• computer vision
• deep learning
• neuromorphic event camera
• object detection
• remote sensing
• SLAM

This thesis explores in depth the study and application of neuromorphic event cameras, highlighting their unique features and the benefits they can bring to fields such as Simultaneous Localization and Mapping (SLAM) and object detection. Unlike traditional frame-based cameras, neuromorphic event cameras record light variations on a pixel-by-pixel basis, offering high-speed response and reduced energy consumption. These characteristics make them particularly suitable for dynamic applications and environments with variable lighting conditions.