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Cited by 45Inertial Sensor and NavigationTarget Tracking and Data Fusion in Sensor NetworksRobotics and Sensor-Based Localization

The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

Kalman filtering for real-time orientation tracking of handheld microsurgical instrument

Wei Tech Ang, Khosla, P.K., Riviere, C.N.·2005·2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE Cat. No.04CH37566)
DOI 10.1109/iros.2004.1389796
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Abstract

This paper presents the theory and modeling of a quaternion-based augmented state Kalman filter for real-time orientation tracking of a handheld microsurgical instrument equipped with a magnetometer-aided all-accelerometer inertial measurement unit (IMU). The onboard sensing system provides two complementary sources of orientation information. The all-accelerometer IMU provides a high resolution but drifting angular velocity estimate, while the magnetic north vector is combined with the estimated gravity vector to yield a non-drifting but noisy orientation estimate. Analysis of the dominant stochastic noise components of the sensors and derivation of the noise covariance are presented. The proposed Kalman filter obtains a non-drifting orientation estimate with improved resolution by incorporating the motion dynamics of the instrument during microsurgery and models the angular velocity drift explicitly as extra dynamic states.

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Crossref · 10.1109/iros.2004.1389796

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1 of 1 references verified0 citation errors · 0 not checkable

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  • Angular motion information derived from differential sensing kinematics has very high sensing resolution but suffers from integration drift, while orientation obtained from the TRIAD algorithm is noisy but non-drifting.

    “It has been shown that the angular motion information derived from the differential sensing kinematics has very high sensing resolution but suffers from integration drift; on the other hand, the orientation obtained from the TRIAD algorithm is noisy but non-drifting.”

    From Active tremor compensation in microsurgery (2005) · cites this paper as [16]

  • Fusing the two complementary sensing sources via an augmented state quaternion-based Kalman filter yields high quality sensing.

    “These two complementary sensing sources are fused via an augmented state quaternion-based Kalman filter to yield high quality sensing [16].”

    From Active tremor compensation in microsurgery (2005) · cites this paper as [16]

Citations

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Research works printed but not linked: 21

  1. 10.1109/70.388775 · DOI
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  3. markley, 2000, Quaternion attitude estimation using vector observations, Jour Of Astronomical Sciences, 48, 369
  4. 0, IEEE Standard Specification Format Guide and Test Procedure for Linear Single-Axis
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  7. marins, 2001, An extended kalman filter for quatemoin-based orientation estimation using MARG sensors, Proc IEEE/RSJ Int Conf Intelligent Robots and Systems, 2003, 10.1109/IROS.2001.976367 · DOI
  8. 10.2514/3.19717 · DOI · Paper page
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  11. 10.2514/3.56190 · DOI
  12. yun, 2003, An improved quaternion-based kalman filter for real-time tracking of rigid body orientation, Proc IEEE/RSJ Int Conf Intelligent Robots & Systems, 1074
  13. 10.1109/7.85036 · DOI
  14. 10.1109/VRAIS.1996.490527 · DOI
  15. foxlin, 1998, Miniature 6-DOF inertial system for tracking HMDs, SPIE Vol 3362 Helmet and Head-Mounted Displays III, 3362, 10.1117/12.317434 · DOI
  16. ang, 2004, Physical model of a dual-axis MEMS accelerometer for low-g motion tracking applications, Proc IEEE Int Conf Robotics and Automation, 1345
  17. luinge, 2002, Inertial Sensing of Human Movement
  18. hehbinder, 2001, Drift-free attitude estimation for accelerated rigid bodies, Proc IEEE Int Conf Robot Automat, 4244
  19. 10.1109/PLANS.2000.838301 · DOI
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  21. ang, 2003, Design of all-accelerometer inertial measurement unit for tremor sensing in hand-held microsurgical instrument, Proc Int Conf Robotics and Automation, 1781

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Metadata

Authors
Ang, W. T., Khosla, P. K., & Riviere, C. N.
Affiliations
Carnegie Mellon University (US)
ORCID
Venue
2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE Cat. No.04CH37566)
Publisher
IEEE
Published
2005-04-01
Volume
3
Pages
2574-2580
ISSN
2153-0858 (ISSN-L)
DOI
10.1109/iros.2004.1389796
OpenAlex
W2161998696
Cited by
45
References
1
Citations by year
2025: 1 · 2024: 1 · 2023: 1 · 2021: 1 · 2020: 1 · 2019: 2 · 2017: 6 · 2016: 4 · 2014: 5 · 2012: 3
FWCI
8.90
Type
conference paper
Language
en
Open access
closed
Locations
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (publishedVersion)
Topics
  • Physical Sciences › Engineering › Aerospace Engineering › Inertial Sensor and Navigation
  • Physical Sciences › Computer Science › Artificial Intelligence › Target Tracking and Data Fusion in Sensor Networks
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Robotics and Sensor-Based Localization
Keywords
Kalman filter, orientation tracking, quaternions, augmented state estimation, magnetometer, accelerometer, inertial measurement unit, microsurgery, sensor fusion

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