Eung-Su Kim, Soon-Yong Park

KIPS Transactions on Software and Data Engineering, Vol. 5, No. 9, pp. 401-410, Sep. 2016
10.3745/KTSDE.2016.5.9.401,   PDF Download:
Keywords: 3D Registration, Color Segmentation, 6D Distance
Abstract

3D registration is a computer vision technique of aligning multi-view range images with respect to a reference coordinate system. Various 3D registration algorithms have been introduced in the past few decades. Iterative Closest Point (ICP) is one of the widely used 3D registration algorithms, where various modifications are available nowadays. In the ICP-based algorithms, the closest points are considered as the corresponding points. However, this assumption fails to find matching points accurately when the initial pose between point clouds is not sufficiently close. In this paper, we propose a new method to solve this problem using the 6D distance (3D color space and 3D Euclidean distances). Moreover, a color segmentation-based adaptive sampling technique is used to reduce the computational time and improve the registration accuracy. Several experiments are performed to evaluate the proposed method. Experimental results show that the proposed method yields better performance compared to the conventional methods.

Statistics
Cite this article