{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,31]],"date-time":"2025-10-31T07:57:57Z","timestamp":1761897477491,"version":"build-2065373602"},"reference-count":42,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2021,4,16]],"date-time":"2021-04-16T00:00:00Z","timestamp":1618531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Horizon 2020","award":["687458"],"award-info":[{"award-number":["687458"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In this work, we propose and evaluate a pose-graph optimization-based real-time multi-sensor fusion framework for vehicle positioning using low-cost automotive-grade sensors. Pose-graphs can model multiple absolute and relative vehicle positioning sensor measurements and can be optimized using nonlinear techniques. We model pose-graphs using measurements from a precise stereo camera-based visual odometry system, a robust odometry system using the in-vehicle velocity and yaw-rate sensor, and an automotive-grade GNSS receiver. Our evaluation is based on a dataset with 180 km of vehicle trajectories recorded in highway, urban, and rural areas, accompanied by postprocessed Real-Time Kinematic GNSS as ground truth. We compare the architecture\u2019s performance with (i) vehicle odometry and GNSS fusion and (ii) stereo visual odometry, vehicle odometry, and GNSS fusion; for offline and real-time optimization strategies. The results exhibit a 20.86% reduction in the localization error\u2019s standard deviation and a significant reduction in outliers when compared with automotive-grade GNSS receivers.<\/jats:p>","DOI":"10.3390\/s21082815","type":"journal-article","created":{"date-parts":[[2021,4,19]],"date-time":"2021-04-19T06:35:53Z","timestamp":1618814153000},"page":"2815","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Real-Time Vehicle Positioning and Mapping Using Graph Optimization"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4008-7123","authenticated-orcid":false,"given":"Anweshan","family":"Das","sequence":"first","affiliation":[{"name":"Signal Processing Systems Group, Department of Electrical Engineering, University of Eindhoven, 5600 MB Eindhoven, The Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jos","family":"Elfring","sequence":"additional","affiliation":[{"name":"Control Systems Technology Group, Department of Mechanical Engineering, University of Eindhoven, 5600 MB Eindhoven, The Netherlands"},{"name":"Product Unit Autonomous Driving, TomTom, 1011 AC Amsterdam, The Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gijs","family":"Dubbelman","sequence":"additional","affiliation":[{"name":"Signal Processing Systems Group, Department of Electrical Engineering, University of Eindhoven, 5600 MB Eindhoven, The Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,4,16]]},"reference":[{"key":"ref_1","unstructured":"SAE, J3016 (2014). 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