A new 6D ICP algorithm with color segmentation: based adaptive sampling

Abstract

In ICP-based algorithms, the closest points are considered as the corresponding points. However, this method fails to find matching points accurately when the initial position of the point clouds is not sufficiently close. In this paper, we propose a new method to solve this problem using six-dimensional (6D) distance, which consists of color information and three-dimensional (3D) distance, and color distribution matching. First, before finding the corresponding points using this method, a Gaussian filter is applied on the input color image. A color based image segmentation is done on that image and then 𝑛 number of samples are randomly chosen from each segment. This process is applied in order to improve the computational time and performance. Second, corresponding point candidates are searched by solving a local minima problem using 6D distance. Then the color distribution matching is applied on these candidates to find the final corresponding point. Several experiments are conducted to evaluate the proposed method and the experimental results prove it has improved over the conventional methods.