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
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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
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
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UT code for WoS article
000595253700003
EID of the result in the Scopus database
2-s2.0-85095916049