透过像素运动观察：利用单摄像头光流学习避障
Seeing Through Pixel Motion: Learning Obstacle Avoidance from Optical Flow with One Camera
AI总结：
提出一种基于单目光流的端到端四旋翼避障系统，利用可微分模拟器、中心流注意力和主动感知策略，在仿真和真实环境中实现高达6米/秒的敏捷鲁棒飞行。
摘要：
光流捕捉图像序列中像素随时间推移的运动，提供关于运动、深度和环境结构的信息。飞行昆虫利用这一信息进行导航和避障，使它们即使在复杂环境中也能执行高度敏捷的动作。尽管具有潜力，自主飞行机器人尚未充分利用这种运动信息来达到同等水平的敏捷性和鲁棒性。从光流进行控制的挑战包括：在高速下提取准确的光流、处理噪声估计，以及确保在复杂环境中的鲁棒性能。为了应对这些挑战，我们提出了一种新颖的端到端系统，用于使用单目光流的四旋翼避障。我们开发了一个高效的可微分模拟器，并结合简化的四旋翼模型，使我们的策略能够直接通过一阶梯度优化进行训练。此外，我们引入了一种中心流注意力机制和一种动作引导的主动感知策略，以增强策略对任务相关光流观测的关注，从而在飞行过程中实现更灵敏的决策。我们的系统在仿真和真实世界中均使用FPV竞速无人机进行了验证。尽管在仿真中的简单环境中训练，我们的系统在真实世界中各种未知、杂乱的环境中展示了敏捷且鲁棒的飞行，速度高达6米/秒。
英文摘要：
Optical flow captures the motion of pixels in an image sequence over time, providing information about movement, depth, and environmental structure. Flying insects utilize this information to navigate and avoid obstacles, allowing them to execute highly agile maneuvers even in complex environments. Despite its potential, autonomous flying robots have yet to fully leverage this motion information to achieve comparable levels of agility and robustness. Challenges of control from optical flow include extracting accurate optical flow at high speeds, handling noisy estimation, and ensuring robust performance in complex environments. To address these challenges, we propose a novel end-to-end system for quadrotor obstacle avoidance using monocular optical flow. We develop an efficient differentiable simulator coupled with a simplified quadrotor model, allowing our policy to be trained directly through first-order gradient optimization. Additionally, we introduce a central flow attention mechanism and an action-guided active sensing strategy that enhances the policy's focus on task-relevant optical flow observations to enable more responsive decision-making during flight. Our system is validated both in simulation and the real world using an FPV racing drone. Despite being trained in a simple environment in simulation, our system is validated both in simulation and the real world using an FPV racing drone. Despite being trained in a simple environment in simulation, our system demonstrates agile and robust flight in various unknown, cluttered environments in the real world at speeds of up to 6m/s.
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。
