RBD-EVIO: optimized event-based visual-inertial odometry for a large field of view with a rotated binary DART descriptor

Event-based cameras offer unique advantages over traditional cameras, such as high dynamic range, absence of motion blur, and microsecond-level latency. This paper introduces an innovative approach to visual odometry, to our knowledge, by integrating the newly proposed Rotated Binary DART (RBD) descriptor within a Visual-Inertial Navigation System (VINS)-based event visual odometry framework. Our method leverages event optical flow and RBD for precise feature selection and matching, ensuring robust performance in dynamic environments. We further validate the effectiveness of RBD in scenarios captured by a large field-of-view (FoV) fisheye event camera under high dynamic range and high-speed rotation conditions. Our results demonstrate significant improvements in tracking accuracy and robustness, setting what we believe to be a new benchmark for event-based visual odometry. This work paves the way for advanced applications in high-speed, high dynamic range, and large FoV visual sensing.