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.

该论文提出了一个刚性点集配准和合并的框架，使用鲁棒的连续数据表示，其中点集表示是通过训练具有高斯径向基函数内核的单类支持向量机来构造的，并随后使用高斯混合模型逼近输出函数。它使用该表示的稀疏参数化和噪声、异常值和遮挡的鲁棒性进行有效的配准算法，最小化支持向量参数化的高斯混合模型之间的 L 2 距离。