Simultaneous kinematic calibration, localization, and mapping (SKCLAM) for industrial robot manipulators
Simultaneous kinematic calibration, localization, and mapping (SKCLAM) for industrial robot manipulators
复制标题
工业机器人机械手的同步运动校准、定位和绘图 (SKCLAM)
DOI：
10.1080/01691864.2019.1689166
复制
发表时间：
2019
影响因子：
2
通讯作者：
Maeda Yusuke
中科院分区：
文献类型：
--
作者：
Li Jinghui;Ito Akitoshi;Yaguchi Hiroyuki;Maeda Yusuke
近年来，在机器人工业中，对更精确、更高效、更经济的机器人操作器的需求正在增加。然而，机械手在生产过程中会产生运动误差。因此，需要低成本的运动学校准。此外，环境映射也需要规划的机械手的运动。在本文中，我们提出了一个同时运动学校准，定位和映射（SKCLAM）的方法，它可以同时校准工业机器人操作器的运动学参数，使用商业RGB-D相机连接到其末端执行器，以重建其周围环境。在我们的方法中，运动学标定是通过特征检测和外极线几何实现的。合成和真实的数据实验进行了验证SKCLAM方法。在实验中，我们成功地减少了机械手的运动误差和重建密集的三维工作空间地图。
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.