ViSA-Enhanced Aerial VLN: A Visual-Spatial Reasoning Enhanced Framework for Aerial Vision-Language Navigation

• URL: http://arxiv.org/abs/2603.08007v1
• Date: Mon, 09 Mar 2026 06:29:17 GMT
• Title: ViSA-Enhanced Aerial VLN: A Visual-Spatial Reasoning Enhanced Framework for Aerial Vision-Language Navigation
• Abstract summary: We propose a Visual-Spatial Reasoning (ViSA) framework for aerial Vision-Language Navigation (VLN)<n>The ViSA-enhanced VLN achieves a 70.3% improvement in success rate compared to the fully trained state-of-the-art (SOTA) method.
• Score: 7.101576681484734
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Existing aerial Vision-Language Navigation (VLN) methods predominantly adopt a detection-and-planning pipeline, which converts open-vocabulary detections into discrete textual scene graphs. These approaches are plagued by inadequate spatial reasoning capabilities and inherent linguistic ambiguities. To address these bottlenecks, we propose a Visual-Spatial Reasoning (ViSA) enhanced framework for aerial VLN. Specifically, a triple-phase collaborative architecture is designed to leverage structured visual prompting, enabling Vision-Language Models (VLMs) to perform direct reasoning on image planes without the need for additional training or complex intermediate representations. Comprehensive evaluations on the CityNav benchmark demonstrate that the ViSA-enhanced VLN achieves a 70.3\% improvement in success rate compared to the fully trained state-of-the-art (SOTA) method, elucidating its great potential as a backbone for aerial VLN systems.

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