LAGCN: Low-Cost Aerial-Ground Collaborative Navigation in Unknown Environments

Abstract

In complex and unknown environments, unmanned ground vehicles (UGVs) with no perception capability or degraded sensing struggle to achieve efficient and safe autonomous navigation. To address this challenge, this paper proposes a low-cost aerial–ground collaborative autonomous navigation system designed for unknown environments, which supports efficient navigation of perception-denied ground platforms while minimizing hardware requirements. In the proposed system, an unmanned aerial vehicle (UAV) equipped with a lightweight camera provides the UGV with relative position observations and semantic bird's-eye-view (BEV) maps. The semantic BEV is designed as a unified intermediate representation for UAV–UGV collaboration. While reducing communication overhead, semantic risk information is explicitly incorporated into the diffusion inference process, enabling the planner to satisfy geometric feasibility constraints during path generation while proactively avoiding semantically high-risk regions. Both simulation and real-world experiments validate the effectiveness of the proposed framework. In particular, within an unknown obstacle-filled space of 7 m × 4m × 3m, the UAV enables efficient and safe collaborative navigation for perception-denied UGVs, demonstrating strong application potential in complex and unknown environments.