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A Comparison Procedure for IMUs Performance

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Progress in Artificial Intelligence (EPIA 2019)

Abstract

Inertial measurement units (IMU) are, typically, a cluster of accelerometers, gyroscopes and magnetometers. Its use was introduced with military applications, being, nowadays, widely common on industrial applications, namely robot navigation. Since there are a lot of units in different cost ranges, it is proposed, in this paper, a procedure to compare their performance in tracking tasks. Once IMU samples are unavoidably corrupted by systematic and stochastic errors, a calibration procedure (without any external equipment) to identify sensors’ error models and a Kalman filter implementation to remove white noise are suggested. Then, the comparison is carried out over two trajectories, square and circular paths, respectively, being described by a robotic arm, which acts as reference. The results show that different manufacturing quality units can track, with success, orientation references but are incapable to perform position tracking activities.

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References

  1. Triad Method. https://en.wikipedia.org/wiki/Triad_method

  2. Comotti, D., Ermidoro, M.: Report of the course progetto di microelettronica. Technical report (2017)

    Google Scholar 

  3. Ferdinando, H., Khoswanto, H., Purwanto, D.: Embedded Kalman filter for inertial measurement unit (IMU) on the ATMega8535. In: 2012 International Symposium on Innovations in Intelligent Systems and Applications, pp. 1–5. IEEE, July 2012. https://doi.org/10.1109/INISTA.2012.6246978, http://ieeexplore.ieee.org/document/6246978/

  4. Fong, W.T., Ong, S.K., Nee, A.Y.C.: Methods for in-field user calibration of an inertial measurement unit without external equipment. Meas. Sci. Technol. 19(8), 085202 (2008). https://doi.org/10.1088/0957-0233/19/8/085202, http://stacks.iop.org/0957-0233/19/i=8/a=085202?key=crossref.eba2d86c89ec30974df9369d76e4e33e

    Article  Google Scholar 

  5. IEEE Aerospace and Electronic Systems Society. Gyro and Accelerometer Panel, IEEE Standards Board, American National Standards Institute: IEEE recommended practice for precision centrifuge testing of linear accelerometers. Institute of Electrical and Electronics Engineers (2001). https://ieeexplore.ieee.org/document/972832

  6. Kim, A., Golnaraghi, M.: Initial calibration of an inertial measurement unit using an optical position tracking system. In: PLANS 2004. Position Location and Navigation Symposium (IEEE Cat. No.04CH37556), pp. 96–101. IEEE. https://doi.org/10.1109/PLANS.2004.1308980, http://ieeexplore.ieee.org/document/1308980/

  7. Kok, M., Hol, J.D., Schön, T.B.: Using inertial sensors for position and orientation estimation. Found. Trends Signal Process. 11(2), 1–153 (2017). https://doi.org/10.1561/2000000094

    Article  MATH  Google Scholar 

  8. Nebot, E., Durrant-Whyte, H.: Initial calibration and alignment of low-cost inertial navigation units for land vehicle applications. J. Robot. Syst. 16(2), 81–92 (1999). https://doi.org/10.1002/(SICI)1097-4563(199902)16:2<81::AID-ROB2>3.0.CO;2-9

    Article  Google Scholar 

  9. Renk, E., Collins, W., Rizzo, M., Lee, F., Bernstein, D.: Optimization-based calibration of a triaxial accelerometer-magnetometer. In: Proceedings of the 2005, American Control Conference, pp. 1957–1962. IEEE (2005). https://doi.org/10.1109/ACC.2005.1470256, http://ieeexplore.ieee.org/document/1470256/

  10. Sabatelli, S., Galgani, M., Fanucci, L., Rocchi, A.: A double stage Kalman filter for sensor fusion and orientation tracking in 9D IMU. In: 2012 IEEE Sensors Applications Symposium Proceedings, pp. 1–5. IEEE, February 2012. https://doi.org/10.1109/SAS.2012.6166315, http://ieeexplore.ieee.org/document/6166315/

  11. Sabatini, A.: Quaternion-based extended Kalman filter for determining orientation by inertial and magnetic sensing. IEEE Trans. Biomed. Eng. 53(7), 1346–1356 (2006). https://doi.org/10.1109/TBME.2006.875664. http://ieeexplore.ieee.org/document/1643403/

    Article  Google Scholar 

  12. Salehi, S., Mostofi, N., Bleser, G.: A practical in-field magnetometer calibration method for IMUs (2012). https://www.researchgate.net/publication/258449504_A_practical_in-field_magnetometer_calibration_method_for_IMUs

  13. Stakkeland, M., Prytz, G., Booij, W.E., Pedersen, S.T.: Characterization of accelerometers using nonlinear Kalman filters and position feedback. IEEE Trans. Instrum. Meas. 56(6), 2698–2704 (2007). https://doi.org/10.1109/TIM.2007.908145. http://ieeexplore.ieee.org/document/4389144/

    Article  Google Scholar 

  14. Tedaldi, D., Pretto, A., Menegatti, E.: A robust and easy to implement method for IMU calibration without external equipments. In: 2014 IEEE International Conference on Robotics and Automation (ICRA), pp. 3042–3049. IEEE, May 2014. https://doi.org/10.1109/ICRA.2014.6907297, http://ieeexplore.ieee.org/document/6907297/

  15. Tomczyński, J., Mańkowski, T., Kaczmarek, P.: Cross-sensor calibration procedure for magnetometer and inertial units. In: Szewczyk, R., Zieliński, C., Kaliczyńska, M. (eds.) ICA 2017. AISC, vol. 550, pp. 450–459. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-54042-9_43

    Chapter  Google Scholar 

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Acknowledgment

This work is financed by the ERDF – European Regional Development Fund through the Operational Programme for Competitiveness and Internationalisation – COMPETE 2020 Programme, and by National Funds through the Portuguese funding agency, FCT - Fundação para a Ciência e a Tecnologia, within project SAICTPAC/0034/2015- POCI-01-0145-FEDER-016418.

This research was also supported by the Portuguese Foundation for Science and Technology (FCT) project COBOTIS (PTDC/EMEEME/ 32595/2017)

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Correspondence to Tiago Mendonça .

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Mendonça, T., Guimarães, D., Moreira, A.P., Costa, P. (2019). A Comparison Procedure for IMUs Performance. In: Moura Oliveira, P., Novais, P., Reis, L. (eds) Progress in Artificial Intelligence. EPIA 2019. Lecture Notes in Computer Science(), vol 11805. Springer, Cham. https://doi.org/10.1007/978-3-030-30244-3_28

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  • DOI: https://doi.org/10.1007/978-3-030-30244-3_28

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-030-30244-3

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