https://arxiv.org/abs/1904.02319
