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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

  • Czech description

Classification

  • Type

    J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database

  • CEP classification

  • 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

  • UT code for WoS article

    000787016500001

  • EID of the result in the Scopus database

    2-s2.0-85128241406