  Commonly, learning-based topological navigation approaches produce a local
policy while preserving some loose connectivity of the space through a
topological map. Nevertheless, spurious or missing edges in the topological
graph often lead to navigation failure. In this work, we propose a
sampling-based graph building method, which results in sparser graphs yet with
higher navigation performance compared to baseline methods. We also propose
graph maintenance strategies that eliminate spurious edges and expand the graph
as needed, which improves lifelong navigation performance. Unlike controllers
that learn from fixed training environments, we show that our model can be
fine-tuned using only a small number of collected trajectory images from a
real-world environment where the agent is deployed. We demonstrate successful
navigation after fine-tuning on real-world environments, and notably show
significant navigation improvements over time by applying our lifelong graph
maintenance strategies.
