A Scale Stretch Method Based on ICP for 3D Data Registration

In this paper, we are concerned with the registration of two 3D data sets with large-scale stretches and noises. First, by incorporating a scale factor into the standard iterative closest point (ICP) algorithm, we formulate the registration into a constraint optimization problem over a 71) nonlinear space. Then, we apply the singular value decomposition (SVD) approach to iteratively solving such optimization problem. Finally, we establish a new ICP algorithm, named Scale-ICP algorithm, for registration of the data sets with isotropic stretches. In order to achieve global convergence for the proposed algorithm, we propose a way to select the initial registrations. To demonstrate the performance and efficiency of the proposed algorithm, we give several comparative experiments between Scale-ICP algorithm and the standard ICP algorithm. Note to Practitioners-In this paper, we proposed the Scale-ICP algorithm, which is used to deal with the registration between two data sets with scale stretches. In practice, there are a large number of such problems. For example, the registration between the range data sets that have different scanning resolutions determined by the distances from the sensor to the object surfaces. The data sets can be not only image data but also the other measured data, and hence it also can be extended to machining industry besides the computer vision field.