Learning anisotropic ICP (LA-ICP) for robust and efficient 3D registration

Abstract

This paper presents an online learning approach to 3D object registration that vastly improves the performance of Iterative Closest Point (ICP) methods. Our approach achieves better robustness and stable convergence by learning generalized distance functions directly from a stream of object depth data. The proposed algorithm, Learning Anisotropic ICP (LA-ICP), parameterizes the point uncertainty of the underlying object surface as an anisotropic Gaussian, and estimates the covariance parameters of the likelihood function for ICP from data. Our learning scheme does not require manual tuning and the parameters of the algorithm are continually updated from observed data. Experiments on various RGB-D object datasets demonstrate the effectiveness of our approach in terms of convergence and pose accuracy as well as robustness to initial conditions.