Contrastive Learning for Image Registration in Visual 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%3A00357942" target="_blank" >RIV/68407700:21230/22:00357942 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.3390/s22082975" target="_blank" >https://doi.org/10.3390/s22082975</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.3390/s22082975" target="_blank" >10.3390/s22082975</a>
Alternative languages
Result language
angličtina
Original language name
Contrastive Learning for Image Registration in Visual Teach and Repeat Navigation
Original language description
Visual teach and repeat navigation (VT&R) is popular in robotics thanks to its simplicity and versatility. It enables mobile robots equipped with a camera to traverse learned paths without the need to create globally consistent metric maps. Although teach and repeat frameworks have been reported to be relatively robust to changing environments, they still struggle with day-to-night and seasonal changes. This paper aims to find the horizontal displacement between prerecorded and currently perceived images required to steer a robot towards the previously traversed path. We employ a fully convolutional neural network to obtain dense representations of the images that are robust to changes in the environment and variations in illumination. The proposed model achieves state-of-the-art performance on multiple datasets with seasonal and day/night variations. In addition, our experiments show that it is possible to use the model to generate additional training examples that can be used to further improve the original model's robustness. We also conducted a real-world experiment on a mobile robot to demonstrate the suitability of our method for VT&R.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
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
Result was created during the realization of more than one project. More information in the Projects tab.
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
Name of the periodical
Sensors
ISSN
1424-8220
e-ISSN
1424-8220
Volume of the periodical
22
Issue of the periodical within the volume
8
Country of publishing house
CH - SWITZERLAND
Number of pages
19
Pages from-to
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UT code for WoS article
000787016500001
EID of the result in the Scopus database
2-s2.0-85128241406