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Estimating motion and structure from line matches: performance
obtained and beyond J. Weng, T. S. Huang, and Narendra Ahuja The performance issues of estimating motion and
structure from line correspondences are studied. An approach to optimal
estimation of motion and structure using line correspondences is presented.
To minimize the expected errors in the estimated parameters, it is necessary
to minimize the matrix-weighted discrepancy between the computed lines and
the observed lines. In order to reliably reach the global minimum solution, a
closed-form solution is computed and then used as the initial starting
condition for an iterative optimal estimation algorithm. Simulation results
show that, in the presence of noise, the accuracy of the optimal solution is
not only considerably better than that of the closed-form solutions, but it
has also reached a level that it is comparable with that of point-based
optimal algorithms. Simulations also show that the error of the optimal
solution is close to a theoretical lower error bound, the Cramer-Rao bound, which implies that there exists little room
for accuracy improvement beyond the performance obtained.
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