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Evaluation of clustering methods in compression of topological models and visual place recognition using global appearance descriptors

Research output: Contribution to journalArticle

Original languageEnglish
Article number377
Number of pages30
JournalApplied Sciences (Switzerland)
Issue number3
DateAccepted/In press - 17 Jan 2019
DatePublished (current) - 22 Jan 2019


This paper presents an extended study about the compression of topological models of indoor environments. The performance of two clustering methods is tested in order to know their utility both to build a model of the environment and to solve the localization task. Omnidirectional images are used to create the compact model, as well as to estimate the robot position within the environment. These images are characterized through global appearance descriptors, since they constitute a straightforward mechanism to build a compact model and estimate the robot position. To evaluate the goodness of the proposed clustering algorithms, several datasets are considered. They are composed of either panoramic or omnidirectional images captured in several environments, under real operating conditions. The results confirm that compression of visual information contributes to a more efficient localization process through saving computation time and keeping a relatively good accuracy.

    Research areas

  • Clustering, Global appearance descriptors, Localization, Mapping, Omnidirectional images

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    Licence: CC BY


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