# DEIO: Deep Event Inertial Odometry

arXiv: https://arxiv.org/abs/2411.03928 · HTML: https://mlsanity.com/paper/2411.03928/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Nov 2024.

## Built on by (most cited first, 7 of 7)

- [SuperEvent: Cross-Modal Learning of Event-based Keypoint Detection for SLAM](https://mlsanity.com/paper/2504.00139/) (2025)
- [Event6D: Event-based Novel Object 6D Pose Tracking](https://mlsanity.com/paper/2603.28045/) (2026)
- [AsynEIO: Asynchronous Monocular Event-Inertial Odometry Using Gaussian Process Regression](https://mlsanity.com/paper/2411.12175/) (2024)
- [SuperEIO: Self-Supervised Event Feature Learning for Event Inertial Odometry](https://mlsanity.com/paper/2503.22963/) (2025)
- [Edged USLAM: Edge-Aware Event-Based SLAM with Learning-Based Depth Priors](https://mlsanity.com/paper/2603.08150/) (2026)
- [E-S2Feat:Semantic-Guided Spiking Local Feature Detection and Description for Event Cameras](https://mlsanity.com/paper/2608.14027/) (2026)
- [DAPEVO: Deep Adaptive Patch Frame-Event Visual Odometry](https://mlsanity.com/paper/2609.30947/) (2026)

## Abstract (arXiv)

Event cameras show great potential for visual odometry (VO) in handling challenging situations, such as fast motion and high dynamic range. Despite this promise, the sparse and motion-dependent characteristics of event data continue to limit the performance of feature-based or direct-based data association methods in practical applications. To address these limitations, we propose Deep Event Inertial Odometry (DEIO), the first monocular learning-based event-inertial framework, which combines a learning-based method with traditional nonlinear graph-based optimization. Specifically, an event-based recurrent network is adopted to provide accurate and sparse associations of event patches over time. DEIO further integrates it with the IMU to recover up-to-scale pose and provide robust state estimation. The Hessian information derived from the learned differentiable bundle adjustment (DBA) is utilized to optimize the co-visibility factor graph, which tightly incorporates event patch correspondences and IMU pre-integration within a keyframe-based sliding window. Comprehensive validations demonstrate that DEIO achieves superior performance on textit10 challenging public benchmarks compared with more than 20 state-of-the-art methods.