{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,25]],"date-time":"2026-02-25T10:29:24Z","timestamp":1772015364541,"version":"3.50.1"},"reference-count":55,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2026,2,18]],"date-time":"2026-02-18T00:00:00Z","timestamp":1771372800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the Open Foundation of the State Key Laboratory of Precision Space-time Information Sensing Technology","award":["STL2023-B-06-01(K)"],"award-info":[{"award-number":["STL2023-B-06-01(K)"]}]},{"name":"the Academic Research Projects of Beijing Union University","award":["ZK20202201"],"award-info":[{"award-number":["ZK20202201"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>Accurate and robust visual localization under changing environments remains a fundamental challenge in autonomous driving and mobile robotics. Traditional handcrafted features often degrade under long-term illumination and viewpoint variations, while recent CNN-based methods, although more robust, typically rely on coarse semantic cues and remain vulnerable to dynamic objects. In this paper, we propose a fine-grained semantics-guided feature extraction framework that adaptively selects stable keypoints while suppressing dynamic disturbances. A fine-grained semantic refinement module subdivides coarse semantic categories into stability-homogeneous sub-classes, and a dual-attention mechanism enhances local repeatability and semantic consistency. By integrating physical priors with self-supervised clustering, the proposed framework learns discriminative and reliable feature representations. Extensive experiments on the Aachen and RobotCar-Seasons benchmarks demonstrate that the proposed approach achieves state-of-the-art accuracy and robustness while maintaining real-time efficiency, effectively bridging coarse semantic guidance with fine-grained stability estimation. Quantitatively, our method achieves strong localization performance on Aachen (up to 88.1% at night under the (0.2\u00b0,0.25\u00a0m) threshold) and on RobotCar-Seasons (up to 57.2%\/28.4% under the same threshold for day\/night), demonstrating improved robustness to seasonal and illumination changes.<\/jats:p>","DOI":"10.3390\/jimaging12020085","type":"journal-article","created":{"date-parts":[[2026,2,18]],"date-time":"2026-02-18T11:45:58Z","timestamp":1771415158000},"page":"85","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["SREF: Semantics-Refined Feature Extraction for Long-Term Visual Localization"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2824-0909","authenticated-orcid":false,"given":"Danfeng","family":"Wu","sequence":"first","affiliation":[{"name":"Beijing Key Laboratory of Information Service Engineering, Beijing Union University, Beijing 100101, China"},{"name":"College of Robotics, Beijing Union University, Beijing 100101, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-3545-9244","authenticated-orcid":false,"given":"Kaifeng","family":"Zhu","sequence":"additional","affiliation":[{"name":"Beijing Key Laboratory of Information Service Engineering, Beijing Union University, Beijing 100101, China"},{"name":"College of Robotics, Beijing Union University, Beijing 100101, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3314-927X","authenticated-orcid":false,"given":"Heng","family":"Shi","sequence":"additional","affiliation":[{"name":"Department of Precision Instrument, Tsinghua University, Beijing 100084, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6679-593X","authenticated-orcid":false,"given":"Fenfen","family":"Zhou","sequence":"additional","affiliation":[{"name":"Beijing Key Laboratory of Information Service Engineering, Beijing Union University, Beijing 100101, China"},{"name":"College of Robotics, Beijing Union University, Beijing 100101, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8082-539X","authenticated-orcid":false,"given":"Minchi","family":"Kuang","sequence":"additional","affiliation":[{"name":"Department of Precision Instrument, Tsinghua University, Beijing 100084, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,2,18]]},"reference":[{"key":"ref_1","unstructured":"Harris, C., and Stephens, M. 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