Recent works have combined monocular event camera and inertial measurement unit to estimate the $SE(3)$ trajectory. However, the asynchronicity of event cameras brings a great challenge to conventional fusion algorithms. In this paper, we present an asynchronous event-inertial odometry under a unified Gaussian Process (GP) regression framework to naturally fuse asynchronous data associations and inertial measurements. A GP latent variable model is leveraged to build data-driven motion prior and acquire the analytical integration capacity. Then, asynchronous event-based feature associations and integral pseudo measurements are tightly coupled using the same GP framework. Subsequently, this fusion estimation problem is solved by underlying factor graph in a sliding-window manner. With consideration of sparsity, those historical states are marginalized orderly. A twin system is also designed for comparison, where the traditional inertial preintegration scheme is embedded in the GP-based framework to replace the GP latent variable model. Evaluations on public event-inertial datasets demonstrate the validity of both systems. Comparison experiments show competitive precision compared to the state-of-the-art synchronous scheme.

翻译：近期研究结合单目事件相机与惯性测量单元来估计 $SE(3)$ 轨迹。然而，事件相机的异步性给传统融合算法带来了巨大挑战。本文提出一种基于统一高斯过程回归框架的异步事件-惯性里程计方法，以自然融合异步数据关联与惯性测量。该方法利用高斯过程隐变量模型构建数据驱动的运动先验，并获得解析积分能力。随后，异步事件特征关联与积分伪测量值通过同一高斯过程框架进行紧耦合。该融合估计问题通过底层因子图以滑动窗口方式求解。考虑到稀疏性，历史状态被有序边缘化。本文还设计了一个对比孪生系统，其中将传统惯性预积分方案嵌入基于高斯过程的框架，以替代高斯过程隐变量模型。在公开事件-惯性数据集上的评估验证了两个系统的有效性。对比实验表明，与最先进的同步方案相比，本方法具有相当的精度竞争力。