Presents an autonomous, statistically robust, sequential function approximation approach to simultaneous parameterization and organization of (possibly partially occluded) surfaces in noisy, outlier-ridden (not Gaussian), functional range data. At the core of this approach is the Robust Sequential Estimator, a robust extension to the method of sequential least squares. Unlike most existing surface characterization techniques, the authors' method generates complete surface hypotheses in parameter space. Given a noisy depth map of an unknown 3-D scene, the algorithm first selects appropriate seed points representing possible surfaces. For each nonredundant seed it chooses the best approximating model from a given set of competing models using a modified Akaike Information Criterion. With this best model, each surface is expanded from its seed over the entire image, and this step is repeated for all seeds. Those points which appear to be outliers with respect to the model in growth are not included in the (possibly disconnected) surface. Point regions are deleted from each newly grown surface in the prune stage. Noise, outliers, or coincidental surface alignment may cause some points to appear to belong to more than one surface. These ambiguities are resolved by a weighted voting scheme within a 5/spl times/5 decision window centered around the ambiguous point. The isolated point regions left after the resolve stage are removed and any missing points in the data are filled by the surface having a majority consensus in an 8-neighborhood.>

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.>

Modeling of the unknown surface, a key first step in the perception of surfaces in range images using the function approximation approach, is considered. Akaike's entropy-based information criterion (AIC) is a simple but powerful tool for choosing the best fitting model among several competing models. However, the AIC presupposes a fixed data set and a normality assumption on the error's distribution. The AIC is extended to a t-distribution noise model, which more realistically represents anomalies in the data such as outliers and quantization errors. This criterion is modified to be used with a robust sequential algorithm to accommodate the variable data size resulting from fitting different models. The modified criterion is applied to real range data, and its performance is compared with that of AIC and Consistent AIC.>