Kazan (Volga region) Federal University, KFU
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FEDERAL UNIVERSITY
 
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COMPARING FIDUCIAL MARKERS PERFORMANCE FOR A TASK OF A HUMANOID ROBOT SELF-CALIBRATION OF MANIPULATORS: A PILOT EXPERIMENTAL STUDY
Form of presentationArticles in international journals and collections
Year of publication2018
Языканглийский
  • Magid Evgeniy Arkadevich, author
  • Sagitov Artur Gazizovich, author
  • Shabalina Kseniya Sergeevna, author
  • Sagitov Artur Gazizovich, postgraduate kfu
  • Shabalina Kseniya Sergeevna, author
  • Bibliographic description in the original language Shabalina K, Sagitov A, Svinin M, Magid E. Comparing fiducial markers performance for a task of a humanoid robot self-calibration of manipulators: A pilot experimental study//Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). - 2018. - Vol.11097 LNAI, Is.. - P.249-258.
    Annotation This paper presents our pilot study of experiments automation with a real robot in order to compare performance of different fiducial marker systems, which could be used in automated camera calibration process. We used Russian humanoid robot AR-601M and automated it's manipulators for performing joint rotations. This paper is an extension of our previous work on ARTag, AprilTag and CALTag marker comparison in laboratory settings with large-sized markers that had showed significant superiority of CALTag system over the competitors. This time the markers were scaled down and placed on AR-601M humanoid's palms. We automated experiments of marker rotations, analyzed the results and compared them with the previously obtained results of manual experiments with large-sized markers. The new automated pilot experiments, which were performed both in pure laboratory conditions and pseudo field environments, demonstrated significant differences with previously obtained manual experimental results:
    Keywords AprilTagAR-601MARTagCALTagExperimental comparisonFiducial marker systemsHumanoid robot
    The name of the journal Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    URL https://www.scopus.com/inward/record.uri?eid=2-s2.0-85053843104&doi=10.1007%2f978-3-319-99582-3_26&partnerID=40&md5=1be894c8e08cee14fd3960c38d8dcd51
    Please use this ID to quote from or refer to the card https://repository.kpfu.ru/eng/?p_id=186111&p_lang=2
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