{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,28]],"date-time":"2026-08-28T02:20:23Z","timestamp":1787883623849,"version":"build-2784847793"},"reference-count":26,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2024,11,27]],"date-time":"2024-11-27T00:00:00Z","timestamp":1732665600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Ministry of Trade, Industry and Energy (MOTIE)","award":["P0021515"],"award-info":[{"award-number":["P0021515"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In this study, we propose an enhanced LiDAR-based mapping and localization system that utilizes a camera-based YOLO (You Only Look Once) algorithm to detect and remove dynamic objects, such as vehicles, from the mapping process. GPS, while commonly used for localization, often fails in urban environments due to signal blockages. To address this limitation, our system integrates YOLOv4 with LiDAR, enabling the removal of dynamic objects to improve map accuracy and localization in high-traffic areas. Existing methods using LiDAR segmentation for map matching often suffer from missed detections and false positives, degrading performance. Our approach leverages YOLOv4\u2019s robust object detection capabilities to eliminate potentially dynamic objects while retaining static environmental features, such as buildings, to enhance map accuracy and reliability. The proposed system was validated using a mid-size SUV equipped with LiDAR and camera sensors. The experimental results demonstrate significant improvements in map-matching and localization performance, particularly in urban environments. The system achieved RMSE (Root Mean Square Error) reductions compared to conventional methods, with RMSE values decreasing from 0.9870 to 0.9724 in open areas and from 1.3874 to 1.1217 in urban areas. These findings highlight the ability of the Vision + LiDAR + NDT method to enhance localization performance in both simple and complex environments. By addressing the challenges of dynamic obstacles, the proposed system effectively improves the accuracy and robustness of autonomous navigation in high-traffic settings without relying on GPS.<\/jats:p>","DOI":"10.3390\/s24237578","type":"journal-article","created":{"date-parts":[[2024,11,27]],"date-time":"2024-11-27T10:05:56Z","timestamp":1732701956000},"page":"7578","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Enhancing LiDAR Mapping with YOLO-Based Potential Dynamic Object Removal in Autonomous Driving"],"prefix":"10.3390","volume":"24","author":[{"given":"Seonghark","family":"Jeong","sequence":"first","affiliation":[{"name":"Propulsion Division, GM Korea Company, Incheon 21344, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8839-298X","authenticated-orcid":false,"given":"Heeseok","family":"Shin","sequence":"additional","affiliation":[{"name":"Convergence Major for Intelligent Drone, Sejong University, Seoul 05006, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5348-8068","authenticated-orcid":false,"given":"Myeong-Jun","family":"Kim","sequence":"additional","affiliation":[{"name":"Graduate School of Automotive Mobility, Kookmin University, Seoul 02707, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dongwan","family":"Kang","sequence":"additional","affiliation":[{"name":"Hanwha Aerospace, Seongnam 13488, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3024-2628","authenticated-orcid":false,"given":"Seangwock","family":"Lee","sequence":"additional","affiliation":[{"name":"Graduate School of Automotive Mobility, Kookmin University, Seoul 02707, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sangki","family":"Oh","sequence":"additional","affiliation":[{"name":"Department of Automotive Engineering, Gyeonggi University of Science and Technology, Siheung 15073, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,11,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Suprava, C., Devaraj, E., Padma, L.G., Mohamedm, F.E., Mohammed, M.A., and Salah, K. 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