A Probabilistic Method for Aligning and Merging Range Images with Anisotropic Error Distribution

This paper describes a probabilistic method of aligning and merging range images. We formulate these issues as problems of estimating the maximum likelihood. By examining the error distribution of a range finder, we model it as a normal distribution along the line of sight. To align range images, our method estimates the parameters based on the expectation maximization (EM) approach. By assuming the error model, the algorithm is implemented as an extension of the iterative closest point (ICP) method. For merging range images, our method computes the signed distances by finding the distances of maximum likelihood. Since our proposed method uses multiple correspondences for each vertex of the range images, errors after aligning and merging range images are less than those of earlier methods that use one-to-one correspondences. Finally, we tested and validated the efficiency of our method by simulation and on real range images.