用语言指令控制无人机，结合地图与扩散模型实现远距离自主导航
KiteRunner: Language-Driven Cooperative Local-Global Navigation Policy with UAV Mapping in Outdoor Environments
- 通过无人机航拍图构建全局可通行概率图，指导局部路径生成
- 在有结构和无结构环境中路径效率分别提升5.6%和12.8%
- 适合需要语言指令控制、长距离自主导航的户外应用
开放世界户外环境中的自主导航面临动态条件、长距离空间推理和语义理解的挑战。传统方法难以兼顾局部规划、全局规划与语义任务执行，而现有大语言模型（LLMs）虽增强语义理解，但缺乏空间推理能力；扩散模型擅长局部优化，却难以支撑大规模远距离导航。为此，本文提出KiteRunner，一种语言驱动的协同式局部-全局导航策略，结合无人机正射影像进行全局规划与扩散模型驱动的局部路径生成，实现开放世界场景下的长距离导航。该方法创新性地利用实时无人机正射影像构建全局概率图，为局部规划提供可通行性引导，并集成CLIP与GPT等大模型以解析自然语言指令。实验表明，KiteRunner在结构化与非结构化环境中路径效率分别较最先进方法提升5.6%和12.8%，显著降低人工干预次数与执行时间。
原文摘要 · Abstract (English)
Autonomous navigation in open-world outdoor environments faces challenges in integrating dynamic conditions, long-distance spatial reasoning, and semantic understanding. Traditional methods struggle to balance local planning, global planning, and semantic task execution, while existing large language models (LLMs) enhance semantic comprehension but lack spatial reasoning capabilities. Although diffusion models excel in local optimization, they fall short in large-scale long-distance navigation. To address these gaps, this paper proposes KiteRunner, a language-driven cooperative local-global navigation strategy that combines UAV orthophoto-based global planning with diffusion model-driven local path generation for long-distance navigation in open-world scenarios. Our method innovatively leverages real-time UAV orthophotography to construct a global probability map, providing traversability guidance for the local planner, while integrating large models like CLIP and GPT to interpret natural language instructions. Experiments demonstrate that KiteRunner achieves 5.6% and 12.8% improvements in path efficiency over state-of-the-art methods in structured and unstructured environments, respectively, with significant reductions in human interventions and execution time.
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