# AsynEIO: Asynchronous Monocular Event-Inertial Odometry Using Gaussian Process Regression

arXiv: https://arxiv.org/abs/2411.12175 · HTML: https://mlsanity.com/paper/2411.12175/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 33 of its 64 references that are in the map.
- First posted on arXiv 19 Nov 2024.

## Builds on (most cited first, 30 of 33)

- [Event-based Vision: A Survey](https://mlsanity.com/paper/1904.08405/) (2019)
- [VINS-Mono: A Robust and Versatile Monocular Visual-Inertial State Estimator](https://mlsanity.com/paper/1708.03852/) (2017)
- [The Event-Camera Dataset and Simulator: Event-based Data for Pose Estimation, Visual Odometry, and SLAM](https://mlsanity.com/paper/1610.08336/) (2016)
- [Ultimate SLAM? Combining Events, Images, and IMU for Robust Visual SLAM in HDR and High Speed Scenarios](https://mlsanity.com/paper/1709.06310/) (2017)
- [Asynchronous, Photometric Feature Tracking using Events and Frames](https://mlsanity.com/paper/1807.09713/) (2018)
- [Event-based Stereo Visual Odometry](https://mlsanity.com/paper/2007.15548/) (2020)
- [On-Manifold Preintegration for Real-Time Visual-Inertial Odometry](https://mlsanity.com/paper/1512.02363/) (2015)
- [Continuous-Time Visual-Inertial Odometry for Event Cameras](https://mlsanity.com/paper/1702.07389/) (2017)
- [Towards Robust Event-guided Low-Light Image Enhancement: A Large-Scale Real-World Event-Image Dataset and Novel Approach](https://mlsanity.com/paper/2404.00834/) (2024)
- [VECtor: A Versatile Event-Centric Benchmark for Multi-Sensor SLAM](https://mlsanity.com/paper/2207.01404/) (2022)
- [CT-ICP: Real-time Elastic LiDAR Odometry with Loop Closure](https://mlsanity.com/paper/2109.12979/) (2021)
- [Exploring Event Camera-based Odometry for Planetary Robots](https://mlsanity.com/paper/2204.05880/) (2022)
- [ESVIO: Event-based Stereo Visual Inertial Odometry](https://mlsanity.com/paper/2212.13184/) (2022)
- [Deep Event Visual Odometry](https://mlsanity.com/paper/2312.09800/) (2023)
- [IDOL: A Framework for IMU-DVS Odometry using Lines](https://mlsanity.com/paper/2008.05749/) (2020)
- [FA-Harris: A Fast and Asynchronous Corner Detector for Event Cameras](https://mlsanity.com/paper/1906.10925/) (2019)
- [PL-EVIO: Robust Monocular Event-based Visual Inertial Odometry with Point and Line Features](https://mlsanity.com/paper/2209.12160/) (2022)
- [eCDT: Event Clustering for Simultaneous Feature Detection and Tracking-](https://mlsanity.com/paper/2207.09108/) (2022)
- [Event-based Visual Inertial Velometer](https://mlsanity.com/paper/2311.18189/) (2023)
- [Asynchronous Multi-View SLAM](https://mlsanity.com/paper/2101.06562/) (2021)
- [Continuous-Time vs. Discrete-Time Vision-based SLAM: A Comparative Study](https://mlsanity.com/paper/2202.08894/) (2022)
- [Asynchronous Optimisation for Event-based Visual Odometry](https://mlsanity.com/paper/2203.01037/) (2022)
- [E-Calib: A Fast, Robust and Accurate Calibration Toolbox for Event Cameras](https://mlsanity.com/paper/2306.09078/) (2023)
- [Cross-Modal Semi-Dense 6-DoF Tracking of an Event Camera in Challenging Conditions](https://mlsanity.com/paper/2401.08043/) (2024)
- [IMU-Aided Event-based Stereo Visual Odometry](https://mlsanity.com/paper/2405.04071/) (2024)
- [DEIO: Deep Event Inertial Odometry](https://mlsanity.com/paper/2411.03928/) (2024)
- [Event-based Stereo Visual Odometry with Native Temporal Resolution via Continuous-time Gaussian Process Regression](https://mlsanity.com/paper/2306.01188/) (2023)
- [A White-Noise-On-Jerk Motion Prior for Continuous-Time Trajectory Estimation on SE(3)](https://mlsanity.com/paper/1809.06518/) (2018)
- [Continuous-Time Radar-Inertial and Lidar-Inertial Odometry using a Gaussian Process Motion Prior](https://mlsanity.com/paper/2402.06174/) (2024)
- [Traj-LIO: A Resilient Multi-LiDAR Multi-IMU State Estimator Through Sparse Gaussian Process](https://mlsanity.com/paper/2402.09189/) (2024)

## Built on by (most cited first, 1 of 1)

- [eKalibr-Inertial: Continuous-Time Spatiotemporal Calibration for Event-Based Visual-Inertial Systems](https://mlsanity.com/paper/2509.05923/) (2025)

## Abstract (arXiv)

Event cameras, when combined with inertial sensors, show significant potential for motion estimation in challenging scenarios, such as high-speed maneuvers and low-light environments. There are many methods for producing such estimations, but most boil down to a synchronous discrete-time fusion problem. However, the asynchronous nature of event cameras and their unique fusion mechanism with inertial sensors remain underexplored. In this paper, we introduce a monocular event-inertial odometry method called AsynEIO, designed to fuse asynchronous event and inertial data within a unified Gaussian Process (GP) regression framework. Our approach incorporates an event-driven frontend that tracks feature trajectories directly from raw event streams at a high temporal resolution. These tracked feature trajectories, along with various inertial factors, are integrated into the same GP regression framework to enable asynchronous fusion. With deriving analytical residual Jacobians and noise models, our method constructs a factor graph that is iteratively optimized and pruned using a sliding-window optimizer. Comparative assessments highlight the performance of different inertial fusion strategies, suggesting optimal choices for varying conditions. Experimental results on both public datasets and our own event-inertial sequences indicate that AsynEIO outperforms existing methods, especially in high-speed and low-illumination scenarios.