DocumentCode :
414070
Title :
Comparison between particle filter approach and Kalman filter-based technique for head tracking in augmented reality systems
Author :
Ababsa, Fakhr-eddine ; Mallem, Malik ; Roussel, David
Author_Institution :
Lab. Syst. Complexes, Evry Univ., France
Volume :
1
fYear :
2004
fDate :
26 April-1 May 2004
Firstpage :
1021
Abstract :
The major problem with augmented reality (AR) systems using see-through head mounted displays (HMD´s) is the end-to-end system delay (or latency). This delay exists because the head tracker, scene generator, and communication links require time to perform their tasks, causing a lag between the measurement of head location and the display of the corresponding virtual objects inside the HMD. One way to eliminate or reduce the latency is to predict future head locations. We propose to use optimal Bayesian algorithms for non-linear/non-Gaussian tracking problems, with a focus on particle filters to predict head motion. Particle filters are sequential Monte Carlo methods based upon point mass (or ´particle´) representation of probability densities, which can applied to any state space model, and which generalize the traditional Kalman filtering methods. A SIR particle filter is discussed and compared with the standard EKF through an illustrative example.
Keywords :
Bayes methods; Monte Carlo methods; augmented reality; helmet mounted displays; nonlinear filters; Kalman filter-based technique; Monte Carlo methods; SIR particle filter approach; augmented reality systems; communication links; end-to-end system delay; head mounted displays; head tracker; head tracking; nonGaussian tracking problems; probability densities; scene generator; state space model; virtual objects; Augmented reality; Delay effects; Delay systems; Displays; Kalman filters; Layout; Particle filters; Particle tracking; Performance evaluation; Time measurement;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Robotics and Automation, 2004. Proceedings. ICRA '04. 2004 IEEE International Conference on
ISSN :
1050-4729
Print_ISBN :
0-7803-8232-3
Type :
conf
DOI :
10.1109/ROBOT.2004.1307284
Filename :
1307284
Link To Document :
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