  This paper presents a framework for rigid point-set registration and merging
using a robust continuous data representation. Our point-set representation is
constructed by training a one-class support vector machine with a Gaussian
radial basis function kernel and subsequently approximating the output function
with a Gaussian mixture model. We leverage the representation's sparse
parametrisation and robustness to noise, outliers and occlusions in an
efficient registration algorithm that minimises the L2 distance between our
support vector--parametrised Gaussian mixtures. In contrast, existing
techniques, such as Iterative Closest Point and Gaussian mixture approaches,
manifest a narrower region of convergence and are less robust to occlusions and
missing data, as demonstrated in the evaluation on a range of 2D and 3D
datasets. Finally, we present a novel algorithm, GMMerge, that parsimoniously
and equitably merges aligned mixture models, allowing the framework to be used
for reconstruction and mapping.
