Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/117995
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dc.contributorDepartment of Aeronautical and Aviation Engineeringen_US
dc.creatorLiu, Xen_US
dc.creatorWen, Wen_US
dc.creatorHuang, Fen_US
dc.creatorGao, Hen_US
dc.creatorWang, Yen_US
dc.creatorHsu, LTen_US
dc.date.accessioned2026-03-11T06:20:53Z-
dc.date.available2026-03-11T06:20:53Z-
dc.identifier.issn0018-9456en_US
dc.identifier.urihttp://hdl.handle.net/10397/117995-
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineersen_US
dc.rights© 2025 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.en_US
dc.rightsThe following publication X. Liu, W. Wen, F. Huang, H. Gao, Y. Wang and L. -T. Hsu, '3-D LiDAR-Aided GNSS NLOS Mitigation for Reliable GNSS-RTK Positioning in Urban Canyons,' in IEEE Transactions on Instrumentation and Measurement, vol. 74, pp. 1-15, 2025, Art no. 9544915 is available at https://doi.org/10.1109/TIM.2025.3629836.en_US
dc.subjectGeometry distributionen_US
dc.subjectGlobal navigation satellite system (GNSS)-real-time kinematic (RTK)en_US
dc.subjectLight detection and ranging (LiDAR)-aided GNSSen_US
dc.subjectNonline-of-sight (NLOS) exclusionen_US
dc.subjectUrban canyonsen_US
dc.title3-D LiDAR-aided GNSS NLOS mitigation for reliable GNSS-RTK positioning in urban canyonsen_US
dc.typeJournal/Magazine Articleen_US
dc.description.otherinformationTitle on author's file: 3D LiDAR Aided GNSS NLOS Mitigation for Reliable GNSS-RTK Positioning in Urban Canyonsen_US
dc.identifier.volume74en_US
dc.identifier.doi10.1109/TIM.2025.3629836en_US
dcterms.abstractGlobal navigation satellite system (GNSS) real-time kinematic (RTK) and light detection and ranging (LiDAR) odometry are complementary, offering global and local positioning capabilities, respectively. GNSS-RTK/LiDAR integration primarily focuses on two aspects: 1) 3-D LiDAR-aided (3DLA) GNSS nonline-of-sight (NLOS) mitigation using point cloud data and 2) state estimation via tightly coupled GNSS/LiDAR integration, which improves GNSS geometry, yielding better float solutions and lower estimation uncertainty. Combining 3DLA NLOS mitigation with tightly coupled integration can thus enhance urban positioning reliability. However, current 3DLA GNSS-RTK system remains limited in terms of NLOS detection reliability, tightly coupled integration effectiveness, and computational efficiency. This article proposes a tightly coupled GNSS-RTK/LiDAR/INS integration system with 3DLA NLOS mitigation. We focus on improving the robustness of 3DLA NLOS detection, the efficiency, and effectiveness of the tightly coupled GNSS-RTK/LiDAR integration. Evaluations on the challenging UrbanNav dataset show over 70% improvement in positioning accuracy compared to conventional GNSS-RTK, achieving submeter to meter-level precision. Runtime analysis confirms the system supports real-time optimization and ambiguity resolution (AR) with an average computation time of 70 ms.en_US
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationIEEE transactions on instrumentation and measurement, 2025, v. 74, 9544915en_US
dcterms.isPartOfIEEE transactions on instrumentation and measurementen_US
dcterms.issued2025-
dc.identifier.scopus2-s2.0-105023381776-
dc.identifier.eissn1557-9662en_US
dc.identifier.artn9544915en_US
dc.description.validate202603 bcjzen_US
dc.description.oaAccepted Manuscripten_US
dc.identifier.SubFormIDG001191/2026-01-
dc.description.fundingSourceOthersen_US
dc.description.fundingTextThis work was supported in part by Hong Kong Innovation and Technology Fund-Innovation and Technology Support Program (ITF-ITSP) under the Project Safety-Certified Multi-Source Fusion Positioning for Autonomous Vehicles in Complex Scenarios (ZPE8), in part by OttoPoon Charitable Foundation under the Project “Large Vision Model for UAV-UGV Collaborative Map Update (CDCG)”, and in part by the Center for Large Al Models (CLAlM) of The Hong Kong Polytechnic University.en_US
dc.description.pubStatusPublisheden_US
dc.description.oaCategoryGreen (AAM)en_US
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