Lidar Target Point Cloud Alignment Based on Improved Neighborhood Curvature with Iteration Closest Point Algorithm

To solve the problems of slow matching speed and large matching error in the precise alignment step of lidar target point cloud alignment technology, an iteration closest point (ICP) precision matching algorithm based on neighborhood curvature improvement is proposed. The registration provides a good initial position; the neighborhood curvature is introduced into the traditional ICP algorithm to achieve the fine registration. Perform registration and numerical analysis experiments on the Stanford Bunny and the scene point cloud. The experimental results demonstrate that the improved ICP algorithm based on the neighborhood curvature can efficiently perform the point cloud alignment, and compared with other algorithms, the alignment speed of the proposed algorithm is better than the alignment matching accuracy, which provides an efficient method to improve the 3D reconstruction and target recognition technology.