A Shape-based weighting strategy applied to the covariance estimation on the ICP

Abstract

In the pairwise rigid registration problem we need to find a rigid transformation that aligns two point clouds. The classical and most common solution is the Iterative Closest Point (ICP) algorithm. However, the ICP and many of its variants require that the point clouds are already coarsely aligned. We present in this work a method named Shape-based Weighting Covariance Iterative Closest Point (SWC-ICP), an improvement over the classical ICP. Our approach improves the possibility to correctly align two point clouds, regardless of the initial pose, even when there is only a partial overlapping between them, or in the presence of noise and outliers. It benefits from the local geometry of the points, encoded in second-order orientation tensors, to provide a second correspondences set to the ICP. The cross-covariance matrix computed from this set is combined with the usual cross-covariance matrix following a heuristic strategy. In order to compare our method with some recent approaches, we present a detailed evaluation protocol to rigid registration. Results show that the SWC-ICP is among the best methods compared, with superior performance in situations of wide angular displacement, even in situations of noise and outliers.