Predictive and Adaptive Maps for Long-term Visual Navigation in Changing Environments

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F19%3A00338343" target="_blank" >RIV/68407700:21230/19:00338343 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1109/IROS40897.2019.8967994" target="_blank" >https://doi.org/10.1109/IROS40897.2019.8967994</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/IROS40897.2019.8967994" target="_blank" >10.1109/IROS40897.2019.8967994</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Predictive and Adaptive Maps for Long-term Visual Navigation in Changing Environments

Popis výsledku v původním jazyce

In this paper, we compare different map management techniques for long-term visual navigation in changing environments. In this scenario, the navigation system needs to continuously update and refine its feature map in order to adapt to the environment appearance change. To achieve reliable long-term navigation, the map management techniques have to (i) select features useful for the current navigation task, (ii) remove features that are obsolete, (iii) and add new features from the current camera view to the map. We propose several map management strategies and evaluate their performance with regard to the robot localisation accuracy in long-term teach-and-repeat navigation. Our experiments, performed over three months, indicate that strategies which model cyclic changes of the environment appearance and predict which features are going to be visible at a particular time and location, outperform strategies which do not explicitly model the temporal evolution of the changes.

Název v anglickém jazyce

Predictive and Adaptive Maps for Long-term Visual Navigation in Changing Environments

Popis výsledku anglicky

In this paper, we compare different map management techniques for long-term visual navigation in changing environments. In this scenario, the navigation system needs to continuously update and refine its feature map in order to adapt to the environment appearance change. To achieve reliable long-term navigation, the map management techniques have to (i) select features useful for the current navigation task, (ii) remove features that are obsolete, (iii) and add new features from the current camera view to the map. We propose several map management strategies and evaluate their performance with regard to the robot localisation accuracy in long-term teach-and-repeat navigation. Our experiments, performed over three months, indicate that strategies which model cyclic changes of the environment appearance and predict which features are going to be visible at a particular time and location, outperform strategies which do not explicitly model the temporal evolution of the changes.

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/GJ17-27006Y" target="_blank" >GJ17-27006Y: Prostorově temporální representace pro dlouhodobou navigaci mobilních robotů</a><br>

Návaznosti

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

Rok uplatnění

2019

Kód důvěrnosti údajů

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

Název statě ve sborníku

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

ISBN

978-1-7281-4004-9

ISSN

2153-0858

e-ISSN

2153-0866

Počet stran výsledku

7

Strana od-do

7033-7039

Název nakladatele

IEEE

Místo vydání

Piscataway, NJ

Místo konání akce

Macau

Datum konání akce

4. 11. 2019

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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