{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/deio-deep-event-inertial-odometry","title":"DEIO: Deep Event Inertial Odometry","arxiv_id":"2411.03928","date":"2024-11-06","proceeding":null,"authors":["Weipeng Guan","Fuling Lin","Peiyu Chen","Peng Lu"],"abstract":"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 \\textit{10} challenging public benchmarks compared with more than 20 state-of-the-art methods.","url_abs":"https://arxiv.org/abs/2411.03928v4","url_pdf":"https://arxiv.org/pdf/2411.03928v4.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"deio-deep-event-inertial-odometry","repo_url":"https://github.com/arclab-hku/deio","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"deio-deep-event-inertial-odometry","repo_url":"https://github.com/arclab-hku/event_based_vo-vio-slam","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=2411.03928","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}