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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20204 - Robotics and automatic control

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

2025 International Conference on Unmanned Aircraft Systems (ICUAS)

ISBN

979-8-3315-1328-3

ISSN

2373-6720

e-ISSN

2575-7296

Počet stran výsledku

8

Strana od-do

817-824

Název nakladatele

IEEE Xplore

Místo vydání

—

Místo konání akce

Charlotte, NC

Datum konání akce

14. 5. 2025

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

001548686600108