Point Cloud Registration Method Based on Bidirectional Search and Curvature Information

This paper addresses the problem of registering unorganized point clouds without prior information and proposes a robust automatic point cloud registration method. This method is designed to overcome the sensitivity of the classical Iterative Closest Point (ICP) algorithm to initial poses and its tendency to get trapped in local optima. The algorithm utilizes local information to filter feature points and construct feature descriptors, and then combines with the Random Sample Consensus (RANSAC) algorithm for coarse point cloud registration. The classical ICP algorithm is improved by using bidirectional search and constraints based on distance and curvature to obtain correct correspondences, achieving precise point cloud registration. Experimental results of point cloud registration demonstrate that this algorithm has significant advantages in terms of robustness and registration accuracy.