Integrating GRU with a Kalman filter to enhance visual inertial odometry performance in complex environments

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dc.contributor.authorTabassum, Tarafder Elmi
dc.contributor.authorXu, Zhengjia
dc.contributor.authorPetrunin, Ivan
dc.contributor.authorRana, Zeeshan A.
dc.date.accessioned2023-10-31T10:16:34Z
dc.date.available2023-10-31T10:16:34Z
dc.date.issued2023-10-29
dc.description.abstractTo enhance system reliability and mitigate the vulnerabilities of the Global Navigation Satellite Systems (GNSS), it is common to fuse the Inertial Measurement Unit (IMU) and visual sensors with the GNSS receiver in the navigation system design, effectively enabling compensations with absolute positions and reducing data gaps. To address the shortcomings of a traditional Kalman Filter (KF), such as sensor errors, an imperfect non-linear system model, and KF estimation errors, a GRU-aided ESKF architecture is proposed to enhance the positioning performance. This study conducts Failure Mode and Effect Analysis (FMEA) to prioritize and identify the potential faults in the urban environment, facilitating the design of improved fault-tolerant system architecture. The identified primary fault events are data association errors and navigation environment errors during fault conditions of feature mismatch, especially in the presence of multiple failure modes. A hybrid federated navigation system architecture is employed using a Gated Recurrent Unit (GRU) to predict state increments for updating the state vector in the Error Estate Kalman Filter (ESKF) measurement step. The proposed algorithm’s performance is evaluated in a simulation environment in MATLAB under multiple visually degraded conditions. Comparative results provide evidence that the GRU-aided ESKF outperforms standard ESKF and state-of-the-art solutions like VINS-Mono, End-to-End VIO, and Self-Supervised VIO, exhibiting accuracy improvement in complex environments in terms of root mean square errors (RMSEs) and maximum errors.en_UK
dc.identifier.citationTabassum TE, Xu Z, Petrunin I, Rana ZA. (2023) Integrating GRU with a Kalman filter to enhance visual inertial odometry performance in complex environments, Aerospace, Volume 10, Issue 11, October 2023, Article Number 923en_UK
dc.identifier.issn2226-4310
dc.identifier.urihttps://doi.org/10.3390/aerospace10110923
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/20470
dc.language.isoenen_UK
dc.publisherMDPIen_UK
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectGNSSen_UK
dc.subjectVisual Inertial Odometryen_UK
dc.subjectfailure modesen_UK
dc.subjectGRU-aided ESKFen_UK
dc.subjectcomplex environmentsen_UK
dc.titleIntegrating GRU with a Kalman filter to enhance visual inertial odometry performance in complex environmentsen_UK
dc.typeArticleen_UK

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