Abstract
Aiming to problems in the pairwise registration of point clouds, such as keypoints are difficult to describe accurately, corresponding points are difficult to match accurately and convergence speed is slow due to uncertainty of initial transformation matrix, we propose a novel feature descriptor based on ratio of rotational volume to describe effectively keypoints, and on the basis of the feature descriptor, we proposed an improved coarse-to-fine registration pipeline of point clouds, in which we use coarse registration to obtain a good initial transformation matrix and then use fine registration based on a modified ICP algorithm to obtain an accurate transformation matrix. Experimental results show that our proposed feature descriptor has a good robustness to rotation, noise, scale and varying mesh resolution, less storage space and faster running speed than PFH, FPFH, SHOT and RoPS descriptors, and our improved pairwise registration pipeline is very effective to solve the problems in the pairwise registration of point clouds.
Highlights
AND RELATED WORKWith the development of laser scanning technology, the capacity to capture 3D spatial data has been enhanced greatly
It is very clear that our proposed feature descriptor achieves the best accuracy in most feature descriptors and is followed by point feature histogram (PFH) and FPFH
In this paper, we propose a novel local feature descriptor based on rotational volume, which describes a ratio of volume of a geometrical model which is generated by rotating a point and its neighboring points around their normal
Summary
AND RELATED WORKWith the development of laser scanning technology, the capacity to capture 3D spatial data has been enhanced greatly. Limit to the field of view of the scanning device, each scanning can only capture partial point cloud of 3D object. In order to obtain complete 3D object, it is necessary to use registration technology to align partial point clouds into a global coordinate framework. The core work of registering partial point clouds is to find the corresponding position and orientation of a pairwise point clouds in a global coordinate framework, which is called pairwise registration [1]. The most popular pairwise registration is the Iterative Closest Point (ICP) algorithm [2], [3]. The process of search and transformation is performed iteratively until the convergence is obtained
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