A Framework for the Consistency Analysis of Relative Pose Sensors for Unmanned Aerial Vehicles (Uavs)

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F25%3A00384666" target="_blank" >RIV/68407700:21230/25:00384666 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1109/ICUAS65942.2025.11007891" target="_blank" >https://doi.org/10.1109/ICUAS65942.2025.11007891</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/ICUAS65942.2025.11007891" target="_blank" >10.1109/ICUAS65942.2025.11007891</a>

Result language
angličtina
Original language name
A Framework for the Consistency Analysis of Relative Pose Sensors for Unmanned Aerial Vehicles (Uavs)
Original language description
In autonomous multi-robot systems robot-to {robot/object} localization methods can be utilized to increase the robustness and to achieve a precise and robust localization of the individuals. This paper investigates on the performance of two promising systems: UVDAR, a vision-based mutual localization in the UV spectrum, which has shown to be effective in swarm formation and leader-following tasks, and PoET, which is a deep learning-based visual relative object pose estimator. To evaluate these methods, we collected datasets in a controlled indoor environment equipped with a motion capture system for precise ground truth measurements. Our evaluation considers two key aspects: the absolute error between measured and true relative poses, and the consistency of the provided measurement uncertainty estimates with the actual errors. We introduce a novel framework for evaluating the consistency of relative pose measurements. This framework supports various error definitions and leverages spline-based trajectory representations to generate smooth, C2-continuous reference measurements. Both the UVDAR dataset and the evaluation framework are made publicly accessible to foster further research and development in this field.
Czech name
—
Czech description
—

Project
—
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
2025 International Conference on Unmanned Aircraft Systems (ICUAS)
ISBN
979-8-3315-1328-3
ISSN
2373-6720
e-ISSN
2575-7296
Number of pages
8
Pages from-to
817-824
Publisher name
IEEE Xplore
Place of publication
—
Event location
Charlotte, NC
Event date
May 14, 2025
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
001548686600108

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