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Change detection using weighted features for image-based localization

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F21%3A00344049" target="_blank" >RIV/68407700:21230/21:00344049 - isvavai.cz</a>

  • Alternative codes found

    RIV/68407700:21730/21:00344049

  • Result on the web

    <a href="https://doi.org/10.1016/j.robot.2020.103676" target="_blank" >https://doi.org/10.1016/j.robot.2020.103676</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1016/j.robot.2020.103676" target="_blank" >10.1016/j.robot.2020.103676</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Change detection using weighted features for image-based localization

  • Original language description

    Autonomous mobile robots are becoming increasingly important in many industrial and domesticenvironments.Dealingwithunforeseensituationsisadifficultproblemthatmustbetackledtoachievelong-term robot autonomy. In vision-based localization and navigation methods, one of the majorissues is the scene dynamics. The autonomous operation of the robot may become unreliable if thechanges occurring in dynamic environments are not detected and managed. Moving chairs, openingand closing doors or windows, replacing objects and other changes make many conventional methodsfail. To deal with these challenges, we present a novel method for change detection based on weightedlocal visual features. The core idea of the algorithm is to distinguish the valuable information in stableregions of the scene from the potentially misleading information in the regions that are changing. Weevaluate the change detection algorithm in a visual localization framework based on feature matchingby performing a series of long-term localization experiments in various real-world environments. Theresults show that the change detection method yields an improvement in the localization accuracy,compared to the baseline method without change detection. In addition, an experimental evaluationon a public long-term localization data set with more than 10000 images reveals that the proposedmethod outperforms two alternative localization methods on images recorded several months afterthe initial mapping

  • 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

    20204 - Robotics and automatic control

Result continuities

  • Project

    <a href="/en/project/EF15_003%2F0000470" target="_blank" >EF15_003/0000470: Robotics 4 Industry 4.0</a><br>

  • Continuities

    P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Others

  • Publication year

    2021

  • Confidentiality

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

Data specific for result type

  • Name of the periodical

    Robotics and Autonomous Systems

  • ISSN

    0921-8890

  • e-ISSN

    1872-793X

  • Volume of the periodical

    135

  • Issue of the periodical within the volume

    January

  • Country of publishing house

    US - UNITED STATES

  • Number of pages

    14

  • Pages from-to

  • UT code for WoS article

    000595253700003

  • EID of the result in the Scopus database

    2-s2.0-85095916049