  Humans have a natural ability to perform semantic associations with the
surrounding objects in the environment. This allows them to create a mental map
of the environment, allowing them to navigate on-demand when given linguistic
instructions. A natural goal in Vision Language Navigation (VLN) research is to
impart autonomous agents with similar capabilities. Recent works take a step
towards this goal by creating a semantic spatial map representation of the
environment without any labeled data. However, their representations are
limited for practical applicability as they do not distinguish between
different instances of the same object. In this work, we address this
limitation by integrating instance-level information into spatial map
representation using a community detection algorithm and utilizing word
ontology learned by large language models (LLMs) to perform open-set semantic
associations in the mapping representation. The resulting map representation
improves the navigation performance by two-fold (233%) on realistic language
commands with instance-specific descriptions compared to the baseline. We
validate the practicality and effectiveness of our approach through extensive
qualitative and quantitative experiments.
