Stochastic optimization for rigid point set registration

In this paper we propose a new method for pairwise rigid point set registration. We pay special attention to noise robustness, outlier resistance and global optimal alignment. The problem of registering two point clouds in space is converted to a minimization of a nonlinear cost function. We propose a cost function that aims to reduce the impact of noise and outliers. Its definition is based on the input point sets and is directly related to the quality of a concrete rigid transform between them. In order to achieve a global optimal registration, without the need of a good initial alignment, we develop a new stochastic approach for global minimization. Tests on a variety of point sets show that the proposed registration algorithm performs very well on noisy, outlier corrupted and incomplete data.