Point cloud registration algorithm based on curvature feature similarity

In this paper, an improved iterative closest point (ICP) algorithm based on the curvature feature similarity of the point cloud is proposed to improve the performance of classic ICP algorithm in an unstructured environment, such as alignment accuracy, robustness and stability. A K-D tree is introduced to segment the 3D point cloud for speeding up the search of nearest neighbor points using principal component analysis for coarse alignment based on the classical ICP algorithm. In the curvature calculation process, discrete index mapping and templates are taken for sampling to simplify the point cloud data and improve the registration efficiency. The nearest point of Euclidean distance is searched based on curvature feature similarity to improve alignment accuracy. The results of the point cloud alignment experiments show that the response time of the algorithm is less than 5s in the case where the curvature features of point cloud are obvious. And the alignment error can be reduced to 1% of the pre-alignment error. Therefore, the algorithm is an efficient, accurate and practical option for unstructured environment applications.