Vision and language navigation (VLN) is a challenging visually-grounded
language understanding task. Given a natural language navigation instruction, a
visual agent interacts with a graph-based environment equipped with panorama
images and tries to follow the described route. Most prior work has been
conducted in indoor scenarios where best results were obtained for navigation
on routes that are similar to the training routes, with sharp drops in
performance when testing on unseen environments. We focus on VLN in outdoor
scenarios and find that in contrast to indoor VLN, most of the gain in outdoor
VLN on unseen data is due to features like junction type embedding or heading
delta that are specific to the respective environment graph, while image
information plays a very minor role in generalizing VLN to unseen outdoor
areas. These findings show a bias to specifics of graph representations of
urban environments, demanding that VLN tasks grow in scale and diversity of
geographical environments.

本研究探讨了在室外场景中，视觉代理如何跨越未知地形实现指令导航，发现与室内情况相比，图形化表达方式对室外导航具有更大的影响，因此未来的研究需关注在不同地形环境中的规模和多样性增长。