AsynEIO：基于高斯过程回归的异步单目事件-惯性里程计
AsynEIO: Asynchronous Monocular Event-Inertial Odometry Using Gaussian Process Regression
AI总结：
AsynEIO 基于高斯过程回归，实现异步单目事件-惯性里程计，通过高时间分辨率跟踪特征轨迹并融合惯性数据，在高速和低光照场景中表现优异。
摘要：
事件相机与惯性传感器结合在高速 maneuver 和低光环境下具有显著的运动估计潜力。尽管有许多方法用于产生此类估计，但大多数都归结为同步离散时间融合问题。然而，事件相机的异步性质及其与惯性传感器的独特融合机制仍被低估。本文介绍了一种名为 AsynEIO 的单目事件-惯性里程计方法，旨在在统一的高斯过程（GP）回归框架内融合异步事件和惯性数据。我们的方法包含一个事件驱动的前端，该前端以高时间分辨率直接从原始事件流中跟踪特征轨迹。这些跟踪的特征轨迹，加上各种惯性因素，被整合到同一个 GP 回归框架中，以实现异步融合。通过推导分析残差雅可比和噪声模型，我们的方法构建了一个因子图，通过滑动窗口优化器迭代优化和修剪。比较评估突显了不同惯性融合策略的性能，建议在不同条件下选择最优策略。在公共数据集和我们自己的事件-惯性序列上的实验结果表明，AsynEIO 在高速和低光照场景中优于现有方法。
英文摘要：
Event cameras, when combined with inertial sensors, show significant potential for motion estimation in challenging scenarios, such as high-speed maneuvers and low-light environments. There are many methods for producing such estimations, but most boil down to a synchronous discrete-time fusion problem. However, the asynchronous nature of event cameras and their unique fusion mechanism with inertial sensors remain underexplored. In this paper, we introduce a monocular event-inertial odometry method called AsynEIO, designed to fuse asynchronous event and inertial data within a unified Gaussian Process (GP) regression framework. Our approach incorporates an event-driven frontend that tracks feature trajectories directly from raw event streams at a high temporal resolution. These tracked feature trajectories, along with various inertial factors, are integrated into the same GP regression framework to enable asynchronous fusion. With deriving analytical residual Jacobians and noise models, our method constructs a factor graph that is iteratively optimized and pruned using a sliding-window optimizer. Comparative assessments highlight the performance of different inertial fusion strategies, suggesting optimal choices for varying conditions. Experimental results on both public datasets and our own event-inertial sequences indicate that AsynEIO outperforms existing methods, especially in high-speed and low-illumination scenarios.
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。
