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The Resource Latency and distortion of electromagnetic trackers for augmented reality systems, Henry Himberg, Yuichi Motai, (electronic book)

Latency and distortion of electromagnetic trackers for augmented reality systems, Henry Himberg, Yuichi Motai, (electronic book)

Label
Latency and distortion of electromagnetic trackers for augmented reality systems
Title
Latency and distortion of electromagnetic trackers for augmented reality systems
Statement of responsibility
Henry Himberg, Yuichi Motai
Creator
Contributor
Author
Subject
Language
eng
Summary
Augmented reality (AR) systems are often used to superimpose virtual objects or information on a scene to improve situational awareness. Delays in the display system or inaccurate registration of objects destroy the sense of immersion a user experiences when using AR systems. AC electromagnetic trackers are ideal for these applications when combined with head orientation prediction to compensate for display system delays. Unfortunately, these trackers do not perform well in environments that contain conductive or ferrous materials due to magnetic field distortion without expensive calibration techniques. In our work we focus on both the prediction and distortion compensation aspects of this application, developing a small footprint predictive filter for display lag compensation and a simplified calibration system for AC magnetic trackers. In the first phase of our study we presented a novel method of tracking angular head velocity from quaternion orientation using an Extended Kalman Filter in both single model (DQEKF) and multiple model (MMDQ) implementations. In the second phase of our work we have developed a new method of mapping the magnetic field generated by the tracker without high precision measurement equipment. This method uses simple fixtures with multiple sensors in a rigid geometry to collect magnetic field data in the tracking volume. We have developed a new algorithm to process the collected data and generate a map of the magnetic field distortion that can be used to compensate distorted measurement data
Cataloging source
CaBNVSL
http://library.link/vocab/creatorName
Himberg, Henry
Dewey number
006.8
Illustrations
illustrations
Index
no index present
LC call number
QA76.9.A94
LC item number
H555 2014
Literary form
non fiction
Nature of contents
  • dictionaries
  • abstracts summaries
  • bibliography
http://library.link/vocab/relatedWorkOrContributorName
Motai, Yuichi.
http://library.link/vocab/subjectName
  • Augmented reality
  • Tracking (Engineering)
Target audience
  • adult
  • specialized
Label
Latency and distortion of electromagnetic trackers for augmented reality systems, Henry Himberg, Yuichi Motai, (electronic book)
Instantiates
Publication
Bibliography note
Includes bibliographical references (pages 163-171)
Color
multicolored
Contents
  • 1. Delta quaternion extended Kalman filter -- 1.1 Introduction -- 1.2 Related work -- 1.3 Background on orientation prediction -- 1.3.1 Extended Kalman filter -- 1.3.2 Quaternions -- 1.4 Filter design -- 1.4.1 Motion model -- 1.4.2 Quaternion framework -- 1.4.3 Delta quaternion framework -- 1.4.4 Quaternion prediction -- 1.4.5 Comparison of filter design -- 1.5 Experimental analysis -- 1.5.1 Experimental data -- 1.5.2 Tuning -- 1.5.3 Execution time -- 1.5.4 Prediction accuracy -- 1.5.5 Noise performance -- 1.6 Summary --
  • 2. Multiple model delta quaternion filter -- 2.1 Introduction -- 2.2 Related work -- 2.3 Background -- 2.3.1 Quaternions and delta quaternion -- 2.3.2 Extended Kalman filter -- 2.3.3 Interacting multiple model estimator -- 2.4 Filter design -- 2.4.1 MMDQ design -- 2.4.2 Delta quaternion filter design -- 2.4.3 Orientation prediction -- 2.5 Experimental results -- 2.5.1 MMDQ configuration -- 2.5.2 TPM initialization -- 2.5.3 Measurement noise -- 2.5.4 Process noise -- 2.5.5 Angular velocity estimation -- 2.5.6 Prediction performance -- 2.5.7 Computational requirements -- 2.6 Summary --
  • 3. Interpolation volume calibration -- 3.1 Introduction -- 3.2 Previous work -- 3.2.1 Managing the tracking volume -- 3.2.2 Single sensor compensation -- 3.2.3 Polynomial function methods -- 3.2.4 Look-up-table methods -- 3.2.5 Multiple sensor techniques -- 3.3 Background -- 3.3.1 Quaternions -- 3.3.2 AC magnetic tracking -- 3.4 Field mapping using IVC -- 3.4.1 Interpolation volume -- 3.4.2 Mapping fixture -- 3.4.3 Field data collection -- 3.4.4 Look-up-table (LUT) generation -- 3.5 Experimental results -- 3.5.1 PnO estimation using interpolation -- 3.5.2 Mapping fixture accuracy -- 3.5.3 Field data collection -- 3.5.4 Look-up-table (LUT) generation -- 3.6 Summary --
  • 4. Conclusion -- A. The Delta Quaternion Extended Kalman Filter (DQEKF) -- B. Multiple Model Delta Quaternion Filter (MMDQ) -- C. Interpolation Volume Calibration (IVC) -- D. MatLab Library -- References -- Authors' biographies
Control code
201404ASE012
Dimensions
unknown
Extent
1 PDF (xv, 173 pages)
File format
multiple file formats
Form of item
online
Isbn
9781627055086
Other control number
10.2200/S00580ED1V01Y201404ASE012
Other physical details
illustrations.
Reformatting quality
access
Specific material designation
remote
System details
System requirements: Adobe Acrobat Reader
Label
Latency and distortion of electromagnetic trackers for augmented reality systems, Henry Himberg, Yuichi Motai, (electronic book)
Publication
Bibliography note
Includes bibliographical references (pages 163-171)
Color
multicolored
Contents
  • 1. Delta quaternion extended Kalman filter -- 1.1 Introduction -- 1.2 Related work -- 1.3 Background on orientation prediction -- 1.3.1 Extended Kalman filter -- 1.3.2 Quaternions -- 1.4 Filter design -- 1.4.1 Motion model -- 1.4.2 Quaternion framework -- 1.4.3 Delta quaternion framework -- 1.4.4 Quaternion prediction -- 1.4.5 Comparison of filter design -- 1.5 Experimental analysis -- 1.5.1 Experimental data -- 1.5.2 Tuning -- 1.5.3 Execution time -- 1.5.4 Prediction accuracy -- 1.5.5 Noise performance -- 1.6 Summary --
  • 2. Multiple model delta quaternion filter -- 2.1 Introduction -- 2.2 Related work -- 2.3 Background -- 2.3.1 Quaternions and delta quaternion -- 2.3.2 Extended Kalman filter -- 2.3.3 Interacting multiple model estimator -- 2.4 Filter design -- 2.4.1 MMDQ design -- 2.4.2 Delta quaternion filter design -- 2.4.3 Orientation prediction -- 2.5 Experimental results -- 2.5.1 MMDQ configuration -- 2.5.2 TPM initialization -- 2.5.3 Measurement noise -- 2.5.4 Process noise -- 2.5.5 Angular velocity estimation -- 2.5.6 Prediction performance -- 2.5.7 Computational requirements -- 2.6 Summary --
  • 3. Interpolation volume calibration -- 3.1 Introduction -- 3.2 Previous work -- 3.2.1 Managing the tracking volume -- 3.2.2 Single sensor compensation -- 3.2.3 Polynomial function methods -- 3.2.4 Look-up-table methods -- 3.2.5 Multiple sensor techniques -- 3.3 Background -- 3.3.1 Quaternions -- 3.3.2 AC magnetic tracking -- 3.4 Field mapping using IVC -- 3.4.1 Interpolation volume -- 3.4.2 Mapping fixture -- 3.4.3 Field data collection -- 3.4.4 Look-up-table (LUT) generation -- 3.5 Experimental results -- 3.5.1 PnO estimation using interpolation -- 3.5.2 Mapping fixture accuracy -- 3.5.3 Field data collection -- 3.5.4 Look-up-table (LUT) generation -- 3.6 Summary --
  • 4. Conclusion -- A. The Delta Quaternion Extended Kalman Filter (DQEKF) -- B. Multiple Model Delta Quaternion Filter (MMDQ) -- C. Interpolation Volume Calibration (IVC) -- D. MatLab Library -- References -- Authors' biographies
Control code
201404ASE012
Dimensions
unknown
Extent
1 PDF (xv, 173 pages)
File format
multiple file formats
Form of item
online
Isbn
9781627055086
Other control number
10.2200/S00580ED1V01Y201404ASE012
Other physical details
illustrations.
Reformatting quality
access
Specific material designation
remote
System details
System requirements: Adobe Acrobat Reader

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