{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,18]],"date-time":"2026-09-18T05:05:07Z","timestamp":1789707907286,"version":"4.0.1"},"reference-count":43,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2026,9,15]],"date-time":"2026-09-15T00:00:00Z","timestamp":1789430400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["Grant U2141237"],"award-info":[{"award-number":["Grant U2141237"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"award":["Grant U2141237"],"award-info":[{"award-number":["Grant U2141237"]}],"id":[{"id":"https:\/\/ror.org\/01h0zpd94","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Visual\u2013inertial odometry (VIO) on industrial stereo camera\u2013IMU platforms suffers from frame-dependent timing errors and degraded feature tracking under auto-exposure, auto-gain adjustment, and rapid motion. This paper presents Hardware-Synchronized Exposure-State-Aware VINS-Fusion (HS-ES-VINS), a hardware-synchronized, exposure-aware extension of VINS-Fusion. The effective sampling time of each global-shutter image is reconstructed at the exposure midpoint using synchronized trigger timestamps, the camera debounce delay, and the frame-wise exposure duration. An exposure\u2013gain\u2013motion risk model adaptively adjusts feature detection criteria, Kanade\u2013Lucas\u2013Tomasi (KLT) tracking parameters, and correspondence filtering, while IMU-integrated rotation supplies initial predictions for feature tracking. On hardware-synchronized indoor and outdoor datasets with independent ground truth, controlled experiments show that the exposure-midpoint timestamp removes the frame-dependent exposure component of the camera\u2013IMU offset that neither fixed nor online scalar compensation can remove, reducing the ATE RMSE by 28.6% (fixed offset) and 22.7% (online estimation) relative to the exposure-start timestamp under 20 ms low-light exposure. Compared with baseline VINS-Fusion, the complete adaptive front end reduces the ATE RMSE by 16.8% and 12.9% on indoor multi-floor and outdoor cycling sequences, and the risk score explains frame-wise tracking quality (correlation of 0.587 with the inlier ratio). The front end runs faster than the baseline (17.1 ms per frame), confirming real-time operation and improved robustness against variable illumination and rapid motion.<\/jats:p>","DOI":"10.3390\/s26185849","type":"journal-article","created":{"date-parts":[[2026,9,15]],"date-time":"2026-09-15T18:23:53Z","timestamp":1789496633000},"page":"5849","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Exposure-Midpoint Temporal Alignment and Risk-Aware Adaptive Feature Tracking for Stereo Visual\u2013Inertial Odometry"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-3106-3462","authenticated-orcid":false,"given":"Sucheng","family":"Yang","sequence":"first","affiliation":[{"name":"School of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-6908-1919","authenticated-orcid":false,"given":"Qingqing","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Electronic and Electrical Engineering, University College London, London WC1E 7JE, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-8776-1846","authenticated-orcid":false,"given":"Zhihong","family":"Zhuang","sequence":"additional","affiliation":[{"name":"School of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3075-2863","authenticated-orcid":false,"given":"Xiaofeng","family":"Shen","sequence":"additional","affiliation":[{"name":"Applied Technology College, Soochow University, Suzhou 215000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1004","DOI":"10.1109\/TRO.2018.2853729","article-title":"VINS-mono: A Robust and Versatile Monocular Visual-Inertial State Estimator","volume":"34","author":"Qin","year":"2018","journal-title":"IEEE Trans. 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