Publikation: Revisiting multi-GNSS Navigation for UA...
Stammdaten
Titel: | Revisiting multi-GNSS Navigation for UAVs - An Equivariant Filtering Approach |
Untertitel: | |
Kurzfassung: | In this work, we explore the recent advances in equivariant filtering for inertial navigation systems to improve state estimation for uncrewed aerial vehicles (UAVs). Traditional state-of-the-art estimation methods, e.g., the multiplicative Kalman filter (MEKF), have some limitations concerning their consistency, errors in the initial state estimate, and convergence performance. Symmetry-based methods, such as the equivariant filter (EqF), offer significant advantages for these points by exploiting the mathematical properties of the system - its symmetry. These filters yield faster convergence rates and robustness to wrong initial state estimates through their error definition. To demonstrate the usability of EqFs, we focus on the sensor-fusion problem with the most common sensors in outdoor robotics: global navigation satellite system (GNSS) sensors and an inertial measurement unit (IMU). We provide an implementation of such an EqF leveraging the semi-direct product of the symmetry group to derive the filter equations. To validate the practical usability of EqFs in real-world scenarios, we evaluate our method using data from all outdoor runs of the INSANE Dataset. Our results demonstrate the performance improvements of the EqF in real-world environments, highlighting its potential for enhancing state estimation for UAVs. |
Schlagworte: |
Publikationstyp: | Beitrag in Proceedings (Autorenschaft) |
Erscheinungsdatum: | 01.02.2024 (Online) |
Erschienen in: |
2023 21st International Conference on Advanced Robotics (ICAR)
2023 21st International Conference on Advanced Robotics (ICAR)
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IEEE;
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zur Publikation |
Titel der Serie: | - |
Bandnummer: | - |
Erstveröffentlichung: | Ja |
Version: | - |
Seite: | S. 134 - 141 |
Versionen
Keine Version vorhanden |
Erscheinungsdatum: | 01.02.2024 |
ISBN (e-book): | - |
eISSN: | 2572-6919 |
DOI: | http://dx.doi.org/10.1109/ICAR58858.2023.10406552 |
Homepage: | https://ieeexplore.ieee.org/document/10406552 |
Open Access |
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AutorInnen
Martin Scheiber (intern) |
Alessandro Fornasier (intern) |
Christian Brommer (intern) |
Stephan Michael Weiss (intern) |
Zuordnung
Organisation | Adresse | ||||
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Fakultät für Technische Wissenschaften
Institut für Intelligente Systemtechnologien
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AT - 9020 Klagenfurt am Wörthersee |
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