Skip to main content
SpringerLink
Log in

Dynamic Pose Tracking Accuracy Improvement via Fusing HTC Vive Trackers and Inertia Measurement Units

  • Regular Paper
  • Published:
International Journal of Precision Engineering and Manufacturing Aims and scope Submit manuscript

Abstract

Virtual reality tracking devices are being investigated for application to motion tracking of robots, human bodies, indoor drones, mobile systems, etc., but most studies so far have been limited to performance analysis of commercialized tracking devices in static conditions. This paper investigated methods for improving the measurement accuracy of dynamic positioning and orientation of the HTC tracker. The signals from the photodiodes in the tracker were extracted and fused together, as well as with external Inertial Measurement Units (IMUs) signals using the Extended Kalman Filter (EKF) and the Unscented Kalman Filter (UKF). Multiple base stations, trackers and IMUs were applied to evaluate the measurement accuracy. Multiple paths were used to test different dynamic operating conditions. The results show that the proposed tracking system can improve accuracy by up to several mm by using the UKF as opposed to the EKF algorithm, increasing the number of base stations, increasing the number of trackers and fusing IMUs.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15

Similar content being viewed by others

References

  1. Baek, J., Noh, G., & Seo, J. (2021). Robotic camera calibration to maintain consistent percision of 3D trackers. International Journal of Precision Engineering and Manufacturing, 22, 1853–1860. https://doi.org/10.1007/s12541-021-00573-3

    Article  Google Scholar 

  2. Editor, K. M. P. (2021). Robotic metrology-assisted assembly combines photogrammetry with laser tracker. Metrology and Quality News Online Magazine, Feb. 24, 2021. https://metrology.news/automated-metrology-assisted-assembly-photogrammetry-laser-tracker-and-robot-technology/ . Accessed 20 March 2023.

  3. Kim, S. H., Nam, E., Ha, T. I., Hwang, S. H., Lee, J. H., Park, S. H., & Min, B. K. (2019). Robotic machining: A review of recent progress. International Journal of Precision Engineering and Manufacturing, 20, 1629–1642. https://doi.org/10.1007/s12541-019-00187-w

    Article  Google Scholar 

  4. Filion, A., Joubair, A., Tahan, A. S., & Bonev, I. A. (2018). Robot calibration using a portable photogrammetry system. Robotics and Computer-Integrated Manufacturing, 49, 77–87. https://doi.org/10.1016/j.rcim.2017.05.004

    Article  Google Scholar 

  5. Moeller, M., et al. (2017). Real time pose control of an industrial robotic system for machining of large scale components in aerospace industry using laser tracker system. SAE International Journal of Aerospace, 10(2) 100–108. https://doi.org/10.4271/2017-01-2165

    Article  Google Scholar 

  6. Kleinkes, D. M., Loser, D. R., & Kleinkes, D. M. (2011). Laser tracker and 6DoF measurement strategies in industrial robot applications. In 2011 Coordinate Metrology System Conference, 25–28.

  7. Leica Absolute Tracker AT960. Hexagon. https://hexagon.com/products/leica-absolute-tracker-at960. Accessed 29 April 2023.

  8. Yang, D., & Zou, J. (2022). Precision analysis of flatness measurement using laser tracker. International Journal of Precision Engineering and Manufacturing, 23(7), 721–732. https://doi.org/10.1007/s12541-022-00660-z

    Article  MathSciNet  Google Scholar 

  9. Furtado, J. S., Liu, H. H. H. T., Lai, G., Lacheray, H., & Desouza-Coelho, J. (2019). Comparative analysis of OptiTrack motion capture systems. In Advances in Motion Sensing and Control for Robotic Applications: Selected Papers from the Symposium on Mechatronics, Robotics, and Control (SMRC’18)-CSME International Congress 2018, May 27-30, 2018 Toronto, Canada, 15–31. Springer International Publishing. https://doi.org/10.1007/978-3-030-17369-2_2

  10. Pérez, L., Rodríguez, Í., Rodríguez, N., Usamentiaga, R., & García, D. F. (2016). Robot guidance using machine vision techniques in industrial environments: A comparative review. Sensors, 16(3), 335. https://doi.org/10.3390/s16030335

    Article  Google Scholar 

  11. Niehorster, D. C., Li, L., & Lappe, M. (2017). The accuracy and precision of position and orientation tracking in the HTC Vive virtual reality system for scientific research. i-Perception, 8(3), 2041669517708205.

  12. Borges, M., Symington, A., Coltin, B., Smith, T., & Ventura, R. (2018). HTC Vive: Analysis and accuracy improvement. In 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2610–2615. IEEE. https://doi.org/10.1109/IROS.2018.8593707

  13. Astad, M. A., Arbo, M. H., Grøtli, E. I., & Gravdahl, J. T. (2019). Vive for robotics: Rapid robot cell calibration. In 2019 7th International conference on control, mechatronics and automation (ICCMA), 151–156. https://doi.org/10.1109/ICCMA46720.2019.8988631

  14. Borges, M., Symington, A., & Ventura, R. (2019). Accurate absolute localization methods for the HTC vive motion capture system.

  15. Greiff, M., Robertsson, A., & Berntorp, K. (2019). Performance bounds in positioning with the VIVE lighthouse system. In 2019 22th International Conference on Information Fusion (FUSION), 1–8.

  16. Leal, A. M. M. (2018). autodiff, a modern, fast and expressive C++ library for automatic differentiation. 2018. [Online]. Available: https://autodiff.github.io. Accessed 14 March 2023.

  17. Agarwal, S., Mierle, K., Team, T. C. S. (2022). Ceres Solver. Mar. 2022. [Online]. Available: https://github.com/ceres-solver/ceres-solver. Accessed 14 March 2023.

  18. Tips for setting up the base stations. https://www.vive.com/ca/support/vive/category_howto/tips-for-setting-up-the-base-stations.html. Accessed 13 March 2023.

  19. Bancroft, J. B., & Lachapelle, G. (2011). Data fusion algorithms for multiple inertial measurement units. Sensors (Basel, Switzerland), 11(7), 6771–6798. https://doi.org/10.3390/s110706771

    Article  Google Scholar 

  20. Ribeiro, M. I. (2004). Kalman and extended kalman filters: Concept, derivation and properties. Institute for Systems and Robotics, 43, 46.

    Google Scholar 

  21. Runge-Kutta Method—An overview | ScienceDirect Topics. https://www.sciencedirect.com/topics/mathematics/runge-kutta-method. Accessed 26 Oct 2022.

  22. Process Noise—an overview | ScienceDirect Topics. https://www.sciencedirect.com/topics/engineering/process-noise. Accessed 14 March 2023.

  23. Becker, A. Online Kalman Filter Tutorial. https://www.kalmanfilter.net/. Accessed 14 March 2023.

  24. Julier, S. J., & Uhlmann, J. K. (1997). New extension of the Kalman filter to nonlinear systems. In Signal Processing, Sensor Fusion, and Target Recognition VI, 3068, 182–193. International Society for Optics and Photonics.

  25. Tsai, R. Y., & Lenz, R. K. (1989). A new technique for fully autonomous and efficient 3D robotics hand/eye calibration. IEEE Trans. Robot. Automat., 5(3), 345–358. https://doi.org/10.1109/70.34770

    Article  Google Scholar 

  26. Zhang, Z. (2000). A flexible new technique for camera calibration. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(11), 1330–1334. https://doi.org/10.1109/34.888718

    Article  Google Scholar 

  27. Park, F. C., & Martin, B. J. (1994). Robot sensor calibration: Solving AX=XB on the Euclidean group. IEEE Transactions on Robotics and Automation, 10(5), 717–721. https://doi.org/10.1109/70.326576

    Article  Google Scholar 

Download references

Acknowledgements

The authors would like to acknowledge the Natural Sciences and Engineering Research Council of Canada (NSERC) and Alberta Innovates (AI) for supporting this work.

Author information

Authors and Affiliations

  1. Department of Mechanical and Manufacturing Engineering, University of Calgary, 2500 University Dr. NW., Calgary, AB, T2N1N4, Canada

    Mitchell Weber & Jihyun Lee

  2. Institute for Machine Tools and Industrial Management, Technical University of Munich (TUM), Boltzmannstr. 15, 85748, Garching bei Munich, Germany

    Roman Hartl & Michael F. Zäh

Authors
  1. Mitchell Weber
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Roman Hartl
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Michael F. Zäh
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jihyun Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jihyun Lee.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Weber, M., Hartl, R., Zäh, M.F. et al. Dynamic Pose Tracking Accuracy Improvement via Fusing HTC Vive Trackers and Inertia Measurement Units. Int. J. Precis. Eng. Manuf. 24, 1661–1674 (2023). https://doi.org/10.1007/s12541-023-00891-8

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12541-023-00891-8

Keywords

Search

Navigation