An Adaptive Data Representation for Robust Point-Set Registration and Merging
This work addresses the problem of robust point-set alignment and merging for applications like reconstruction and mapping, offering incremental improvements in convergence and robustness.
The paper tackles robust point-set registration and merging by introducing a continuous data representation using a one-class SVM and Gaussian mixture model, achieving improved robustness to noise, outliers, and occlusions compared to existing methods like ICP and Gaussian mixture approaches, as demonstrated on 2D and 3D datasets.
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.