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

TL;DR

This paper introduces a novel asynchronous event-inertial odometry method using a unified Gaussian Process framework, effectively handling asynchronous data and inertial measurements for improved trajectory estimation.

Contribution

It presents a unified GP-based approach for fusing asynchronous event camera data and inertial measurements, including a twin system with inertial preintegration for comparison.

Findings

Demonstrates competitive accuracy against state-of-the-art synchronous methods.

Validates the proposed system on public event-inertial datasets.

Shows effective handling of asynchronous data fusion in odometry.

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.