AERO-VIS：异步事件基于的实时机载视觉-惯性SLAM
AERO-VIS: Asynchronous Event-based Real-time Onboard Visual-Inertial SLAM
AI总结：
AERO-VIS是一种集成、数据驱动、稳健且性能优化的双目事件-惯性SLAM系统，通过异步处理事件流，实现低延迟和实时性能，首次实现了纯事件基于的惯性SLAM系统，支持闭环无人机控制和大规模状态估计。
摘要：
事件相机对高动态范围和运动模糊的鲁棒性有潜力改进视觉里程计系统在挑战性环境中的性能。尽管其高时间分辨率不需要同步处理，但大多数基于事件的里程计方法仍以固定速率运行，这简化了系统设计但限制了延迟和吞吐量。本文提出AERO-VIS，一种具有集成、数据驱动、稳健且性能优化的关键点检测器的双目事件-惯性SLAM系统。通过异步处理事件流，系统能够动态适应下游运行需求，确保低延迟和实时性能。在将AERO-VIS部署到无人机上时，实现了前所未有的机载基于事件的SLAM精度。这些独特特性使我们能够提出首个纯事件基于的惯性SLAM系统，该系统在仅依赖机载计算的情况下展示了闭环无人机控制和大规模状态估计。实验视频和源代码可在ethz-mrl.github.io/AERO-VIS获取。
英文摘要：
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 decoupling event preprocessing from state estimation, our architecture operates asynchronously at the maximum rate supported by available hardware, dynamically adapting to downstream runtime demands and ensuring low-latency, 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. Videos of the experiments, source code, and additional results are available at https://ethz-mrl.github.io/AERO-VIS/.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
