  Most existing works solving Room-to-Room VLN problem only utilize RGB images
and do not consider local context around candidate views, which lack sufficient
visual cues about surrounding environment. Moreover, natural language contains
complex semantic information thus its correlations with visual inputs are hard
to model merely with cross attention. In this paper, we propose GeoVLN, which
learns Geometry-enhanced visual representation based on slot attention for
robust Visual-and-Language Navigation. The RGB images are compensated with the
corresponding depth maps and normal maps predicted by Omnidata as visual
inputs. Technically, we introduce a two-stage module that combine local slot
attention and CLIP model to produce geometry-enhanced representation from such
input. We employ V&L BERT to learn a cross-modal representation that
incorporate both language and vision informations. Additionally, a novel
multiway attention module is designed, encouraging different phrases of input
instruction to exploit the most related features from visual input. Extensive
experiments demonstrate the effectiveness of our newly designed modules and
show the compelling performance of the proposed method.
