Semi-supervised Learning for Image Alignment in Teach and Repeat Navigation
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F22%3A00361048" target="_blank" >RIV/68407700:21230/22:00361048 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1145/3477314.3507045" target="_blank" >https://doi.org/10.1145/3477314.3507045</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1145/3477314.3507045" target="_blank" >10.1145/3477314.3507045</a>
Alternative languages
Result language
angličtina
Original language name
Semi-supervised Learning for Image Alignment in Teach and Repeat Navigation
Original language description
Visual teach and repeat navigation (VT&R) is a framework that enables mobile robots to traverse previously learned paths. In principle, it relies on computer vision techniques that can compare the camera's current view to a model based on the images captured during the teaching phase. However, these techniques are usually not robust enough when significant changes occur in the environment between the teach and repeat phases. In this paper, we show that contrastive learning methods can learn how the environment changes and improve the robustness of a VT&R framework. We apply a fully convolutional Siamese network to register the images of the teaching and repeat phases. Their horizontal displacement between the images is then used in a visual servoing manner to keep the robot on the intended trajectory. The experiments performed on several datasets containing seasonal variations indicate that our method outperforms state-of-the-art algorithms tailored to the purpose of registering images captured in different seasons.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
<a href="/en/project/GC20-27034J" target="_blank" >GC20-27034J: Towards long-term autonomy through introduction of the temporal domain into spatial representations used in robotics</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Proceedings of the 37th ACM/SIGAPP Symposium on Applied Computing
ISBN
978-1-4503-8713-2
ISSN
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e-ISSN
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Number of pages
8
Pages from-to
731-738
Publisher name
ACM
Place of publication
New York
Event location
Virtual
Event date
Apr 25, 2022
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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