Fast and Robust Teach-and-Repeat Navigation Using MixVPR Visual Place Recognition

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21730%2F25%3A00385752" target="_blank" >RIV/68407700:21730/25:00385752 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1109/ECMR65884.2025.11163381" target="_blank" >https://doi.org/10.1109/ECMR65884.2025.11163381</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ECMR65884.2025.11163381" target="_blank" >10.1109/ECMR65884.2025.11163381</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Fast and Robust Teach-and-Repeat Navigation Using MixVPR Visual Place Recognition

Popis výsledku v původním jazyce

Teach-and-repeat navigation systems employing advanced visual place recognition techniques for localization exhibit key attributes for long-term mobile robot navigation, such as the ability to operate in unstructured and dynamic environments. However, existing solutions based on deep-learning techniques have high computational requirements, limiting their applicability. This work introduces a novel and efficient teachand- repeat system built on the modern visual place recognition method MixVPR. Real-world testing demonstrated its ability to operate both indoors and outdoors, achieving robustness and navigation precision comparable to other state-of-the-art systems. In addition, its lower hardware requirements make it suitable for a wide range of robotic platforms and practical applications.

Název v anglickém jazyce

Fast and Robust Teach-and-Repeat Navigation Using MixVPR Visual Place Recognition

Popis výsledku anglicky

Teach-and-repeat navigation systems employing advanced visual place recognition techniques for localization exhibit key attributes for long-term mobile robot navigation, such as the ability to operate in unstructured and dynamic environments. However, existing solutions based on deep-learning techniques have high computational requirements, limiting their applicability. This work introduces a novel and efficient teachand- repeat system built on the modern visual place recognition method MixVPR. Real-world testing demonstrated its ability to operate both indoors and outdoors, achieving robustness and navigation precision comparable to other state-of-the-art systems. In addition, its lower hardware requirements make it suitable for a wide range of robotic platforms and practical applications.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20204 - Robotics and automatic control

Projekt

<a href="/cs/project/EH22_008%2F0004590" target="_blank" >EH22_008/0004590: Robotika a pokročilá průmyslová výroba</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2025

Kód důvěrnosti údajů

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

Název statě ve sborníku

2025 European Conference on Mobile Robots Conference Proceedings

ISBN

979-8-3315-2705-1

ISSN

2639-7919

e-ISSN

2767-8733

Počet stran výsledku

6

Strana od-do

1-6

Název nakladatele

IEEE

Místo vydání

—

Místo konání akce

Padua

Datum konání akce

2. 9. 2025

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

001592487100090