The robustness of event cameras to high dynamic range and motion blur holds the potential to improve visual odometry systems in challenging environments. Although their high temporal resolution does not require synchronous processing, most event-based odometry methods still run at fixed rates, which simplifies system design but restricts latency and throughput. In this work, we present AERO-VIS, a stereo event-inertial SLAM system with an integrated, data-driven, robust, and performance-optimized keypoint detector. By processing the event stream asynchronously, the system dynamically adapts to downstream runtime demands, ensuring low-latency and real-time performance. When deploying AERO-VIS on a UAV, we achieve unprecedented accuracy in onboard event-based SLAM. These unique characteristics enable us to present the first purely event-based inertial SLAM system that demonstrates closed-loop UAV control and large-scale state estimation while relying solely on onboard compute. A video of the experiments and the source code are available at ethz-mrl.github.io/AERO-VIS.

翻译：事件相机对高动态范围与运动模糊的鲁棒性，为在复杂环境中提升视觉里程计系统性能提供了潜力。尽管其高时间分辨率无需同步处理，但大多数基于事件的里程计方法仍以固定速率运行——这简化了系统设计，却限制了延迟和吞吐量。本文提出AERO-VIS：一种集成数据驱动、鲁棒且经性能优化的关键点检测器的立体事件-惯性SLAM系统。通过异步处理事件流，系统可动态适应下游实时性需求，确保低延迟与实时性能。将AERO-VIS部署于无人机时，我们在机载事件SLAM中取得了前所未有的精度。这些独特特性使我们能够首次实现完全基于事件的惯性SLAM系统，在仅依赖机载计算的情况下完成闭环无人机控制与大尺度状态估计。实验视频与源代码详见ethz-mrl.github.io/AERO-VIS。