Discrete-Time Markovian Jump Linear Robust Filtering for Visual and GPS Aided Magneto-Inertial Navigation
Sensor fusion is a major field in autonomous mobile robots navigation research. These methods integrate information obtained from accelerometers, rate gyros and monocular cameras to provide pose and orientation of the robot, which are known in the literature as Visual-Inertial Simultaneous Localizat...
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10835059/ |
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| author | Kenny A. Q. Caldas Roberto S. Inoue Marco H. Terra Vitor Guizilini Fabio Ramos |
| author_facet | Kenny A. Q. Caldas Roberto S. Inoue Marco H. Terra Vitor Guizilini Fabio Ramos |
| author_sort | Kenny A. Q. Caldas |
| collection | DOAJ |
| description | Sensor fusion is a major field in autonomous mobile robots navigation research. These methods integrate information obtained from accelerometers, rate gyros and monocular cameras to provide pose and orientation of the robot, which are known in the literature as Visual-Inertial Simultaneous Localization and Mapping systems. For outdoor navigation, sensor fusion algorithms may also use magnetometers and GPS modules, since in indoor environments and certain urban areas they may suffer from measurements corruption in the presence of ferromagnetic materials and signal occlusion, respectively. To avoid combining corrupted or noisy data, we propose a Robust Kalman Filter (RKF) for Discrete-time Markovian Jump Linear Systems which estimates the position and orientation of the platform considering the best Markovian mode at each instant. The proposed RKF approach reduces the degradation of the filter performance due to parametric uncertainties present in the system models. We present experimental results in comparison with standard and state-of-the-art sensor fusion techniques to show that our system is robust even in challenging conditions. |
| format | Article |
| id | doaj-art-2205fec173764602843e0314d480ee1a |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-2205fec173764602843e0314d480ee1a2025-01-15T00:02:58ZengIEEEIEEE Access2169-35362025-01-01137590760210.1109/ACCESS.2025.352744910835059Discrete-Time Markovian Jump Linear Robust Filtering for Visual and GPS Aided Magneto-Inertial NavigationKenny A. Q. Caldas0https://orcid.org/0000-0003-4366-3595Roberto S. Inoue1https://orcid.org/0000-0003-2813-9330Marco H. Terra2https://orcid.org/0000-0002-4477-1769Vitor Guizilini3https://orcid.org/0000-0002-8715-8307Fabio Ramos4https://orcid.org/0000-0002-2996-2188Department of Electrical and Computer Engineering, São Carlos School of Engineering, University of São Paulo, São Carlos, BrazilComputer Science Department, Federal University of São Carlos, São Carlos, BrazilDepartment of Electrical and Computer Engineering, São Carlos School of Engineering, University of São Paulo, São Carlos, BrazilToyota Research Institute (TRI), Los Altos, CA, USANVIDIA, USA and School of Computer Science, University of Sydney, Sydney, NSW, AustraliaSensor fusion is a major field in autonomous mobile robots navigation research. These methods integrate information obtained from accelerometers, rate gyros and monocular cameras to provide pose and orientation of the robot, which are known in the literature as Visual-Inertial Simultaneous Localization and Mapping systems. For outdoor navigation, sensor fusion algorithms may also use magnetometers and GPS modules, since in indoor environments and certain urban areas they may suffer from measurements corruption in the presence of ferromagnetic materials and signal occlusion, respectively. To avoid combining corrupted or noisy data, we propose a Robust Kalman Filter (RKF) for Discrete-time Markovian Jump Linear Systems which estimates the position and orientation of the platform considering the best Markovian mode at each instant. The proposed RKF approach reduces the degradation of the filter performance due to parametric uncertainties present in the system models. We present experimental results in comparison with standard and state-of-the-art sensor fusion techniques to show that our system is robust even in challenging conditions.https://ieeexplore.ieee.org/document/10835059/Inertial navigation systemMarkovian jump linear systemsmulti-sensor fusionrobust Kalman filtervisual SLAM |
| spellingShingle | Kenny A. Q. Caldas Roberto S. Inoue Marco H. Terra Vitor Guizilini Fabio Ramos Discrete-Time Markovian Jump Linear Robust Filtering for Visual and GPS Aided Magneto-Inertial Navigation IEEE Access Inertial navigation system Markovian jump linear systems multi-sensor fusion robust Kalman filter visual SLAM |
| title | Discrete-Time Markovian Jump Linear Robust Filtering for Visual and GPS Aided Magneto-Inertial Navigation |
| title_full | Discrete-Time Markovian Jump Linear Robust Filtering for Visual and GPS Aided Magneto-Inertial Navigation |
| title_fullStr | Discrete-Time Markovian Jump Linear Robust Filtering for Visual and GPS Aided Magneto-Inertial Navigation |
| title_full_unstemmed | Discrete-Time Markovian Jump Linear Robust Filtering for Visual and GPS Aided Magneto-Inertial Navigation |
| title_short | Discrete-Time Markovian Jump Linear Robust Filtering for Visual and GPS Aided Magneto-Inertial Navigation |
| title_sort | discrete time markovian jump linear robust filtering for visual and gps aided magneto inertial navigation |
| topic | Inertial navigation system Markovian jump linear systems multi-sensor fusion robust Kalman filter visual SLAM |
| url | https://ieeexplore.ieee.org/document/10835059/ |
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