事件相机实时无人机定位，异步处理提升精度与速度
AERO-VIS: Asynchronous Event-based Real-time Onboard Visual-Inertial SLAM
- 异步处理事件流，动态适应计算需求
- 在无人机上实现闭环控制与大规模状态估计
- 首个仅依赖机载算力的纯事件惯性SLAM系统
事件相机对高动态范围和运动模糊具有鲁棒性，有望提升复杂环境下的视觉里程计性能。尽管其高时间分辨率无需同步处理，但多数事件式里程计仍以固定速率运行，虽简化设计却限制了延迟与吞吐量。本文提出AERO-VIS，一种集成数据驱动、鲁棒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，系统能动态响应下游运行需求，确保低延迟与实时性。在无人机部署中，AERO-VIS实现了前所未有的机载事件式SLAM精度。该特性使我们首次展示仅依赖机载计算的纯事件惯性SLAM系统，支持闭环无人机控制与大规模状态估计。实验视频与源代码见ethz-mrl.github.io/AERO-VIS。
原文摘要 · Abstract (English)
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.
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