Continuous Gaussian Process Pre-Optimization for Asynchronous Event-Inertial Odometry

TL;DR

This paper introduces a continuous-time Gaussian Process-based pre-integration method for asynchronous event-inertial odometry, improving accuracy and efficiency in high-speed, HDR environments by better fusing asynchronous sensor data.

Contribution

It proposes a novel continuous-time preintegration approach using Temporal Gaussian Processes, enabling efficient and precise asynchronous event-inertial sensor fusion.

Findings

Outperforms existing methods in accuracy and efficiency

Provides constant-time initialization and querying

Demonstrates superior performance on multiple datasets

Abstract

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.