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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

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • 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

    <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

  • e-ISSN

  • 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