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Performance analysis of total least squares methods in three-dimensional motion estimation

DSpace at IIT Bombay

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Title Performance analysis of total least squares methods in three-dimensional motion estimation
 
Creator CHAUDHURI, SUBHASIS
CHATTERJEE, SHANKAR
 
Subject artificial intelligence
estimation theory
matrix algebra
parameter estimation
pattern recognition
picture processing
 
Description An algorithm is presented to obtain the total least squares (TLS) estimates of the motion parameters of an object from range/stereo data or perspective views in a closed form. TLS estimates are suitable when data in both time frames are corrupted by noise, which is an appropriate model for motion analysis in practice. The robustness of different linear least squares methods is analyzed for the estimation of motion parameters against the sensor noise and possible mismatches in establishing object feature point correspondence. As the errors in point correspondence increase, the performance of an ordinary least squares (LS) estimator was found to deteriorate much faster than that of the TLS estimator. The Cramer-Rao lower bound (CRLB) of the error covariance matrix was derived for the TLS model under the assumption of uncorrelated additive Gaussian noise. The CRLB for the TLS model is shown to be always higher than that for the LS model.
 
Publisher IEEE
 
Date 2008-11-21T10:38:45Z
2011-11-25T12:50:19Z
2011-12-26T13:07:50Z
2011-12-27T05:55:49Z
2008-11-21T10:38:45Z
2011-11-25T12:50:19Z
2011-12-26T13:07:50Z
2011-12-27T05:55:49Z
1991
 
Type Article
 
Identifier IEEE Transactions on Robotics and Automation 7(5), 707-14
1042-296X
http://dx.doi.org/10.1109/70.97884
http://hdl.handle.net/10054/93
http://dspace.library.iitb.ac.in/xmlui/handle/10054/93
 
Language en_US