  Currently, state-of-the-art exploration methods maintain high-resolution map
representations in order to optimize exploration goals in each step that
maximizes information gain. However, during exploring, those "optimal"
selections could quickly become obsolete due to the influx of new information,
especially in large-scale environments, and result in high-frequency
re-planning that hinders the overall exploration efficiency. In this paper, we
propose a graph-based topological planning framework, building a sparse
topological map in three-dimensional (3D) space to guide exploration steps with
high-level intents so as to render consistent exploration maneuvers.
Specifically, this work presents a novel method to estimate 3D space's geometry
with convex polyhedrons. Then, the geometry information is utilized to group
space into distinctive regions. And those regions are added as nodes into the
topological map, directing the exploration process. We compared our method with
the state-of-the-art in simulated environments. The proposed method achieves
higher space coverage and outperforms exploration efficiency by more than 40%
during experiments. Finally, a field experiment was conducted to further
evaluate the applicability of our method to empower efficient and robust
exploration in real-world environments.
