Kazan (Volga region) Federal University, KFU
KAZAN
FEDERAL UNIVERSITY
 
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. Comparing Fiducial Markers Performance for a Task of a Humanoid Robot Self-calibration of Manipulators: A Pilot Experimental Study / Sagitov A., Svinin M., Magid E. // Lecture Notes in Computer Science (incl. subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). - 2018. - Vol. 11097. - 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 ARTag, AprilTag, CALTag, Fiducial marker systems, AR-601M, Humanoid robot, Experimental comparison
    The name of the journal Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    URL https://link.springer.com/chapter/10.1007/978-3-319-99582-3_26
    Please use this ID to quote from or refer to the card https://repository.kpfu.ru/eng/?p_id=186111&p_lang=2
    Resource files 
    File name Size (MB) Format  
    F_2018_Comparing_Fiducial_Markers_Performance_for_a_Task_of_a__umanoid_Robot_Self_calibration.pdf 1,99 pdf show / download

    Full metadata record