Vision-and-language navigation (VLN) enables the agent to navigate to a
remote location following the natural language instruction in 3D environments.
To represent the previously visited environment, most approaches for VLN
implement memory using recurrent states, topological maps, or top-down semantic
maps. In contrast to these approaches, we build the top-down egocentric and
dynamically growing Grid Memory Map (i.e., GridMM) to structure the visited
environment. From a global perspective, historical observations are projected
into a unified grid map in a top-down view, which can better represent the
spatial relations of the environment. From a local perspective, we further
propose an instruction relevance aggregation method to capture fine-grained
visual clues in each grid region. Extensive experiments are conducted on both
the REVERIE, R2R, SOON datasets in the discrete environments, and the R2R-CE
dataset in the continuous environments, showing the superiority of our proposed
method