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

2024-12-12

IEEE Robotics and Automation Letters

AI Summary

Existing asynchronous event-camera–IMU tightly coupled odometry methods suffer from limited accuracy and latency under high-speed motion and high-dynamic-range (HDR) conditions, primarily due to reliance on conventional discrete-time preintegration models. This paper proposes the Gaussian Process Pre-optimization (GPO) framework—a continuous-time formulation enabling analytically tractable state and Jacobian propagation at arbitrary timestamps. GPO introduces temporal Gaussian processes (TGPs) for continuous preintegration, achieving linear optimization complexity and constant-time query capability. Leveraging a lightweight two-stage optimization and asynchronous event–inertial tight coupling within a filtering paradigm, GPO natively supports fully asynchronous sensor fusion. Evaluated on both public and in-house datasets, GPO consistently improves localization accuracy and computational efficiency over state-of-the-art asynchronous fusion approaches, demonstrating superior overall performance.

Technology Category

Intelligent Robots: State EstimationSearch and Optimization: Mixed Discrete/Continuous SearchPlanning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphsSystems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applicationsSemantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

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 letter, we propose GPO, a continuous-time preintegration framework that can efficiently achieve tightly-coupled fusion of fully asynchronous sensors. Concretely, we model the preintegration as two local Temporal Gaussian Process (TGP) trajectories and leverage a light-weight two-step optimization to infer the continuous preintegration pseudo-measurements. We show that the Jacobians of arbitrary queried states can be naturally propagated using our framework, which enables GPO to be involved in the asynchronous fusion. Our method realizes a linear and constant time cost for optimization and query, respectively. To further validate the proposal, we leverage GPO to design an asynchronous event-inertial odometry and compare with other asynchronous fusion schemes. Experiments conducted on both public and own-collected datasets demonstrate that the proposed GPO offers significant advantages in terms of accuracy and efficiency, outperforming existing approaches in handling asynchronous sensor fusion.

Problem

• Developing continuous-time preintegration for asynchronous event-inertial fusion
• Overcoming limitations of IMU preintegration designed for synchronous sensors
• Enabling precise ego-motion estimation in high-speed HDR environments

Innovation

• Continuous-time preintegration using Temporal Gaussian Process
• Efficient two-step optimization for precision initialization
• Linear constant time cost for initialization and query