Transforming Least Squares to Weighted Least Squares for Accurate Range Image Registration

Abstract

The traditional ICP algorithm is a de facto standard approach for range image registration. While it assumes that one data set is a subset of another, this assumption is often violated in practice. As a result, a number of algorithms have been developed to first eliminate false matches due to occlusion, appearance and disappearance of points and then estimate the camera motion parameters in the least squares sense. Instead of eliminating outliers in the process of image registration, we in this paper use the generalised entropy to estimate the probability of correspondences established using the traditional closest point criterion, leading camera motion parameters to be estimated in the weighted least squares sense. For more accurate registration results, we also learn from the probability of correspondences estimated in the past. Since one point in one data set can only correspond to a single point in another, the two way constraint is also imposed. Finally the cam era motion parameters are optimized using the powerful mean field annealing scheme. A comparative study based on real images has shown that the proposed algorithm produces promising automatic range image registration results.