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Performance evaluation of a class of M-estimators for surface parameter estimation in noisy range data

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2 Author(s)
Mirza, M.J. ; Dept. of Electr. Eng., Ohio State Univ., Columbus, OH, USA ; Boyer, K.L.

Depth maps are frequently analyzed as if the errors are normally, identically, and independently distributed. This noise model does not consider at least two types of anomalies encountered in sampling: a few large deviations in the data (outliers) and a uniformly distributed error component arising from rounding and quantization. The theory of robust statistics, which formally addresses these problems, is used in a robust sequential estimator (RSE) of the authors' design. The RSE assigns different weights to each observation based on maximum-likelihood analysis, assuming that the errors follow a t distribution which represents the outliers more realistically. This concept is extended to several well-known maximum-likelihood estimators (M-estimators). Since most M-estimators do not have a target distribution, the weights are obtained by a simple linearization and then embedded in the same RSE algorithm. Experimental results over a variety of real and synthetic range imagery are presented, and the performance of these estimators is evaluated under different noise conditions

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Robotics and Automation, IEEE Transactions on  (Volume:9 ,  Issue: 1 )