並進オプティカルフローと不確実性マスクによる自律FPV飛行
Autonomous FPV Flight with Translational Optical Flow and Uncertainty Mask
単眼カメラのみを用いた自律FPVドローン飛行において、オプティカルフローを並進成分と回転成分に分解し、並進フローと不確実性マスクを利用して障害物を検出し、強化学習で制御ポリシーを学習することで、実環境で最高11.79 m/sの高速飛行を実現した。
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著者: Yang Deng, Yu Hu, Feng Yu, Linzuo Zhang, Danping Zou
分類: cs.RO
原文アブストラクト
Autonomous FPV quadrotor flight in complex environments using a monocular RGB camera as the sole exteroceptive sensor remains a fundamental challenge. Recent research has shown that using optical flow as the input of a neural network can achieve end-to-end autonomous flight in cluttered scenes. However, extracting the most relevant information from the flow estimation is the key bottleneck limiting agility and robustness. Existing methods struggle to disentangle obstacle-induced optical flow from the ego-motion background flow and suffer from low signal-to-noise ratios near the focus of expansion (FoE). To address these issues, we decompose the optical flow into translational and rotational components and utilize only the translational flow, which captures scene geometry and depth cues. In addition, we introduce an uncertainty mask derived from inconsistencies between forward and backward flow estimates. This mask highlights obstacle structures, including those within the FoE region. Both cues are fed to a control policy trained in a differentiable simulation framework, which enables efficient first-order optimization across perception and control. We validate our approach through extensive experiments in both simulated and real-world forest environments. The proposed system achieves robust flight at speeds of up to 13.91 m/s in simulation and 11.79 m/s in real-world tests, with a 93.3\% success rate over 30 real-world trials, nearly doubling the previously reported 6 m/s real-world speed of the monocular-RGB optical-flow UAV obstacle avoidance system.
