无需完整状态估计穿越移动门
Flying through Moving Gates without Full State Estimation
AI总结：
针对未知动态环境中的移动门穿越问题，提出仅依赖单目视线、无需地图或 VIO 的 PN 视觉控制优化方法，并通过仿真与真机验证其高速鲁棒性。
摘要：
自主无人机竞速需要强大的感知、规划与控制能力，并已成为自主敏捷飞行的基准和试验场。现有工作通常假设赛道静态且地图已知，从而能够离线规划时间最优轨迹，通过相对门的定位来降低视觉惯性里程计（VIO）在状态估计中的漂移，或针对特定赛道和运行环境训练基于学习的方法。相比之下，灾害响应或投递等许多现实任务需要在未知且动态的环境中执行。为了使无人机竞速对未见环境和移动门更具鲁棒性，我们提出一种控制算法，该算法无需赛道地图或 VIO，仅依赖对门视线方向的单目测量。为此，我们采用比例导引（PN）定律，以便在门运动或有风的情况下仍能准确穿越门。我们将无人机竞速中融合 PN 的视觉控制问题表述为一个约束优化问题，并推导出闭式最优解。通过仿真和真实世界实验，我们证明该算法能够高速穿越移动门，同时对不同的门运动、模型误差、风和延迟具有鲁棒性。
英文摘要：
Autonomous drone racing requires powerful perception, planning, and control and has become a benchmark and test field for autonomous, agile flight. Existing work usually assumes static race tracks with known maps, which enables offline planning of time-optimal trajectories, performing localization to the gates to reduce the drift in visual-inertial odometry (VIO) for state estimation or training learning-based methods for the particular race track and operating environment. In contrast, many real-world tasks like disaster response or delivery need to be performed in unknown and dynamic environments. To make drone racing more robust against unseen environments and moving gates, we propose a control algorithm that operates without a race track map or VIO, relying solely on monocular measurements of the line of sight to the gates. For this purpose, we adopt the law of proportional navigation (PN) to accurately fly through the gates despite gate motions or wind. We formulate the PN-informed vision-based control problem for drone racing as a constrained optimization problem and derive a closed-form optimal solution. Through simulations and real-world experiments, we demonstrate that our algorithm can navigate through moving gates at high speeds while being robust to different gate movements, model errors, wind, and delays.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
