  In this paper, we propose a novel probabilistic variant of iterative closest
point (ICP) dubbed as CoBigICP. The method leverages both local geometrical
information and global noise characteristics. Locally, the 3D structure of both
target and source clouds are incorporated into the objective function through
bidirectional correspondence. Globally, error metric of correntropy is
introduced as noise model to resist outliers. Importantly, the close
resemblance between normal-distributions transform (NDT) and correntropy is
revealed. To ease the minimization step, an on-manifold parameterization of the
special Euclidean group is proposed. Extensive experiments validate that
CoBigICP outperforms several well-known and state-of-the-art methods.
