Казанский (Приволжский) федеральный университет, КФУ
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COMPARING FIDUCIAL MARKERS PERFORMANCE FOR A TASK OF A HUMANOID ROBOT SELF-CALIBRATION OF MANIPULATORS: A PILOT EXPERIMENTAL STUDY
Форма представленияСтатьи в зарубежных журналах и сборниках
Год публикации2018
Языканглийский
  • Магид Евгений Аркадьевич, автор
  • Сагитов Артур Газизович, автор
  • Шабалина Ксения Сергеевна, автор
  • Сагитов Артур Газизович, автор
  • Шабалина Ксения Сергеевна, автор
  • Библиографическое описание на языке оригинала 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.
    Аннотация 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:
    Ключевые слова ARTag, AprilTag, CALTag, Fiducial marker systems, AR-601M, Humanoid robot, Experimental comparison
    Название журнала 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
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