用高斯过程统一建模异步事件与惯性数据，提升定位精度。
Asynchronous Event-Inertial Odometry using a Unified Gaussian Process Regression Framework
- 基于高斯过程构建运动先验，实现异步数据融合
- 在公开数据集上达到与同步方案相当的定位精度
- 适合需要高精度异步传感器融合的机器人导航场景
近期工作将单目事件相机与惯性测量单元结合以估计SE(3)轨迹，但事件相机的异步性给传统融合算法带来挑战。本文提出一种基于统一高斯过程（GP）回归框架的异步事件-惯性里程计，自然融合异步数据关联与惯性测量。利用GP隐变量模型建立数据驱动的运动先验，并获得解析积分能力。随后，通过同一GP框架紧密耦合异步事件特征关联与积分伪测量。该融合估计问题通过滑动窗口下的因子图求解，考虑稀疏性，有序地对历史状态进行边缘化。还设计了对比系统，将传统惯性预积分嵌入GP框架以替代GP隐变量模型。在公开事件-惯性数据集上的评估验证了两种系统的有效性。对比实验表明，其精度可与当前最先进的同步方案相媲美。
原文摘要 · Abstract (English)
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.
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