Similar to vision-and-language navigation (VLN) tasks that focus on bridging the gap between vision and language for embodied navigation, the new Rendezvous (RVS) task requires reasoning over allocentric spatial relationships (independent of the observer's viewpoint) using non-sequential navigation instructions and maps. However, performance substantially drops in new environments with no training data. Using opensource descriptions paired with coordinates (e.g., Wikipedia) provides training data but suffers from limited spatially-oriented text resulting in low geolocation resolution. We propose a large-scale augmentation method for generating high-quality synthetic data for new environments using readily available geospatial data. Our method constructs a grounded knowledge-graph, capturing entity relationships. Sampled entities and relations (`shop north of school') generate navigation instructions via (i) generating numerous templates using context-free grammar (CFG) to embed specific entities and relations; (ii) feeding the entities and relation into a large language model (LLM) for instruction generation. A comprehensive evaluation on RVS, showed that our approach improves the 100-meter accuracy by 45.83% on unseen environments. Furthermore, we demonstrate that models trained with CFG-based augmentation achieve superior performance compared with those trained with LLM-based augmentation, both in unseen and seen environments. These findings suggest that the potential advantages of explicitly structuring spatial information for text-based geospatial reasoning in previously unknown, can unlock data-scarce scenarios.

翻译：与专注于为具身导航弥合视觉与语言鸿沟的视觉语言导航任务类似，新型会合任务要求利用非顺序性导航指令和地图，对以客体为中心的空间关系进行推理。然而，在缺乏训练数据的新环境中，模型性能会大幅下降。使用与坐标配对的开源描述虽能提供训练数据，但受限于空间指向性文本的匮乏，导致地理定位分辨率低下。我们提出一种大规模数据增强方法，利用现成的地理空间数据为全新环境生成高质量合成数据。该方法构建了一个实体关系知识图谱，通过采样实体与关系生成导航指令：首先利用上下文无关文法生成大量包含特定实体与关系的文本模板；随后将实体与关系输入大语言模型以生成指令。在会合任务上的综合评估表明，该方法在未见环境中的百米精度提升了45.83%。进一步实验证明，基于上下文无关文法的数据增强训练模型，在未见与已见环境中均优于基于大语言模型增强的训练模型。这些发现表明，在数据稀缺的未知场景中，显式构建空间信息结构能为基于文本的地理空间推理带来潜在优势。