  The exceptional mobility and long endurance of air-ground robots are raising
interest in their usage to navigate complex environments (e.g., forests and
large buildings). However, such environments often contain occluded and unknown
regions, and without accurate prediction of unobserved obstacles, the movement
of the air-ground robot often suffers a suboptimal trajectory under existing
mapping-based and learning-based navigation methods. In this work, we present
AGRNav, a novel framework designed to search for safe and energy-saving
air-ground hybrid paths. AGRNav contains a lightweight semantic scene
completion network (SCONet) with self-attention to enable accurate obstacle
predictions by capturing contextual information and occlusion area features.
The framework subsequently employs a query-based method for low-latency updates
of prediction results to the grid map. Finally, based on the updated map, the
hierarchical path planner efficiently searches for energy-saving paths for
navigation. We validate AGRNav's performance through benchmarks in both
simulated and real-world environments, demonstrating its superiority over
classical and state-of-the-art methods. The open-source code is available at
https://github.com/jmwang0117/AGRNav.
