Aerial vehicles are revolutionizing applications that require capturing the 3D structure of dynamic targets in the wild, such as sports, medicine, and entertainment. The core challenges in developing a motion-capture system that operates in outdoors environments are: (1) 3D inference requires multiple simultaneous viewpoints of the target, (2) occlusion caused by obstacles is frequent when tracking moving targets, and (3) the camera and vehicle state estimation is noisy. We present a real-time aerial system for multi-camera control that can reconstruct human motions in natural environments without the use of special-purpose markers. We develop a multi-robot coordination scheme that maintains the optimal flight formation for target reconstruction quality amongst obstacles. We provide studies evaluating system performance in simulation, and validate real-world performance using two drones while a target performs activities such as jogging and playing soccer. Supplementary video: https://youtu.be/jxt91vx0cns
翻译:航空飞行器正在使各种应用发生革命,这些应用需要捕捉野生动态目标的3D结构,如体育、医药和娱乐。在开发户外环境中运行的运动抓捕系统方面的核心挑战是:(1) 3D推论要求目标的多重同步观点,(2) 障碍造成的隔绝在跟踪移动目标时经常发生,(3) 摄影机和车辆状态估计是吵闹的。我们提供了一个实时多摄像头控制系统,可以在不使用特殊目的标记的情况下在自然环境中重建人类运动。我们开发了一个多机器人协调计划,在障碍之间维持目标重建质量的最佳飞行结构。我们提供研究,评估模拟中的系统性能,并使用两架无人驾驶飞机验证真实世界的性能,而目标则进行诸如跑步和踢足球等活动。补充视频:https://youtu.be/jxt91vx0cns。