ICP Registration Using Invariant Features

This paper investigates the use of Euclidean invariant features in a generalization of iterative closest point registration of range images. Pointwisecorrespondences are chosen as the closest point with respect to a weighted linear combination of positional and feature distances. It is shown that under ideal noise-free conditions, correspondences formed using this distance function are correct more often than correspondences formed using the positional distance alone. In addition, monotonic convergence to at least a local minimum is shown to hold for this method. When noise is present, a method that automatically sets the optimal relative contribution of features and positions is described. This method trades o error in feature values due to noise against error in positions due to misalignment. Experimental results show that using invariant features decreases the probability of being trapped in a local minimum, and is most e ective for di cult registration problems where the scene is ...

• Type: Journal
• Year: 2002
• Where: PAMI
• Authors: Gregory C. Sharp, Sang Wook Lee, David K. Wehe
• Keywords: Closest Point, Iterative Closest Point, Local Minimum, PAMI 2002