An Improved ICP Algorithm for 3D Point Cloud Registration
Anguo Chen, Jinlei Zhuang, Xiuqin Han
Abstract
The rigid registration of two point clouds is a fundamental task in many areas, such as 3D reconstruction and robot navigation. The Iterative Closest Point (ICP) algorithm has been widely for this task. The basic principle of ICP algorithm is to match corresponding points between the two point clouds and compute an optimal transformation matrix that minimizes the Euclidean distance between corresponding points. A major drawback of the ICP algorithm is the sensitivity to partial overlaps often observed in 3D scans. In this regard, we propose a new rigid registration algorithm based on ICP. Firstly, the Super 4PCS algorithm is used to implement the initial alignment of point clouds to reduce the probability of ICP algorithm falling into a local optimal solution. Then, we propose a local refinement registration method by adaptively eliminating the boundary points of the overlap region of two point clouds. Our algorithm effectively solves the problem that ICP algorithm achieves poor registration accuracy or even produces an incorrect result when dealing with partial overlap data. According to the experimental results, our algorithm achieves better registration accuracy than the classical ICP algorithm.