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.

通过构建基于地理空间数据的知识图谱，利用上下文无关文法（CFG）和大型语言模型 (LLM) 生成导航指令，本研究提出了一种用于生成高质量合成数据的大规模增强方法，以解决新环境下训练数据不足的问题。结果显示，通过 CFG 增强训练的模型在未知环境和已知环境中均取得了更优异的性能，这表明在未知领域中，显式结构化空间信息的文本地理推理具有潜在的优势，可以应用于数据稀缺的场景。