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Unified robot and inertial sensor self-calibration

Published online by Cambridge University Press:  16 February 2023

James M. Ferguson Open the ORCID record for James M. Ferguson [Opens in a new window] ,
Tayfun Efe Ertop ,
S. Duke Herrell III  and
Robert J. Webster III
James M. Ferguson*
Affiliation:
Department of Mechanical Engineering, Vanderbilt University, Nashville, TN, USA
Tayfun Efe Ertop
Affiliation:
Department of Mechanical Engineering, Vanderbilt University, Nashville, TN, USA
S. Duke Herrell III
Affiliation:
Department of Urologic Surgery, Vanderbilt University Medical Center, Nashville, TN, USA
Robert J. Webster III
Affiliation:
Department of Mechanical Engineering, Vanderbilt University, Nashville, TN, USA
*
*Corresponding author. E-mail: james.m.ferguson@vanderbilt.edu
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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

robot calibrationinertial sensor calibrationBayesian inferencecontinuous-time batch estimationoptimal trajectory planning
Type
Research Article
Information
Robotica , Volume 41 , Issue 5 , May 2023 , pp. 1590 - 1616
Copyright
© The Author(s), 2023. Published by Cambridge University Press

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References

Roth, Z., Mooring, B. and Ravani, B., “An overview of robot calibration,” IEEE J. Robot. Automat. 3(5), 377–385 (1987).CrossRefGoogle Scholar
Ziegert, J. and Datseris, P.. Basic Considerations for Robot Calibration. In: International Conference on Robotics and Automation (IEEE, 1988) pp. 932–938.Google Scholar
Mooring, B. W., Roth, Z. S. and Driels, M. R.. Fundamentals of Manipulator Calibration (Wiley, New York,1991).Google Scholar
Zhuang, H. and Roth, Z. S.. Camera-Aided Robot Calibration (CRC Press,Boca Raton, Florida, USA, 1996).Google Scholar
He, R., Zhao, Y., Yang, S. and Yang, S., “Kinematic-parameter identification for serial-robot calibration based on POE formula,” IEEE Trans. Robot. 26(3), 411–423 (2010).Google Scholar
Fong, W., Ong, S. and Nee, A., “Methods for in-field user calibration of an inertial measurement unit without external equipment,” Meas. Sci. Technol. 19(8), 085202 (2008).CrossRefGoogle Scholar
Kim, A. and Golnaraghi, M., “Initial Calibration of An Inertial Measurement Unit Using An Optical Position Tracking System,” In: Position Location and Navigation Symposium (IEEE, 2004) pp. 96–101.Google Scholar
Zhang, H., Wu, Y., Wu, W., Wu, M. and Hu, X., “Improved multi-position calibration for inertial measurement units,” Meas. Sci. Technol. 21(1), 015107 (2009).CrossRefGoogle Scholar
Tedaldi, D., Pretto, A. and Menegatti, E., “A Robust and Easy to Implement Method for IMU Calibration Without External Equipments,” In: International Conference on Robotics and Automation (IEEE, 2014) pp. 3042–3049.CrossRefGoogle Scholar
Poddar, S., Kumar, V. and Kumar, A., “A comprehensive overview of inertial sensor calibration techniques,” J. Dynam. Syst. Meas. Contr. 139(1), 011006-1–011006-11 (2017).Google Scholar
Rohac, J., Sipos, M. and Simanek, J., “Calibration of low-cost triaxial inertial sensors,” IEEE Instru. Meas. Mag. 18(6), 32–38 (2015).CrossRefGoogle Scholar
Furgale, P., Tong, C. H., Barfoot, T. D. and Sibley, G., “Continuous-time batch trajectory estimation using temporal basis functions,” Int. J. Robot. Res. 34(14), 1688–1710 (2015).CrossRefGoogle Scholar
Elatta, A., Gen, L. P., Zhi, F. L., Daoyuan, Y. and Fei, L., “An overview of robot calibration,” Inform. Technol. J. 3(1), 74–78 (2004).Google Scholar
Cheng, P. and Oelmann, B., “Joint-angle measurement using accelerometers and gyroscopes—A survey,” IEEE Trans. Instrum. Meas. 59(2), 404–414 (2010).CrossRefGoogle Scholar
Ghassemi, F., Tafazoli, S., Lawrence, P. D. and Hashtrudi-Zaad, K., “Design and calibration of an integration-free accelerometer-based joint-angle sensor,” IEEE Trans. Instrum. Meas. 57(1), 150–159 (2008).CrossRefGoogle Scholar
Cantelli, L., Muscato, G., Nunnari, M. and Spina, D., “A joint-angle estimation method for industrial manipulators using inertial sensors,” IEEE/ASME Trans. Mechatron. 20(5), 2486–2495 (2015).CrossRefGoogle Scholar
Roan, P., Deshpande, N., Wang, Y. and Pitzer, B. “Manipulator State Estimation with Low Cost Accelerometers and Gyroscopes,” In: International Conference on Intelligent Robots and Systems (IEEE/RSJ, 2012) pp. 4822–4827.CrossRefGoogle Scholar
Olofsson, B., Antonsson, J., Kortier, H. G., Bernhardsson, B., Robertsson, A. and Johansson, R., “Sensor fusion for robotic workspace state estimation,” IEEE/ASME Trans. Mechatron. 21(5), 2236–2248 (2015).CrossRefGoogle Scholar
Munoz-Barron, B., Rivera-Guillen, J. R., Osornio-Rios, R. A. and Romero-Troncoso, R. J., “Sensor fusion for joint kinematic estimation in serial robots using encoder, accelerometer and gyroscope,” J. Intell. Robot. Syst. 78(3-4), 529–540 (2015).CrossRefGoogle Scholar
Burgner-Kahrs, J., Rucker, D. C. and Choset, H., “Continuum robots for medical applications: A survey,” IEEE Trans. Robot. 31(6), 1261–1280 (2015).CrossRefGoogle Scholar
Le, H. M., Do, T. N. and Phee, S. J., “A survey on actuators-driven surgical robots,” Sens. Actuat. A Phys. 247, 323–354 (2016).CrossRefGoogle Scholar
Birjandi, S. A. B., Kühn, J. and Haddadin, S., “Observer-extended direct method for collision monitoring in robot manipulators using proprioception and imu sensing,” IEEE Robot. Automat. Lett. 5(2), 954–961 (2020).CrossRefGoogle Scholar
Canepa, G., Hollerbach, J. M. and Boelen, A. J. M. A., “Kinematic Calibration by Means of a Triaxial Accelerometer,” In: International Conference on Robotics and Automation, vol. 4 (IEEE, 1994) pp. 2776–2782.Google Scholar
D’Amore, N., Ciarleglio, C. and Akin, D. L., “IMU-Based Manipulator Kinematic Identification,” In: International Conference on Robotics and Automation (IEEE, 2015) pp. 1437–1441.CrossRefGoogle Scholar
Du, G. and Zhang, P., “IMU-based online kinematic calibration of robot manipulator,” Sci. World J. 2013, 1–10 (2013).CrossRefGoogle ScholarPubMed
Du, G., “Online serial manipulator calibration based on multisensory process via extended Kalman and particle filters,” IEEE Trans. Ind. Electron. 61(12), 6852–6859 (2014).Google Scholar
Du, G., Liang, Y., Li, C., Liu, P. X. and Li, D., “Online robot kinematic calibration using hybrid filter with multiple sensors,” IEEE Trans. Instrum. Meas. 69(9), 7092–7107 (2020).CrossRefGoogle Scholar
Renk, E. L., Rizzo, M., Collins, W., Lee, F. and Bernstein, D. S., “Calibrating a triaxial accelerometer-magnetometer-using robotic actuation for sensor reorientation during data collection,” IEEE Contr. Syst. Mag. 25(6), 86–95 (2005).Google Scholar
Botero-Valencia, J., Marquez-Viloria, D., Castano-Londono, L. and Morantes-Guzmán, L., “A low-cost platform based on a robotic arm for parameters estimation of inertial measurement units,” Measurement 110, 257–262 (2017).CrossRefGoogle Scholar
Beravs, T., Podobnik, J. and Munih, M., “Three-axial accelerometer calibration using kalman filter covariance matrix for online estimation of optimal sensor orientation,” IEEE Trans. Instrum. Meas. 61(9), 2501–2511 (2012).CrossRefGoogle Scholar
Qin, T., Li, P. and Shen, S., “VINS-Mono: A robust and versatile monocular visual-inertial state estimator,” IEEE Trans. Robot. 34(4), 1004–1020 (2018).CrossRefGoogle Scholar
Kaiser, J., Martinelli, A., Fontana, F. and Scaramuzza, D., “Simultaneous state initialization and gyroscope bias calibration in visual inertial aided navigation,” IEEE Robot. Automat. Lett. 2(1), 18–25 (2017).CrossRefGoogle Scholar
Huang, W., Liu, H. and Wan, W., “An online initialization and self-calibration method for stereo visual-inertial odometry,” IEEE Trans. Robot. 36(4), 1153–1170 (2020).CrossRefGoogle Scholar
Hakyoung Chung, L. O. and Borenstein, J., “Accurate mobile robot dead-reckoning with a precision-calibrated fiber-optic gyroscope,” IEEE Trans. Robot. Autom. 17(1), 80–84 (2001).CrossRefGoogle Scholar
Barshan, B. and Durrant-Whyte, H. F., “Inertial navigation systems for mobile robots,” IEEE Trans. Robot. Autom. 11(3), 328–342 (1995).CrossRefGoogle Scholar
Martinelli, A., “Vision and imu data fusion: closed-form solutions for attitude, speed, absolute scale, and bias determination,” IEEE Trans. Robot. 28(1), 44–60 (2012).CrossRefGoogle Scholar
Forster, C., Carlone, L., Dellaert, F. and Scaramuzza, D., “On-manifold preintegration for real-time visual–inertial odometry,” IEEE Trans. Robot. 33(1), 1–21 (2017).CrossRefGoogle Scholar
Meggiolaro, M. A. and Dubowsky, S., “An Analytical Method to Eliminate the Redundant Parameters in Robot Calibration,” In: International Conference on Robotics and Automation (IEEE, 2000) pp. 3609–3615.Google Scholar
Hayati, S. and Mirmirani, M., “Improving the absolute positioning accuracy of robot manipulators,” J. Robot. Syst. 2(4), 397–413 (1985).CrossRefGoogle Scholar
Furgale, P., Rehder, J. and Siegwart, R., “Unified Temporal and Spatial Calibration for Multi-sensor Systems," In: International Conference on Intelligent Robots and Systems (IEEE/RSJ, 2013) pp. 1280–1286.CrossRefGoogle Scholar
Schumaker, L. L.. Spline Functions: Computational Methods (SIAM,Philadelphia, PA, USA, 2015).Google Scholar
Swevers, J., Ganseman, C., Tukel, D. B., De Schutter, J. and Van Brussel, H., “Optimal robot excitation and identification,” IEEE Trans. Robot. Autom. 13(5), 730–740 (1997).CrossRefGoogle Scholar
Bonnet, V., Fraisse, P., Crosnier, A., Gautier, M., González, A. and Venture, G., “Optimal exciting dance for identifying inertial parameters of an anthropomorphic structure,” IEEE Trans. Robot. 32(4), 823–836 (2016).CrossRefGoogle Scholar
Nocedal, J. and Wright, S.. Numerical Optimization (Springer Science & Business Media,Berlin, Germany, 2006).Google Scholar
Maybeck, P. S.. Stochastic Models, Estimation, and Control (Academic Press, Cambridge, Massachusetts, USA, 1982).Google Scholar
Bno055 intelligent 9-axis absolute orientation sensor,” Bosch Sensortec, Baden-Württemberg, Germany, p. 21, 2016.Google Scholar
Markley, F. L., Cheng, Y., Crassidis, J. L. and Oshman, Y., “Averaging quaternions,” J.Guid. Contr. Dynam. 30(4), 1193–1197 (2007).CrossRefGoogle Scholar
Park, K.-J., “Fourier-based optimal excitation trajectories for the dynamic identification of robots,” Robotica 24(5), 625–633 (2006).CrossRefGoogle Scholar
Armstrong, B., “On finding exciting trajectories for identification experiments involving systems with nonlinear dynamics,” Int. J. Robot. Res. 8(6), 28–48 (1989).CrossRefGoogle Scholar
Wilson, A. D., Schultz, J. A. and Murphey, T. D., “Trajectory synthesis for fisher information maximization,” IEEE Trans. Robot. 30(6), 1358–1370 (2014).CrossRefGoogle ScholarPubMed
Shiakolas, P., Conrad, K. and Yih, T., “On the accuracy, repeatability, and degree of influence of kinematics parameters for industrial robots,” Int. J. Model. Simul. 22(4), 245–254 (2002).CrossRefGoogle Scholar
Modes, V. and Burgner-Kahrs, J., “Calibration of concentric tube continuum robots: Automatic alignment of precurved elastic tubes,” IEEE Robot. Automat. Lett. 5(1), 103–110 (2019).CrossRefGoogle Scholar