Hybrid-EVIO: Event-Based Visual-Inertial Odometry with Hybrid Visual Front-End

Hybrid-EVIO: Event-Based Visual-Inertial Odometry with Hybrid Visual Front-End | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 5 February 2026 V2 Latest version Share on Hybrid-EVIO: Event-Based Visual-Inertial Odometry with Hybrid Visual Front-End Authors : Xiaokai Song 0000-0002-1409-7742 [email protected] , Zhang Li , Banglei Guan , Kun Wang , Yibin Ye , Zi Wang , and Qifeng Yu Authors Info & Affiliations https://doi.org/10.22541/au.175373191.11504859/v2 Published IEEE Transactions on Automation Science and Engineering Version of record Peer review timeline 186 views 193 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Frame-based visual-inertial odometry (VIO) can be significantly enhanced by integrating event cameras, particularly in high-speed motion and high-dynamic-range (HDR) scenarios. However, existing VIO frameworks often process event and frame-based data simultaneously, introducing unnecessary computational overhead. Additionally, tracking handcrafted features on event streams typically requires extensive parameter tuning and lacks robustness against noise. To address these limitations, we propose Hybrid-EVIO, a method that effectively fuses event data, standard frames, and inertial measurement unit (IMU) measurements. Hybrid-EVIO consists of a hybrid visual front-end and a sliding-window-based back-end. The front-end combines traditional and learning-based techniques in a scene-adaptive manner: features are tracked using either conventional methods on frames or a learned sparse optical flow network on asynchronous events, depending on the imaging quality. IMU measurements are further utilized to construct epipolar constraints, prefiltering extreme outliers before Random Sample Consensus and thereby improving pose estimation accuracy. Finally, a tightly coupled, graph-based optimization framework integrates three sensor modalities for high-precision state estimation. We evaluate the proposed method on multiple representative and challenging public datasets. Our approach outperforms state-of-the-art methods, reducing trajectory errors by up to 34\% in the best case. Our trajectories and evaluation code are publicly available at https://github.com/sssxxxkkkk/hybrid_EVIO . Supplementary Material File (event-based visual-inertial odometry with hybrid visual front-end-3.pdf) Download 11.55 MB Information & Authors Information Version history V1 Version 1 28 July 2025 V2 Version 2 05 February 2026 Peer review timeline Published IEEE Transactions on Automation Science and Engineering Version of Record 1 Jan 2026 Published Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords epipolar constraint event cameras learning sensor fusion visual inertial odometry (vio) Authors Affiliations Xiaokai Song 0000-0002-1409-7742 [email protected] View all articles by this author Zhang Li View all articles by this author Banglei Guan View all articles by this author Kun Wang View all articles by this author Yibin Ye View all articles by this author Zi Wang View all articles by this author Qifeng Yu View all articles by this author Metrics & Citations Metrics Article Usage 186 views 193 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Xiaokai Song, Zhang Li, Banglei Guan, et al. Hybrid-EVIO: Event-Based Visual-Inertial Odometry with Hybrid Visual Front-End. Authorea . 05 February 2026. DOI: https://doi.org/10.22541/au.175373191.11504859/v2 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. 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