Circular trajectory correspondences for iterative closest point registration

Iterative closest point (ICP) is a popular algorithm for finding rigid transformations between 3D point clouds. It aims to find the rotation and translation differences between the point clouds. A key component in the algorithm is finding correspondences between the two data sets, which are then used to determine the differences. A method for finding these pairings is described, utilizing the circular trajectory of points when the cloud is being rotated. The proposed method reveals more information per iteration cycle than techniques previously used for 3D data. The method enables the use of an efficient implementation by using a simple data structure, which has the same computational complexity to build and to access as the k-d tree commonly used with nearest neighbor correspondence searches. The experimental results show that the convergence rate is superior to the original ICP based on point-to-point minimization and compares favorably to more refined and complex approaches, e.g. normal shooting with point to plane minimization. This together with the efficient implementation strategy and low amount of computation per iteration makes the circular trajectory correspondences (CTC) a valid choice for registration tasks, especially in applications where processing power is limited.