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Related Experiment Videos

Using an extended kalman filter for rigid body pose estimation.

Kjartan Halvorsen1, Torsten Söderström, Virgil Stokes

  • 1Biomechanics and Motor Control, Stockholm University College of Physical Education and Sports, Stockholm, Sweden. kjartanh@ihs.se

Journal of Biomechanical Engineering
|August 3, 2005
PubMed
Summary

This study introduces an improved method for tracking rigid body motion using an extended Kalman filter (EKF). The EKF method enhances pose estimation accuracy, especially when marker data is temporarily missing.

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Area of Science:

  • Biomechanics
  • Robotics
  • Computer Vision

Background:

  • Rigid body pose estimation is crucial in biomechanics and robotics.
  • Current methods, like Söderkvist and Wedin's, assume isotropic and homogenous measurement errors.
  • These methods often compute pose frame-by-frame without considering temporal dynamics.

Purpose of the Study:

  • To present an alternative method for rigid body pose estimation using state space formulation and an extended Kalman filter (EKF).
  • To improve accuracy and robustness, particularly in handling missing marker data.
  • To provide analytical expressions for linearized system equations for efficient implementation.

Main Methods:

  • Formulated state space models describing rigid body kinematics with a state vector of generalized coordinates and their time derivatives.

Related Experiment Videos

  • Applied an extended Kalman filter (EKF) to track rigid body motion.
  • Derived analytical expressions for the linearized measurement function and system equations.

    • Main Results:

    • The EKF method demonstrated improved accuracy compared to the Söderkvist and Wedin method on simulated data, especially with temporarily missing marker data.
    • The EKF's time-variant nature naturally handles missing marker data.
    • State updates utilize all available information, even with fewer than three markers.

    Conclusions:

    • The proposed EKF method offers enhanced accuracy in rigid body pose estimation by integrating prior knowledge of motion characteristics and measurement errors.
    • Analytical expressions eliminate the need for approximate discrete differentiation, facilitating faster implementation.
    • This approach provides a more robust and accurate solution for rigid body motion tracking.