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arXiv 2512.22010cs.CVcs.AI
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LongFly: 长航程无人机视觉与语言导航中的时空上下文整合

LongFly: Long-Horizon UAV Vision-and-Language Navigation with Spatiotemporal Context Integration

School of Mechanical Engineering, Beijing Institute of Technology（北京理工大学机械工程学院）；Chongqing Innovation Center, Beijing Institute of Technology（北京理工大学重庆创新中心）；Department of Computer Science and Technology, Tsinghua University（清华大学计算机科学与技术系）；Department of Automation, Tsinghua University（清华大学自动化系）；School of Software, Dalian University of Technology（大连理工大学软件学院）；School of Artificial Intelligence, Xidian University（西安电子科技大学人工智能学院）

Wen Jiang, Li Wang, Kangyao Huang, Wei Fan, Jinyuan Liu, Shaoyu Liu, Hongwei Duan, Bin Xu, Xiangyang Ji

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AI总结：

LongFly通过整合时空上下文提升无人机长航程视觉与语言导航的准确性和稳定性。

摘要：

无人机（UAVs）是灾后搜救中至关重要的工具，面临高信息密度、视角快速变化和动态结构等挑战，尤其是在长航程导航中。然而，当前的无人机视觉与语言导航（VLN）方法难以在复杂环境中建模长航程的时空上下文，导致语义对齐不准确和路径规划不稳定。为此，我们提出了LongFly，一种用于长航程无人机VLN的时空上下文建模框架。LongFly提出了一种历史感知的时空建模策略，将碎片化和冗余的历史数据转换为结构化、紧凑且富有表现力的表示。首先，我们提出了基于槽位的历史图像压缩模块，动态地将多视角的历史观测压缩为固定长度的上下文表示。然后，引入了时空轨迹编码模块，以捕捉无人机轨迹的时序动态和空间结构。最后，为了整合现有时空上下文与当前观测，我们设计了提示引导的多模态整合模块，以支持基于时间的推理和鲁棒的航点预测。实验结果表明，LongFly在成功率上比最先进的无人机VLN基线高出7.89%，在考虑路径长度加权的成功率上高出6.33%，在可见和未见环境中均保持一致。

英文摘要：

Unmanned aerial vehicles (UAVs) are crucial tools for post-disaster search and rescue, facing challenges such as high information density, rapid changes in viewpoint, and dynamic structures, especially in long-horizon navigation. However, current UAV vision-and-language navigation(VLN) methods struggle to model long-horizon spatiotemporal context in complex environments, resulting in inaccurate semantic alignment and unstable path planning. To this end, we propose LongFly, a spatiotemporal context modeling framework for long-horizon UAV VLN. LongFly proposes a history-aware spatiotemporal modeling strategy that transforms fragmented and redundant historical data into structured, compact, and expressive representations. First, we propose the slot-based historical image compression module, which dynamically distills multi-view historical observations into fixed-length contextual representations. Then, the spatiotemporal trajectory encoding module is introduced to capture the temporal dynamics and spatial structure of UAV trajectories. Finally, to integrate existing spatiotemporal context with current observations, we design the prompt-guided multimodal integration module to support time-based reasoning and robust waypoint prediction. Experimental results demonstrate that LongFly outperforms state-of-the-art UAV VLN baselines by 7.89\% in success rate and 6.33\% in success weighted by path length, consistently across both seen and unseen environments.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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