用真实硬件模拟海上环境，验证无人机自主飞行的视觉定位系统。
Hardware- and Vision-in-the-Loop Validation of Deep Monocular Pose Estimation for Autonomous Maritime UAV Flight
- 用深度变换器模型在机上处理渲染的海面图像，实现单目位姿估计。
- 通过延迟卡尔曼滤波融合延迟视觉与高频惯性数据，稳定控制飞行。
- 首次在真实硬件上复现海上飞行关键约束，适合开发前测试。
船舶自主无人机作业需要可靠的基于视觉的相对位姿估计，但海上验证成本高、受天气影响且存在风险。本文提出一种硬件验证的视觉闭环框架，可在室内实现完全自主飞行，同时模拟逼真的海景环境。渲染的海面视图由基于深度变换器的单目位姿估计算法在机上处理，延迟的视觉测量与高频惯性测量单元（IMU）数据通过延迟卡尔曼滤波融合，为几何控制提供一致的状态估计。该系统捕捉了感知延迟、异步更新和计算资源限制等嵌入式关键效应，这些在纯仿真中无法体现。自主起飞、轨迹跟踪与着陆实验均实现稳定闭环飞行。结果表明，该系统为船载部署前提供了安全且硬件真实的中间验证阶段。
原文摘要 · Abstract (English)
Autonomous UAV operations on ships require reliable vision-based relative pose estimation, yet at-sea validation is costly, weather-dependent, and risky. This paper presents a hardware-validated vision-in-the-loop framework that enables fully autonomous indoor flight while emulating photorealistic maritime environments. Rendered maritime views are processed onboard by a deep transformer-based monocular pose estimator. Delayed vision measurements are fused with high-rate IMU data using a delayed Kalman filter to provide consistent state estimates for geometric control. The system captures critical embedded effects, including perception latency, asynchronous updates, and computational constraints, that are absent in pure simulation. Autonomous takeoff, trajectory tracking, and landing experiments demonstrate stable closed-loop flight. The results establish a safe and hardware-realistic intermediate stage for developing maritime UAV autonomy prior to shipboard deployment.
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