An End-to-end Flight Control Network for High-speed UAV Obstacle Avoidance based on Event-Depth Fusion
Achieving safe, high-speed autonomous flight in complex environments with static, dynamic, or mixed obstacles remains challenging, as a single perception modality is incomplete. Depth cameras are effective for static objects but suffer from motion blur at high speeds. Conversely, event cameras excel at capturing rapid motion but struggle to perceive static scenes. To exploit the complementary strengths of both sensors, we propose an end-to-end flight control network that achieves feature-level fusion of depth images and event data through a bidirectional crossattention module. The end-to-end network is trained via imitation learning, which relies on high-quality supervision. Building on t...
| Authors | Dikai Shang, Jingyue Zhao, Shi Xu et al. |
|---|---|
| Primary category | cs.RO |
| Published | 28 March 2026 |
| Last updated at source | 28 March 2026 |
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