{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:03:36Z","timestamp":1760144616132,"version":"build-2065373602"},"reference-count":29,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2024,5,12]],"date-time":"2024-05-12T00:00:00Z","timestamp":1715472000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>This paper presents an enhanced ground vehicle localization method designed to address the challenges associated with state estimation for autonomous vehicles operating in diverse environments. The focus is specifically on the precise localization of position and orientation in both local and global coordinate systems. The proposed approach integrates local estimates generated by existing visual\u2013inertial odometry (VIO) methods into global position information obtained from the Global Navigation Satellite System (GNSS). This integration is achieved through optimizing fusion in a pose graph, ensuring precise local estimation and drift-free global position estimation. Considering the inherent complexities in autonomous driving scenarios, such as the potential failures of a visual\u2013inertial navigation system (VINS) and restrictions on GNSS signals in urban canyons, leading to disruptions in localization outcomes, we introduce an adaptive fusion mechanism. This mechanism allows seamless switching between three modes: utilizing only VINS, using only GNSS, and normal fusion. The effectiveness of the proposed algorithm is demonstrated through rigorous testing in the Carla simulation environment and challenging UrbanNav scenarios. The evaluation includes both qualitative and quantitative analyses, revealing that the method exhibits robustness and accuracy.<\/jats:p>","DOI":"10.3390\/s24103079","type":"journal-article","created":{"date-parts":[[2024,5,13]],"date-time":"2024-05-13T11:18:17Z","timestamp":1715599097000},"page":"3079","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["A Vision\/Inertial Navigation\/Global Navigation Satellite Integrated System for Relative and Absolute Localization in Land Vehicles"],"prefix":"10.3390","volume":"24","author":[{"given":"Yao","family":"Zhang","sequence":"first","affiliation":[{"name":"National Key Laboratory of Automotive Chassis Integration and Bionics, Jilin University, Changchun 130025, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liang","family":"Chu","sequence":"additional","affiliation":[{"name":"National Key Laboratory of Automotive Chassis Integration and Bionics, Jilin University, Changchun 130025, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yabin","family":"Mao","sequence":"additional","affiliation":[{"name":"National Key Laboratory of Automotive Chassis Integration and Bionics, Jilin University, Changchun 130025, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xintong","family":"Yu","sequence":"additional","affiliation":[{"name":"China FAW Group Co., Ltd., Changchun 130000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiawei","family":"Wang","sequence":"additional","affiliation":[{"name":"National Key Laboratory of Automotive Chassis Integration and Bionics, Jilin University, Changchun 130025, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2068-908X","authenticated-orcid":false,"given":"Chong","family":"Guo","sequence":"additional","affiliation":[{"name":"National Key Laboratory of Automotive Chassis Integration and Bionics, Jilin University, Changchun 130025, China"},{"name":"Changsha Automobile Innovation Research Institute, Changsha 410005, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,5,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Wan, G., Yang, X., Cai, R., Li, H., Zhou, Y., Wang, H., and Song, S. 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