FreqNav：面向面向对象空中视觉语言导航的分阶段频率路由
FreqNav: Stage-Wise Frequency Routing for Object-Oriented Aerial Vision-Language Navigation
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中文总结 AI 辅助
针对现有空中VLN方法的感知干扰问题，提出FreqNav框架，通过动态分配视觉令牌实现分阶段频率路由，提升性能并加快推理速度，验证了其实际应用潜力。
中文摘要 AI 辅助
面向对象的空中视觉语言导航（VLN）要求在长视界闭环控制下搜索指定目标并精准着陆。导航过程中，感知优先级会动态变化：早期探索优先关注低频空间布局，随后转向高频目标细节。现有VLN方法用相同视觉令牌建模不同导航阶段的感知需求，导致无关物体和背景杂波产生干扰。为此，我们将长视界空中导航形式化为从空间结构到局部细节的频率偏好转变，提出轻量型频率路由自适应感知框架FreqNav。在固定计算预算下，FreqNav会根据当前导航阶段动态重新分配各频率分量的视觉令牌：频率令牌路由器从双视图观测中选择与阶段相关的视觉表示，相位相关 grounding 模块通过显式监督锚定视觉证据，扩散Transformer则预测平滑轨迹以实现连续控制。实验表明，FreqNav在优于强基线的同时，推理速度提升约3倍；实际部署进一步验证了其在长视界空中自主任务中的有效性、效率及应用潜力。
英文摘要
Object-oriented aerial vision-and-language navigation (VLN) requires searching for a described target and landing on it precisely, under long-horizon and closed-loop control. Guided by a target-descriptive instruction during navigation, perceptual priorities dynamically evolve: early-stage exploration prioritizes low-frequency spatial layout, and then shifts to high-frequency target details. Existing VLN methods model the varying perceptual requirements across navigation stages with identical visual tokens, leading to interference from irrelevant objects and background clutter. To this end, we therefore formulate long-horizon aerial navigation as a frequencypreference shift from spatial structure to local detail and propose FreqNav, a lightweight frequency-routing adaptive perception framework. Under a fixed computational budget, FreqNav dynamically reallocates visual tokens across frequency components according to the current navigation stage. A Frequency Token Router selects stage-relevant visual representations from dual-view observations, while a Phase-dependent Grounding Module anchors visual evidence through explicit supervision. A Diffusion Transformer then predicts smooth trajectories for continuous control. Experiments show that FreqNav outperforms strong baselines while achieving approximately 3x faster inference. Real-world deployment further demonstrates its effectiveness, efficiency, and practical potential for long-horizon aerial autonomy.
