[2603.27181] An End-to-end Flight Control Network for High-speed UAV Obstacle Avoidance based on Event-Depth Fusion

Computer Science > Robotics arXiv:2603.27181 (cs) [Submitted on 28 Mar 2026] Title:An End-to-end Flight Control Network for High-speed UAV Obstacle Avoidance based on Event-Depth Fusion Authors:Dikai Shang, Jingyue Zhao, Shi Xu, Nanyang Ye, Lei Wang View a PDF of the paper titled An End-to-end Flight Control Network for High-speed UAV Obstacle Avoidance based on Event-Depth Fusion, by Dikai Shang and Jingyue Zhao and Shi Xu and Nanyang Ye and Lei Wang View PDF HTML (experimental) Abstract: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 this insight, we design an efficient expert planner using Spherical Principal Search (SPS). This planner reduces computational complexity from $O(n^2)$ to $O(n)$ while generating smoother trajectories, achieving over 80% success rate at 17m/s--nearly 20% higher than traditional planners. Simulation experim...