  This work focuses on Registration or Alignment of 3D point sets. Although the
Registration problem is a well established problem and it's solved using
multiple variants of Iterative Closest Point (ICP) Algorithm, most of the
approaches in the current state of the art still suffers from misalignment when
the \textit{Source} and the \textit{Target} point sets are separated by large
rotations and translation. In this work, we propose a variant of the Standard
ICP algorithm, where we introduce a Correntropy Relationship Matrix in the
computation of rotation and translation component which attempts to solve the
large rotation and translation problem between \textit{Source} and
\textit{Target} point sets. This matrix is created through correntropy
criterion which is updated in every iteration. The correntropy criterion
defined in this approach maintains the relationship between the points in the
\textit{Source} dataset and the \textit{Target} dataset. Through our
experiments and validation we verify that our approach has performed well under
various rotation and translation in comparison to the other well-known state of
the art methods available in the Point Cloud Library (PCL) as well as other
methods available as open source. We have uploaded our code in the github
repository for the readers to validate and verify our approach
https://github.com/aralab-unr/CoSM-ICP.
