Graph-Based Topological Exploration Planning in Large-Scale 3D Environments
This work addresses exploration inefficiency for robotics and autonomous systems in large-scale 3D settings, representing an incremental improvement over existing methods.
The paper tackles the problem of inefficient exploration planning in large-scale 3D environments by proposing a graph-based topological framework that uses convex polyhedrons to estimate geometry and group space into regions, achieving over 40% higher exploration efficiency compared to state-of-the-art methods in simulations.
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.