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

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