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.
|Number of pages
|Transactions of the Korean Society of Mechanical Engineers, A
|Published - 2022
Bibliographical notePublisher 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