提出连续时间预积分方法，提升异步事件-惯性里程计精度与效率
Continuous Gaussian Process Pre-Optimization for Asynchronous Event-Inertial Odometry
- 用时间索引运动轨迹建模，基于高斯过程实现连续时间预积分
- 初始化与查询时间复杂度分别为线性和常数，显著提升效率
- 在公开与自采数据集上优于现有方法，适合高速高动态场景
事件相机作为类生物传感器，以高时间分辨率异步触发，相比传统强度相机更具优势。近期研究聚焦于融合事件与惯性测量，以实现在高速和高动态范围环境下的自身运动估计。然而，现有方法主要依赖为同步传感器设计的惯性预积分及离散时间框架。本文提出一种基于时间高斯过程（TGP）的连续时间预积分方法GPO，将预积分建模为时间索引的运动轨迹，并采用高效的两步优化初始化精确的预积分伪测量。该方法实现初始化的线性时间复杂度和查询的常数时间复杂度。为进一步验证，我们基于GPO设计了异步事件-惯性里程计，并在相同系统中与其他异步融合方案对比。在公开与自采数据集上的实验表明，所提GPO在精度与效率方面均显著优于现有方法，尤其在处理异步传感器融合时表现优异。
原文摘要 · Abstract (English)
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.
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