Simultaneous kinematic calibration, localization, and mapping (SKCLAM) for industrial robot manipulators
Year: 2019
Type: article
Abstract: Recently, the demand for more accurate, productive, and economical robot manipulators is increasing in the robotics industry. However, a manipulator will produce kinematic errors during production. Thus low-cost kinematic calibration is demanded. Moreover, environmental mapping is also demanded to plan the motions of the manipulator. In this paper, we proposed a simultaneous kinematic calibration, localization, and mapping (SKCLAM) method, which can simultaneously calibrate the kinematic parameters of an industrial robot manipulator using a commercial RGB-D camera attached to its end effector to reconstruct its surroundings. In our method, the kinematic calibration is achieved with feature detection and epipolor geometry. Synthetic and real data experiments were conducted to verify the SKCLAM method. We succeeded in reducing the kinematic errors of the manipulator and reconstructing dense 3D maps of the workspace in the experiments.
Source: Advanced Robotics
Authors: Jinghui Li, Akitoshi Ito, Hiroyuki Yaguchi, Yusuke Maeda
Institution: Yokohama National University
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2987106954