A comparative analysis of hybrid sensor fusion schemes for visual–inertial navigation

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dc.contributor.authorTabassum, Tarafder Elmi
dc.contributor.authorPetrunin, Ivan
dc.contributor.authorRana, Zeeshan A.
dc.date.accessioned2025-04-17T14:44:15Z
dc.date.available2025-04-17T14:44:15Z
dc.date.freetoread2025-04-17
dc.date.issued2025-12-31
dc.date.pubOnline2025-03-28
dc.description.abstractVisual Inertial Odometry (VIO) has been extensively studied for navigation in GNSS-denied environments, but its performance can be heavily impacted by the complexity of the navigation environments such as weather conditions, illumination variation, flight dynamics, and environmental structure. Hybrid fusion approaches integrating Neural Networks (NN), especially Gated Recurrent units (GRU) with the Kalman filters (KF), such as Error-State Kalman Filter (ESKF) have shown promising results mitigating system nonlinearities due to challenging environmental conditions data issues, there is a lack of systematic studies quantitively analysing and comparing performance differences unhand. To address this gap and enable robust navigation in complex conditions, this study proposes and systematically analyses the performance of three hybrid fusion schemes for VIO-based navigation of Unmanned Aerial Vehicles (UAV). These three hybrid VIO schemes include Visual Odometry (VO) error compensation using NN, KF error compensation using NN, and prediction of Kalman gain using NN. The comparative analysis is performed using data generated in MATLAB incorporating the Unreal Engine involving diverse challenging environmental conditions: fog, rain, illumination level variability and variability in the number of features available for extraction during the UAV flight in the urban environment. The results demonstrate the performance improvement achieved by hybrid VIO fusion schemes compared to ESKF-based traditional fusion methods in the presence of multiple visual failure modes. Comparative analysis reveals notable improvement achieved by method 1 with enhancements of 93% in sunny, 91% in foggy and 90% in rainy conditions than the other two hybrid VIO architectures.
dc.description.journalNameIEEE Transactions on Instrumentation and Measurement
dc.identifier.citationTabassum TE, Petrunin I, Rana ZA. (2025) A comparative analysis of hybrid sensor fusion schemes for visual–inertial navigation. IEEE Transactions on Instrumentation and Measurement, Available online 28 March 2025
dc.identifier.eissn1557-9662
dc.identifier.elementsID567472
dc.identifier.issn0018-9456
dc.identifier.urihttps://doi.org/10.1109/tim.2025.3555758
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/23820
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.publisher.urihttps://ieeexplore.ieee.org/document/10945337
dc.rightsAttribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subject40 Engineering
dc.subject4001 Aerospace Engineering
dc.subjectElectrical & Electronic Engineering
dc.subject4006 Communications engineering
dc.subject4008 Electrical engineering
dc.subject4009 Electronics, sensors and digital hardware
dc.subjectError compensation
dc.subjectfailure modes
dc.subjecthybrid visual-inertial odometry (VIO)
dc.subjectKalman filter (KF)
dc.subjectneural networks (NNs)
dc.subjectrobustness
dc.titleA comparative analysis of hybrid sensor fusion schemes for visual–inertial navigation
dc.typeArticle
dc.type.subtypeJournal Article
dcterms.dateAccepted2025-03-17

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