Real-Time Neuromorphic Navigation: Integrating Event-Based Vision and Physics-Driven Planning on a Parrot Bebop2 Quadrotor

Understand

In autonomous aerial navigation, real-time and energy-efficient obstacle avoidance remains a significant challenge, especially in dynamic and complex indoor environments.

• This work presents a novel integration of neuromorphic event cameras with physics-driven planning algorithms implemented on a Parrot Bebop2 quadrotor.
• Neuromorphic event cameras, characterized by their high dynamic range and low latency, offer significant advantages over traditional frame-based systems, particularly in poor lighting conditions or during high-speed maneuvers.
• We use a DVS camera with a shallow Spiking Neural Network (SNN) for event-based object detection of a moving ring in real-time in an indoor lab.