Robust and Long-term Monocular Teach and Repeat Navigation using a Single-experience Map

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F21%3A00357706" target="_blank" >RIV/68407700:21230/21:00357706 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1109/IROS51168.2021.9635886" target="_blank" >https://doi.org/10.1109/IROS51168.2021.9635886</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/IROS51168.2021.9635886" target="_blank" >10.1109/IROS51168.2021.9635886</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Robust and Long-term Monocular Teach and Repeat Navigation using a Single-experience Map

Popis výsledku v původním jazyce

This paper presents a robust monocular visual teach-and-repeat (VT&R) navigation system for long-term operation in outdoor environments. The approach leverages deep-learned descriptors to deal with the high illumination variance of the real world. In particular, a tailored self-supervised descriptor, DarkPoint, is proposed for autonomous navigation in outdoor environments. We seamlessly integrate the localisation with control, in which proportional-integral control is used to eliminate the visual error with the pitfall of the unknown depth. Consequently, our approach achieves day-to-night navigation using a single-experience map and is able to repeat complex and fast manoeuvres. To verify our approach, we performed a vast array of navigation experiments in various outdoor environments, where both navigation accuracy and robustness of the proposed system are investigated. The experimental results show that our approach is superior to the baseline method with regards to accuracy and robustness.

Název v anglickém jazyce

Robust and Long-term Monocular Teach and Repeat Navigation using a Single-experience Map

Popis výsledku anglicky

This paper presents a robust monocular visual teach-and-repeat (VT&R) navigation system for long-term operation in outdoor environments. The approach leverages deep-learned descriptors to deal with the high illumination variance of the real world. In particular, a tailored self-supervised descriptor, DarkPoint, is proposed for autonomous navigation in outdoor environments. We seamlessly integrate the localisation with control, in which proportional-integral control is used to eliminate the visual error with the pitfall of the unknown depth. Consequently, our approach achieves day-to-night navigation using a single-experience map and is able to repeat complex and fast manoeuvres. To verify our approach, we performed a vast array of navigation experiments in various outdoor environments, where both navigation accuracy and robustness of the proposed system are investigated. The experimental results show that our approach is superior to the baseline method with regards to accuracy and robustness.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/GC20-27034J" target="_blank" >GC20-27034J: Rozšíření prostorových modelů explicitní representací času pro dlouhodobou autonomii mobilních robotů</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2021

Kód důvěrnosti údajů

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

Název statě ve sborníku

2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

ISBN

978-1-6654-1714-3

ISSN

2153-0858

e-ISSN

2153-0866

Počet stran výsledku

8

Strana od-do

2635-2642

Název nakladatele

IEEE

Místo vydání

Piscataway

Místo konání akce

Praha

Datum konání akce

27. 9. 2021

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000755125502025