Deep Learning-based Pose Estimation for Mobile Manipulator Tasks

Hae Chang Kim, In Hwan Yoon, Jae Bok Song

Research output: Contribution to journalArticlepeer-review


Navigation errors typically lead to target pose errors in mobile manipulators. Although cameras can be attached to the end of the robot to calculate the pose required for a task through marker recognition and hand-eye calibration, pose errors may still occur because of camera distortion. To address this issue, a deep neural network was employed to compensate for the estimated marker pose error and reduce the transformation error between the endeffector and camera. The proposed deep learning-based pose estimation method minimized the 3D pose errors by 62% compared to the existing methods. Furthermore, the target objects were moved to the target jig in all 50 pick-and-place tasks, with a tolerance of less than 1 mm.

Original languageEnglish
Pages (from-to)1161-1166
Number of pages6
JournalTransactions of the Korean Society of Mechanical Engineers, A
Issue number3
Publication statusPublished - 2022

Bibliographical note

Publisher Copyright:
© 2022 Korean Society of Mechanical Engineers. All rights reserved.


  • Deep Learning
  • Hand-Eye Calibration
  • Marker Detection
  • Mobile Manipulator

ASJC Scopus subject areas

  • Mechanical Engineering


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