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Unified robot and inertial sensor self-calibration

James M. Ferguson, Tayfun Efe Ertop, S. Duke Herrell, Robert J. Webster

Year
2023
Citations
3

Abstract

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.

Keywords

Inertial measurement unitRobotCalibrationJacobian matrix and determinantComputer scienceRobot calibrationInertial frame of referenceArtificial intelligenceComputer visionControl theory (sociology)

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