A new implementation of the ICP algorithm for 3D surface registration using a comprehensive look up matrix

The iterative closest point algorithm is one of the most efficient algorithms for robust rigid registration of 3D data. It consists in finding the closest points between two sets of data which are then used to estimate the parameters of the global rigid transformation to register the two data sets. All the steps of the algorithm are highly dependent upon the accuracy with which correspondence pairs of points are found. In this paper, a new enhanced implementation of the ICP algorithm proposes to use a look up matrix for finding the best correspondence pairs. It results in reducing the minimum mean square error between the two data sets after registration, compared to existing implementations. The algorithm was implemented and tested to evaluate its convergence properties and robustness to noise. Performance improvements are obtained. The new algorithm has successfully been applied to register 3D medical data.