Benchmarking 6DOF Outdoor Visual Localization in Changing Conditions

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21730%2F18%3A00328078" target="_blank" >RIV/68407700:21730/18:00328078 - isvavai.cz</a>

Výsledek na webu

<a href="https://ieeexplore.ieee.org/document/8578995" target="_blank" >https://ieeexplore.ieee.org/document/8578995</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/CVPR.2018.00897" target="_blank" >10.1109/CVPR.2018.00897</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Benchmarking 6DOF Outdoor Visual Localization in Changing Conditions

Popis výsledku v původním jazyce

Visual localization enables autonomous vehicles to navigate in their surroundings and augmented reality applications to link virtual to real worlds. Practical visual localization approaches need to be robust to a wide variety of viewing condition, including day-night changes, as well as weather and seasonal variations, while providing highly accurate 6 degree-of-freedom (6DOF) camera pose estimates. In this paper, we introduce the first benchmark datasets specifically designed for analyzing the impact of such factors on visual localization. Using carefully created ground truth poses for query images taken under a wide variety of conditions, we evaluate the impact of various factors on 6DOF camera pose estimation accuracy through extensive experiments with state-of-the-art localization approaches. Based on our results, we draw conclusions about the difficulty of different conditions, showing that long-term localization is far from solved, and propose promising avenues for future work, including sequence-based localization approaches and the need for better local features. Our benchmark is available at visuallocalization.net.

Název v anglickém jazyce

Benchmarking 6DOF Outdoor Visual Localization in Changing Conditions

Popis výsledku anglicky

Visual localization enables autonomous vehicles to navigate in their surroundings and augmented reality applications to link virtual to real worlds. Practical visual localization approaches need to be robust to a wide variety of viewing condition, including day-night changes, as well as weather and seasonal variations, while providing highly accurate 6 degree-of-freedom (6DOF) camera pose estimates. In this paper, we introduce the first benchmark datasets specifically designed for analyzing the impact of such factors on visual localization. Using carefully created ground truth poses for query images taken under a wide variety of conditions, we evaluate the impact of various factors on 6DOF camera pose estimation accuracy through extensive experiments with state-of-the-art localization approaches. Based on our results, we draw conclusions about the difficulty of different conditions, showing that long-term localization is far from solved, and propose promising avenues for future work, including sequence-based localization approaches and the need for better local features. Our benchmark is available at visuallocalization.net.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20204 - Robotics and automatic control

Projekt

<a href="/cs/project/EF15_003%2F0000468" target="_blank" >EF15_003/0000468: Inteligentní strojové vnímání</a><br>

Návaznosti

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

Rok uplatnění

2018

Kód důvěrnosti údajů

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

Název statě ve sborníku

CVPR 2018: Proceedings of the 2018 IEEE Conference on Computer Vision and Pattern Recognition

ISBN

978-1-5386-6420-9

ISSN

1063-6919

e-ISSN

2575-7075

Počet stran výsledku

10

Strana od-do

8601-8610

Název nakladatele

IEEE

Místo vydání

Piscataway, NJ

Místo konání akce

Salt Lake City

Datum konání akce

19. 6. 2018

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000457843608080