Event cameras, as bio-inspired sensors, are asynchronously triggered with high-temporal resolution compared to intensity cameras. Recent work has focused on fusing the event measurements with inertial measurements to enable ego-motion estimation in high-speed and HDR environments. However, existing methods predominantly rely on IMU preintegration designed mainly for synchronous sensors and discrete-time frameworks. In this paper, we propose a continuous-time preintegration method based on the Temporal Gaussian Process (TGP) called GPO. Concretely, we model the preintegration as a time-indexed motion trajectory and leverage an efficient two-step optimization to initialize the precision preintegration pseudo-measurements. Our method realizes a linear and constant time cost for initialization and query, respectively. To further validate the proposal, we leverage the GPO to design an asynchronous event-inertial odometry and compare with other asynchronous fusion schemes within the same odometry system. Experiments conducted on both public and own-collected datasets demonstrate that the proposed GPO offers significant advantages in terms of precision and efficiency, outperforming existing approaches in handling asynchronous sensor fusion.

翻译：事件相机作为仿生传感器，与强度相机相比具有异步触发和高时间分辨率的特点。近期研究集中于将事件测量与惯性测量融合，以实现高速和高动态范围环境下的自身运动估计。然而，现有方法主要依赖于主要为同步传感器和离散时间框架设计的IMU预积分。本文提出一种基于时间高斯过程的连续时间预积分方法，称为GPO。具体而言，我们将预积分建模为时间索引的运动轨迹，并利用高效的两步优化来初始化精确的预积分伪测量。我们的方法实现了初始化和查询分别具有线性和恒定时间成本。为进一步验证该方案，我们利用GPO设计了一个异步事件-惯性里程计，并在同一里程计系统内与其他异步融合方案进行比较。在公开数据集和自采集数据集上进行的实验表明，所提出的GPO在精度和效率方面具有显著优势，在处理异步传感器融合方面优于现有方法。