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This work focuses on Registration or Alignment of 3D point sets. Although theRegistration problem is a well established problem and it's solved usingmultiple variants of Iterative Closest Point (ICP) Algorithm, most of theapproaches in the current state of the art still suffers from misalignment whenthe \textit{Source} and the \textit{Target} point sets are separated by largerotations and translation. In this work, we propose a variant of the StandardICP algorithm, where we introduce a Correntropy Relationship Matrix in thecomputation of rotation and translation component which attempts to solve thelarge rotation and translation problem between \textit{Source} and\textit{Target} point sets. This matrix is created through correntropycriterion which is updated in every iteration. The correntropy criteriondefined in this approach maintains the relationship between the points in the\textit{Source} dataset and the \textit{Target} dataset. Through ourexperiments and validation we verify that our approach has performed well undervarious rotation and translation in comparison to the other well-known state ofthe art methods available in the Point Cloud Library (PCL) as well as othermethods available as open source. We have uploaded our code in the githubrepository for the readers to validate and verify our approachhttps://github.com/aralab-unr/CoSM-ICP.

Registration of 3D Point Sets Using Correntropy Similarity Matrix