Point cloud coarse registration method based on local feature description

Abstract Point cloud registration is usually divided into two processes: coarse registration and fine registration. Coarse registration provides initial values for fine registration. Generally speaking, the more accurate the result of coarse registration is, the better the effect of fine registration will be. In order to further improve the accuracy of coarse registration, a point cloud coarse registration framework based on local feature description is proposed. First, a key point detection method based on shape index is proposed, the local features of key points are used to match points, the point correspondences are filtered using the rigid transformation geometric consistency criterion, and the initial rigid transformation matrix and inliers can be get by using RANSAC The rigid transformation matrix is re-estimated using the inliers, and the inliers are recalculated by the new rigid transformation matrix, the above process is iterated until the error is less than the threshold or the number of iteration is reached. A more accurate rigid transformation matrix is get again through a re-estimation method based on truncation ratio, and the registration verification is completed by utilizing the compatibility of the matrix norm and the vector norm. Finally, the proposed method is verified by common datasets. The experimental results show that the proposed method can effectively improve the accuracy and stability of coarse registration.