Point cloud registration in multidirectional affine transformation

Abstract

Point clouds scanned by three-dimensional lasers may be multidirectional affine transformed when the specifications for the products, laser scanners, and thermal expansion are incompatible. If a point cloud is out of order in such a case, many existing algorithms may not be suitable to solve the problem. Therefore, this paper proposes a multidirectional affine registration (MDAR) algorithm based on the statistical characteristics and shape features of point clouds. First, we transform the problem into a problem of finding certain matrix eigenvalues. In addition, the similarity of the global vector features is introduced, and the scaling factor is calculated by maximizing the similarity. Finally, using the estimated affine factors, the multidirectional affine registration is transformed into a rigid registration. Simulation results show that the MDAR algorithm has better accuracy and less time consumption than several existing algorithms.