<title>Fusion of stereo views: estimating structure and motion using a robust method</title>

Abstract

This paper concerns the estimation of motion parameters and scene structure through the fusion of successive stereo pairs. While a least-squares estimator is quite stable in the presence of well-behaved noise, it gives disastrous results when the input data are contaminated with a few outliers. Due to difficulties in stereo and temporal image matching, such outliers cannot be easily eliminated within the feature matching stage. Therefore, immunity to outliers is essential to motion and structure estimation algorithms. The robust estimator described in this paper reduces the influence of outliers so that the estimates are not very sensitive to gross errors in the input data. Experiments with real world images are presented with automatically established stereo and temporal matching. The accuracy of the estimated motion and depth map of the real scene is partially validated with the ground truth. Results show that the robust estimator is stable in the presence of outliers.