RiverVLN：面向无人水面艇的相位接地时间视觉-语言导航
RiverVLN: Phase-Grounded Temporal Vision--Language Navigation for Unmanned Surface Vehicles
AI总结：
针对无人水面艇在连续河流运动中的长时程视觉-语言导航，提出RiverVLN基准和PGT-NAV框架，通过相位接地将指令转为可验证语义序列，显著降低漂移并实现0.79平均成功率。
AI中文摘要：
视觉-语言导航（VLN）主要面向室内和地面机器人开发，在这些场景中，语言通常可被视为静态目标，运动则通过离散或近乎瞬时的动作来近似。这些假设在无人水面艇（USV）场景下不再成立：河流导航需要在惯性和有限机动性下进行连续运动，而长时程指令必须通过稀疏且视觉上模糊的海洋地标来执行。我们提出了RiverVLN，据我们所知，这是首个专为连续河流运动下的长时程USV VLN设计的基准，以及PGT-NAV，一种面向USV的相位接地时间导航框架。PGT-NAV并非直接将整个指令映射为运动，而是将其转换为有序的、视觉可验证的语义相位序列，并通过接地的视觉和运动证据在线维护当前激活相位。这一显式的语义进度状态与视觉-运动历史及相位特定接地相融合，以预测六个局部SE(2)位姿增量。生成的轨迹在预测-执行-重新观测循环中执行，其中艇体向W3执行，更新相位和接地，并通过基于地图的安全层进行重新规划。实验表明，与GNM风格和ViNT风格的基线相比，PGT-NAV显著减少了递归位置和航向漂移，并在Unity-ROS闭环导航中实现了0.79的平均成功率。未见过的桥洞开启试验和真实世界USV实验进一步证明，相位接地表示可从受控评估迁移到物理USV部署中。
英文摘要：
Vision-language navigation (VLN) has largely been developed for indoor and terrestrial robots, where language can often be treated as a static goal and motion is approximated by discrete or near-instantaneous actions. These assumptions break down for unmanned surface vehicles (USVs): river navigation requires continuous motion under inertia and limited maneuverability, while long-horizon instructions must be executed through sparse and visually ambiguous maritime landmarks. We introduce RiverVLN, to our knowledge the first benchmark designed for long-horizon USV VLN under continuous riverine motion, and PGT-NAV, a phase-grounded temporal navigation framework for USVs. Rather than directly mapping an entire instruction to motion, PGT-NAV converts it into an ordered sequence of visually verifiable semantic phases and maintains the active phase online through grounded visual and motion evidence. This explicit semantic progress state is fused with visual-motion history and phase-specific grounding to predict six local SE(2) pose increments. The resulting trajectory is executed in a predict-execute-re-observe loop, where the vessel executes toward W3, updates phase and grounding, and replans through a map-based safety layer. Experiments show that PGT-NAV substantially reduces recursive position and heading drift relative to GNM-style and ViNT-style baselines and achieves an average success rate of 0.79 in Unity-ROS closed-loop navigation. Unseen bridge-opening trials and real-world USV experiments further demonstrate that the phase-grounded representation transfers from controlled evaluation to physical USV deployment.
