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arXiv · 2412.03136

Asynchronous Event-Inertial Odometry using a Unified Gaussian Process Regression Framework

Xudong Li·Zhixiang Wang·Zihao Liu·Yizhai Zhang·Fan Zhang·Xiuming Yao·Panfeng Huang
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Abstract

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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Xudong Li, Zhixiang Wang, Zihao Liu, Yizhai Zhang, Fan Zhang, Xiuming Yao, Panfeng Huang. 2024-12-04. Asynchronous Event-Inertial Odometry using a Unified Gaussian Process Regression Framework. https://doi.org/10.1109/iros58592.2024.10802357

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