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

TL;DR

This paper introduces an end-to-end flight control network that fuses event camera and depth data for high-speed UAV obstacle avoidance, achieving higher success rates than single-modality methods.

Contribution

It presents a novel bidirectional crossattention fusion method and an efficient planner, significantly improving obstacle avoidance performance at high speeds.

Findings

Achieves over 80% success rate at 17m/s in simulation.

Surpasses traditional planners with a smoother trajectory generation.

Improves success rate by 10-20% over single-modality models.

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 experiments show that our method attains a 70-80% success rate at 17 m/s across varied scenes, surpassing single-modality and unidirectional fusion models by 10-20%. These results demonstrate that bidirectional fusion effectively integrates event and depth information, enabling more reliable obstacle avoidance in complex environments with both static and dynamic objects.