DC FieldValueLanguage
dc.contributorDepartment of Aeronautical and Aviation Engineeringen_US
dc.creatorBai, Xen_US
dc.creatorWen, Wen_US
dc.creatorHsu, Len_US
dc.date.accessioned2021-11-19T01:30:19Z-
dc.date.available2021-11-19T01:30:19Z-
dc.identifier.issn0018-9456en_US
dc.identifier.urihttp://hdl.handle.net/10397/91611-
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineersen_US
dc.rights© 2021 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.||The following publication X. Bai, W. Wen and L. -T. Hsu, "Degeneration-Aware Outlier Mitigation for Visual Inertial Integrated Navigation System in Urban Canyons," in IEEE Transactions on Instrumentation and Measurement, vol. 70, pp. 1-15, 2021, Art no. 5019915 is available at https://doi.org/10.1109/TIM.2021.3126010en_US
dc.subjectDynamicsen_US
dc.subjectFeature extractionen_US
dc.subjectGNCen_US
dc.subjectNavigationen_US
dc.subjectOptical flowen_US
dc.subjectOptimization methoden_US
dc.subjectOutlier measurementsen_US
dc.subjectTrackingen_US
dc.subjectUrban canyonsen_US
dc.subjectVehicle dynamicsen_US
dc.subjectVINSen_US
dc.subjectVisual odometryen_US
dc.subjectVisualizationen_US
dc.titleDegeneration-aware outlier mitigation for visual inertial integrated navigation system in urban canyonsen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.doi10.1109/TIM.2021.3126010en_US
dcterms.abstractIn this paper, we proposed a graduated non-convexity (GNC) aided outlier mitigation method for the improvement of the visual-inertial integrated navigation system (VINS) to face the challenge of dynamic environments with numerous unexpected outlier measurements. A GNC optical flow algorithm was proposed for the detection of the outliers of feature tracking in the front-end of VINS by iteratively estimating the optical flow and the optimal weightings of feature correspondences. Then the feature correspondences with small weightings were excluded. However, excessive outlier exclusion may cause insufficient constraints on the state, causing degeneration of VINS. To solve the problem, this paper proposed to detect the potential degeneration based on the degree of constraint in different directions of the pose estimation. Then the number of features being considered was intelligently adapted based on the degeneration level to improve the geometry constraint in the coming epochs. We evaluated the effectiveness of the proposed method by using two challenging datasets (including challenging night scenarios) collected in urban canyons of Hong Kong. The results show that the proposed method can effectively reject the potential outlier visual measurements, and alleviate the degeneration, leading to improved positioning performance in both evaluated datasets.en_US
dcterms.bibliographicCitationIEEE transactions on instrumentation and measurement, 2021en_US
dcterms.isPartOfIEEE transactions on instrumentation and measurementen_US
dcterms.issued2021-
dc.identifier.scopus2-s2.0-85118984339-
dc.identifier.eissn1557-9662en_US
dc.description.validate202111 bchyen_US
dc.description.oaNot applicableen_US
dc.identifier.FolderNumbera1087-n01-
dc.identifier.SubFormID43925-
dc.description.fundingSourceOthersen_US
dc.description.fundingTextBD63en_US
dc.description.pubStatusPublisheden_US
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