海事UAV自律飛行のための深層単眼姿勢推定のハードウェア・ビジョン・イン・ザ・ループ検証
Hardware- and Vision-in-the-Loop Validation of Deep Monocular Pose Estimation for Autonomous Maritime UAV Flight
船舶上でのUAV自律飛行に必要な単眼姿勢推定を、フォトリアルな海事環境を模擬した屋内実験でハードウェア検証する枠組みを提案した。
詳しい要約
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2. 先行研究と比べてどこがすごい？
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著者: Maneesha Wickramasuriya, Beomyeol Yu, Jaden Shin, Mason Huslig, Taeyoung Lee, Murray Snyder
分類: cs.RO, cs.AI, cs.SY, eess.SY
原文アブストラクト
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.
