SuperEIO: Self-Supervised Event Feature Learning for Event Inertial Odometry

Event cameras asynchronously output low-latency event streams, promising for state estimation in complex conditions. The motion-dependent nature of event cameras presents persistent challenges in achieving robust event feature detection and matching. Recent learning-based approaches have demonstrated superior robustness over traditional handcrafted methods, particularly under aggressive motion and HDR scenarios. This article proposes SuperEIO, a novel framework that leverages a learning-based event-only detector and IMU measurements for event-inertial odometry. Our event-only feature detector employs a convolutional neural network on continuous event streams, while a graph neural network achieves event descriptor matching for loop closure. We accelerate network inference with TensorRT, ensuring low-latency, real-time operation on resource-constrained devices. Extensive evaluations on multiple public benchmarks demonstrate its superior accuracy and robustness compared with the advanced event-based methods. Moreover, we conduct a large-scale real-world experiment on an edge handheld platform to demonstrate long-term effectiveness. Our pipeline is open-sourced to facilitate research in the field:https://github.com/arclab-hku/SuperEIO.