Rigid registration based on local geometric dissimilarity

Abstract

This work aims to enhance a classic method for the rigid registration problem, the ICP (Iterative Closest Point), modifying one of its main steps, the closest point search, in order to consider approximated information of local geometry combined to the Euclidean distance, originally used. For this, a preprocessing stage is applied, in which the local geometry is estimated in second-order orientation tensors. We define the CTSF, a similarity factor between tensors. Our method uses a linear combination between this factor and the Euclidean distance, in order to establish correspondences, and a strategy of weight variation between both factors. This increases the convergence probability for higher angles with respect to the original ICP, making our method comparable to some of the state-of-art techniques. In order to comprove the enhancement, exhaustive tests were made in point clouds with different geometric features, with variable levels of additive noise and outliers and in partial overlapping situations, varying also the parameters of the tensor estimative method. A dataset of synthetic point clouds was defined for the experiments, as well as a statistic protocol for quantitative evaluation. The results were analyzed in order to highlight good parameter ranges for different point clouds, and how these parameters affect the behavior of the method in situations of additive noise, outliers and partial overlapping.