论文/arXiv 2609.03483·1 个月前

基于共享鸟瞰地图的空地协同视觉语言导航

提出免训练基线AGC-VLN，通过共享鸟瞰地图协同UAV与UGV，并在CARLA-Air Town10HD闭环任务中验证。

AI 整理

• 本体 SDK 与控制
• 感知与导航
• 具身模型

原文链接：Air-Ground Collaborative Vision-and-Language Navigation via Shared Bird's-Eye Maps（arXiv 2609.03483 · 发布日期：2026-09-03）

Air-Ground Collaborative Vision-and-Language Navigation via Shared Bird's-Eye Maps

提出免训练基线AGC-VLN，通过共享鸟瞰地图协同UAV与UGV，并在CARLA-Air Town10HD闭环任务中验证。

核心要点

• 空地协同VLN仍少有探索，既有免训练方法缺乏协作机制。
• 将UAV全局视角、UGV位姿和VLM锚定目标渲染为共享鸟瞰地图。
• UGV用冻结VLM沿路规划并闭环执行，UAV用3D-SPF搜索并飞向目标。
• 100个闭环episode中联合成功率77.0%；摘要给出GitHub项目页面。

验证范围与局限：验证范围为CARLA-Air的Town10HD场景中100个闭环episode。

英文摘要 (Abstract)

Air-ground collaborative Vision-and-Language Navigation (VLN) pairs an unmanned aerial vehicle (UAV) with a global bird's-eye view and an unmanned ground vehicle (UGV) with a local first-person view, yet the setting remains largely unexplored: existing training-free methods solve single-agent tasks but offer no collaboration mechanism, and a recent CARLA-Air evaluation found no stable cooperative behavior across five state-of-the-art VLA models; naive semantic communication or bidirectional coupling even degrades performance. We establish AGC-VLN (Air-Ground Collaborative VLN), the first training-free baseline for air-ground collaborative VLN. The key insight is that training-free methods decompose navigation into VLM-based semantic reasoning and deterministic geometric execution, exposing a collaboration interface: the UAV's global view, over which it renders the UGV's reported pose and the VLM-anchored target as CAR/GOAL markers with distance labels, yielding a shared bird's-eye map. From this map, the UGV acquires global spatial context its first-person view cannot provide, plans a road-following path with a frozen VLM, and executes it under closed-loop control; in parallel, the UAV runs 3D-SPF, a spatial-search upgrade of SPF that localizes the target in the downward view and flies toward it. On 100 closed-loop episodes in CARLA-Air's Town10HD scene, AGC-VLN reaches a 77.0% joint success rate, a collaboration gain of +27.0% over the weaker individual agent (the UAV, 50.0%), and exceeds the strongest published single-agent baseline (Travel UAV, 53.0%) by 24.0 points, stemming from the complementarity of the UAV's global view and the UGV's road-following execution. Project page: https://github.com/ZSN2024/AGC-VLN.

作者：Shuning Zhang, Liang Li, Yunheng Wang, Tao Wang, Yihang Kang, Renjing Xu

来源

本页事实对应的公开页面，以原文为准。

1. arXiv 2609.03483：Air-Ground Collaborative Vision-and-Language Navigation via Shared Bird's-Eye Maps访问 2026-10-21