Master data

Title: UVIO: An UWB-Aided Visual-Inertial Odometry Framework with Bias-Compensated Anchors Initialization
Subtitle:
Abstract:

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.

Keywords: Location awareness, Uncertainty, Autonomous aerial vehicles, Real-time systems, Trajectory, Odometry, Reliability
Publication type: Article in Proceedings (Authorship)
Publication date: 13.12.2023 (Online)
Published by: 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)
to publication
 ( IEEE; )
Title of the series: -
Volume number: -
First publication: Yes
Version: -
Page: -

Versionen

Keine Version vorhanden
Publication date: 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
  • Available online (not open access)

Assignment

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

Categorisation

Subject areas
  • 102003 - Image processing
  • 202035 - Robotics
  • 202037 - Signal processing
Research Cluster
  • Self-organizing systems
Peer reviewed
  • Yes
Publication focus
  • Science to Science (Quality indicator: I)
Classification raster of the assigned organisational units:
working groups
  • Control of Networked Systems

Cooperations

Organisation Address
Università degli Studi di Trento
38122 Trento
Italy - Alpine-Adriatic region, particularly Friuli Venezia Giulia, Venezia, Trentino-Alto Adige
IT - 38122  Trento

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