Optical-Inertial Tracking System with High Bandwidth and Low Latency

  • Göntje C. ClaasenEmail author
  • Philippe Martin
  • Frédéric Picard
Part of the Studies in Computational Intelligence book series (SCI, volume 480)


We propose an optical-inertial tracking system for a servo-controlled handheld tool in a computer-assisted surgery system. We present a mathematical system description and a data fusion algorithm which integrates data from optical and inertial sensors. The algorithm is a right-invariant Extended Kalman Filter (EKF) which takes into account system symmetries to improve the filter convergence. The tracking system has a high bandwidth thanks to the inertial sensors and a low latency thanks to a direct approach where sensor data is used directly in the data fusion algorithm without previous computations. Experimental data show that the optical-inertial system can indeed track a moving object.


Extended Kalman Filter Inertial Measurement Unit Inertial Sensor Sensor Unit Optical Tracking 
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  1. 1.
    G. Brisson, T. Kanade, A.M.D. Gioia, B. Jaramaz, Precision Freehand Sculpting of Bone. Medical Image Computing and Computer-Assisted Intervention (Springer, LNCS, 2004), pp. 105–112Google Scholar
  2. 2.
    H. Haider, O.A. Barrera, K.L. Garvin, Minimally invasive total knee arthroplasty surgery through navigated freehand bone cutting: Winner of the 2005 HAP Paul Award. J. Arthroplasty 22(4), 535–542 (2007)CrossRefGoogle Scholar
  3. 3.
    N. Parnian, S.P. Won, F. Golnaraghi, Position Sensing Using Integration of a Vision System and Inertial Sensors. 34th Annual Conference of the IEEE Industrial Electronics Society (2008), pp 3011–3015Google Scholar
  4. 4.
    D. Roetenberg, Inertial and magnetic sensing of human motion. PhD thesis, Universiteit Twente, 2006Google Scholar
  5. 5.
    A. Tobergte, M. Pomarlan, G. Hirzinger, Robust Multi Sensor Pose Estimation for Medical Applications. IEEE/RSJ International Conference on Intelligent Robots and Systems (2009), pp. 492–497Google Scholar
  6. 6.
    B. Hartmann, N. Link, G.F. Trommer, Indoor 3D Position Estimation Using Low-Cost Inertial Sensors and Marker-Based Video-Tracking. IEEE/ION Position Location and Navigation Symposium (2010), pp. 319–326Google Scholar
  7. 7.
    R. Hartley, A. Zisserman, Multiple View Geometry in Computer Vision, 2nd edn. (Cambridge University Press, Cambridge, 2003)Google Scholar
  8. 8.
    IEEE, IEEE standard specification format guide and test procedure for single-axis interferometric fiber optic gyros (IEEE Std 95–1997).Google Scholar
  9. 9.
    B.L. Stevens, F.L. Lewis, Aircraft Control and Simulation (Wiley, Hoboken, New Jersey, 2003)Google Scholar
  10. 10.
    G.C. Goodwin, S.F. Graebe, M.E. Salgado, Control System Design (Prentice Hall, Englewood Cliffs NJ, 2001)Google Scholar
  11. 11.
    U. Jürss, W. Rudolph, Tracking Hot Spots. elektor 384 (2008)Google Scholar
  12. 12.
    J.Y. Bouguet, Camera calibration toolbox for Matlab,
  13. 13.
    S. Umeyama, Least-squares estimation of transformation parameters between two point patterns. IEEE. T. Pattern. Anal. 13(4), 376–380 (1991)CrossRefGoogle Scholar
  14. 14.
    D. Simon, Optimal State Estimation: Kalman, \(H_{\infty }\), and Nonlinear Approaches (Wiley, Hoboken, New Jersey, 2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Göntje C. Claasen
    • 1
    Email author
  • Philippe Martin
    • 1
  • Frédéric Picard
    • 2
  1. 1.Centre Automatique et Systèmes, MINES ParisTechParisFrance
  2. 2.Department of Orthopaedics Golden Jubilee National HospitalGlasgowUK

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