发表机构
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
LightVLN提出一种轻量级空中视觉语言导航框架，通过紧凑记忆与历史引导局部聚合降低计算开销，在OpenFly和AerialVLN-S数据集上超越7B基线，并实现机载实时部署。
AI 中文摘要
空中视觉语言导航（VLN）使无人机能够在复杂的三维环境中，根据视觉观察执行长时程的自然语言指令。然而，现有的空中VLN模型通常依赖大规模的视觉-语言骨干网络和密集的视觉历史，带来了巨大的计算和内存开销，阻碍了机载部署。我们提出了LightVLN，一个轻量级的历史感知空中VLN框架，它结合了紧凑的0.5B语言骨干网络以及历史和当前观察的紧凑表示。LightVLN利用策略已计算的视觉特征，将每个历史帧压缩为单个令牌。它进一步引入历史和指令条件化的局部聚合，将当前观察从256个视觉令牌减少到32个，同时保留与导航相关的空间信息。在最多16个历史帧的情况下，策略最多使用48个观察派生令牌。在公开的OpenFly数据集上，LightVLN在Test-Seen和Test-Unseen上的成功率（SR）分别达到50.93%和36.14%，在大多数报告的指标上优于所评估的7B语言骨干基线。它还在AerialVLN-S Val-Seen上实现了25.83%的SR。在一个重建的未见校园中，我们将LightVLN部署在DJI M350 RTK上，并外接Jetson Orin NX 16 GB，进行闭环机载计算的真实到模拟硬件在环（HIL）评估，实现了14.61 Hz的模型推理和11.13 Hz的端到端决策更新。这些结果证明了LightVLN在航空导航中的有效性和效率。
英文摘要
Aerial vision-and-language navigation (VLN) enables unmanned aerial vehicles to execute long-horizon natural-language instructions from visual observations in complex three-dimensional environments. However, recent aerial VLN models often rely on large-scale vision-language backbones and dense visual histories, imposing substantial computation and memory costs that hinder onboard deployment. We propose LightVLN, a lightweight history-aware aerial VLN framework that combines a compact 0.5B language backbone with compact representations of both historical and current observations. LightVLN compresses each historical frame into a single token using visual features already computed by the policy. It further introduces history- and instruction-conditioned local aggregation to reduce the current observation from 256 to 32 visual tokens while preserving navigation-relevant spatial information. With up to 16 historical frames, the policy uses at most 48 observation-derived tokens. On the public OpenFly dataset, LightVLN achieves 50.93% Test-Seen and 36.14% Test-Unseen success rates (SR), outperforming the evaluated 7B language-backbone baselines on most reported metrics. It also achieves 25.83% SR on AerialVLN-S Val-Seen. In a reconstructed unseen campus, we deploy LightVLN on a DJI M350 RTK with an external Jetson Orin NX 16 GB for closed-loop onboard-compute real-to-sim hardware-in-the-loop (HIL) evaluation, achieving 14.61 Hz model inference and 11.13 Hz end-to-end decision updates. These results demonstrate the effectiveness and efficiency of LightVLN for aerial navigation.
Comments8 pages, 5 figures
