Point cloud matching using singular value decomposition

We propose a simple, fast three-dimensional (3D) matching method that determines the best rotation matrix between non-corresponding point clouds (PCs) with no iterations. An estimated rotation matrix can be derived by the two following steps: (1) the singular value decomposition is applied to a measured data matrix, and a database matrix is constructed from the PC datasets; (2) the inner product of each left singular vector is used to produce the estimated rotation. Through experimentation, we demonstrate that the proposed method executes 3D PC matching with <4 % of the computational time of the iterative closest point algorithm with nearly identical accuracy. © 2016, ISAROB.