Stammdaten

Titel: UVIO: An UWB-Aided Visual-Inertial Odometry Framework with Bias-Compensated Anchors Initialization
Untertitel:
Kurzfassung:

This paper introduces UVIO, a multi-sensor framework that leverages Ultra Wide Band (UWB) technology and Visual-Inertial Odometry (VIO) to provide robust and low-drift localization. In order to include range measurements in state estimation, the position of the UWB anchors must be known. This study proposes a multi-step initialization procedure to map multiple unknown anchors by an Unmanned Aerial Vehicle (UAV), in a fully autonomous fashion. To address the limitations of initializing UWB anchors via a random trajectory, this paper uses the Geometric Dilution of Precision (GDOP) as a measure of optimality in anchor position estimation, to compute a set of optimal waypoints and synthesize a trajectory that minimizes the mapping uncertainty. After the initialization is complete, the range measurements from multiple anchors, including measurement biases, are tightly integrated into the VIO system. While in range of the initialized anchors, the VIO drift in position and heading is eliminated. The effectiveness of UVIO and our initialization procedure has been validated through a series of simulations and real-world experiments.

Schlagworte: Location awareness, Uncertainty, Autonomous aerial vehicles, Real-time systems, Trajectory, Odometry, Reliability
Publikationstyp: Beitrag in Proceedings (Autorenschaft)
Erscheinungsdatum: 13.12.2023 (Online)
Erschienen in: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
zur Publikation
 ( IEEE; )
Titel der Serie: -
Bandnummer: -
Erstveröffentlichung: Ja
Version: -
Seite: -

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Keine Version vorhanden
Erscheinungsdatum: 13.12.2023
ISBN (e-book):
  • 978-1-6654-9190-7
eISSN: 2153-0866
DOI: http://dx.doi.org/10.1109/IROS55552.2023.10342012
Homepage: https://ieeexplore.ieee.org/document/10342012
Open Access
  • Online verfügbar (nicht Open Access)

Zuordnung

Organisation Adresse
Fakultät für Technische Wissenschaften
 
Institut für Intelligente Systemtechnologien
Universitätsstraße 65-67
9020 Klagenfurt am Wörthersee
Österreich
   hubert.zangl@aau.at
http://www.uni-klu.ac.at/tewi/ict/sst/index.html
zur Organisation
Universitätsstraße 65-67
AT - 9020  Klagenfurt am Wörthersee

Kategorisierung

Sachgebiete
  • 102003 - Bildverarbeitung
  • 202035 - Robotik
  • 202037 - Signalverarbeitung
Forschungscluster
  • Selbstorganisierende Systeme
Peer Reviewed
  • Ja
Publikationsfokus
  • Science to Science (Qualitätsindikator: I)
Klassifikationsraster der zugeordneten Organisationseinheiten:
Arbeitsgruppen
  • Control of Networked Systems

Kooperationen

Organisation Adresse
Università degli Studi di Trento
38122 Trento
Italien - Alpen-Adria-Raum, insbes. Friaul-Julisch-Venetien, Venetien, Trentino-Südtirol
IT - 38122  Trento

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