LookasideVLN：方向感知的空中视觉与语言导航
LookasideVLN: Direction-Aware Aerial Vision-and-Language Navigation
将自然语言中的方向线索（如“左转”“前方”）编码为图结构，并与多模态大语言模型对齐用于路径规划，为视觉语言导航提供了一种轻量且可解释的空间推理范式。
原文依据 LookasideVLN comprises three core components: (1) an Egocentric Lookaside Graph (ELG) that dynamically encodes instruction-relevant landmarks and their directional relationships
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论空中视觉与语言导航（Aerial VLN）使无人机能够遵循自然语言指令在复杂城市环境中导航。尽管近期进展通过大规模记忆图和前瞻路径规划取得了进步，但它们仍受限于浅层的指令理解和高计算成本。具体而言，现有方法主要依赖地标描述，忽略了方向线索——人类导航中空间上下文的关键来源。在本工作中，我们提出LookasideVLN，一种利用自然语言中的方向线索来实现更准确空间推理和更高计算效率的新范式。LookasideVLN包含三个核心组件：（1）自我中心旁视图（ELG），动态编码指令相关的地标及其方向关系；（2）空间地标知识库（SLKB），从先前的导航经验中提供轻量级记忆检索；（3）旁视多模态大语言模型导航代理，对齐来自用户指令、视觉观察以及ELG中地标方向信息的多模态信息以进行路径规划。大量实验表明，LookasideVLN显著优于最优的CityNavAgent，即使在单层前瞻的情况下也是如此，证明利用方向线索是空中视觉与语言导航中一种强大而高效的策略。
英文原文摘要 1438 字符
Aerial Vision-and-Language Navigation (Aerial VLN) enables unmanned aerial vehicles (UAVs) to follow natural language instructions and navigate complex urban environments.While recent advances have achieved progress through large-scale memory graphs and lookahead path planning, they remain limited by shallow instruction understanding and high computational cost. In particular, existing methods rely primarily on landmark descriptions, overlooking directional cues--a key source of spatial context in human navigation.In this work, we propose LookasideVLN, a new paradigm that exploits directional cues in natural language to achieve both more accurate spatial reasoning and greater computational efficiency. LookasideVLN comprises three core components: (1) an Egocentric Lookaside Graph (ELG) that dynamically encodes instruction-relevant landmarks and their directional relationships, (2) a Spatial Landmark Knowledge Base (SLKB) that provides lightweight memory retrieval from prior navigation experiences, and (3) a Lookaside MLLM Navigation Agent that aligns multimodal information from user instructions, visual observations, and landmark-direction information from ELG for path planning.Extensive experiments show that LookasideVLN significantly outperforms the state-of-the-art CityNavAgent, even with a single-level lookahead, demonstrating that leveraging directional cues is a powerful yet efficient strategy for Aerial VLN.