Ferguson James M, Ertop Tayfun Efe, Herrell S Duke, Webster Robert J
Department of Mechanical Engineering, Vanderbilt University, Nashville, TN, USA.
Department of Urologic Surgery, Vanderbilt University Medical Center, Nashville, TN, USA.
Robotica. 2023 May;41(5):1590-1616. doi: 10.1017/s0263574723000012. Epub 2023 Feb 16.
Robots and inertial measurement units (IMUs) are typically calibrated independently. IMUs are placed in purpose-built, expensive automated test rigs. Robot poses are typically measured using highly accurate (and thus expensive) tracking systems. In this paper, we present a quick, easy, and inexpensive new approach to calibrate both simultaneously, simply by attaching the IMU anywhere on the robot's end effector and moving the robot continuously through space. Our approach provides a fast and inexpensive alternative to both robot and IMU calibration, without any external measurement systems. We accomplish this using continuous-time batch estimation, providing statistically optimal solutions. Under Gaussian assumptions, we show that this becomes a nonlinear least squares problem and analyze the structure of the associated Jacobian. Our methods are validated both numerically and experimentally and compared to standard individual robot and IMU calibration methods.
机器人和惯性测量单元（IMU）通常是独立校准的。IMU被放置在专门设计的、昂贵的自动化测试装置中。机器人的位姿通常使用高精度（因此也很昂贵）的跟踪系统来测量。在本文中，我们提出了一种快速、简便且廉价的新方法，通过将IMU简单地附着在机器人末端执行器的任何位置，并让机器人在空间中连续移动，来同时校准两者。我们的方法提供了一种快速且廉价的替代方案，无需任何外部测量系统即可同时校准机器人和IMU。我们使用连续时间批量估计来实现这一点，提供统计上最优的解决方案。在高斯假设下，我们表明这会变成一个非线性最小二乘问题，并分析相关雅可比矩阵的结构。我们的方法在数值和实验上都得到了验证，并与标准的单独机器人和IMU校准方法进行了比较。
