AirGroundVLN: A Large-Scale Benchmark for Goal-Oriented Air-Ground Collaborative Vision-and-Language Navigation

2026-10-07

AI Summary

This study addresses the absence of benchmarks, inconsistent cross-platform spatial context, and low single-platform planning reliability in air-ground collaborative vision-language navigation. To tackle these challenges, we propose the AG-CoNAV framework and construct AirGroundVLN, a large-scale benchmark comprising tens of thousands of scenarios. Methodologically, we design a Spatiotemporal Anchored Collaborative Memory (SACM) to maintain cross-view consistency and introduce an Aerial-Guided Region-to-Local hierarchical Planning (AGRLP) strategy that integrates broad aerial guidance with fine-grained ground-level localization. Extensive experiments validate the effectiveness of the proposed framework, demonstrating significant improvements in navigation success rates. Furthermore, this work establishes a comprehensive benchmark for advancing research in air-ground collaborative navigation.

Abstract

Goal-oriented Vision-and-Language Navigation (VLN) requires agents to locate and reach targets described in natural language without prescribed routes. Air--ground collaboration is valuable for tasks requiring both wide-area search and fine-grained localization. However, systematic study of goal-oriented air--ground collaborative VLN remains limited by the lack of large-scale, diverse benchmarks and two core challenges: 1) substantial differences between aerial and ground views, together with useful observations becoming unavailable as navigation proceeds, make it difficult to maintain spatially consistent context across platforms and over time; and 2) asymmetric spatial observability makes ground perception locally detailed but spatially limited and aerial perception broad but locally coarse, limiting the reliability of single-platform planning. To address these limitations, we introduce AirGroundVLN, a benchmark containing 10,281 navigation episodes and 955 target instances across 19 Unreal Engine environments, with seen/unseen splits and an aerial-visibility protocol for systematic evaluation. Alongside the benchmark, we propose AG-CoNAV, a trainable reference framework comprising two key components: Spatiotemporally Anchored Collaborative Memory (SACM) and Aerial-Guided Regional-to-Local Planning (AGRLP). SACM maintains and retrieves spatially consistent historical context across aerial and ground observations. Meanwhile, AGRLP combines regional aerial guidance with fine-grained ground navigation. Extensive experiments demonstrate the effectiveness of AG-CoNAV and establish AirGroundVLN as a comprehensive benchmark for future exploration.

Problem

• Vision-and-Language Navigation
• Air-Ground Collaboration
• Goal-Oriented Navigation
• Benchmark
• Spatial Observability

Innovation

• Air-Ground Collaborative VLN
• Spatiotemporally Anchored Collaborative Memory
• Aerial-Guided Regional-to-Local Planning
• Goal-Oriented Navigation
• Benchmark