Point Cloud Registration Experiment: Implementing Alignment of Two Point Cloud Datasets Using S-ICP Algorithm

In this study, we conducted experimental registration of two point cloud datasets using the S-ICP (Statistical Iterative Closest Point) algorithm. Point cloud registration serves as a fundamental technology with broad applications in 3D reconstruction, computer vision, and robotics domains. The S-ICP algorithm represents one of the commonly employed methods for point cloud registration, effectively balancing computational efficiency with registration accuracy. During our experiment, we implemented the S-ICP algorithm for point cloud alignment and performed comprehensive analysis and discussion based on experimental data. The implementation typically involves key steps such as point cloud preprocessing, nearest neighbor search using KD-tree structures, transformation matrix estimation through singular value decomposition (SVD), and iterative optimization until convergence criteria are met. Through this experimental study, we gained deeper insights into the underlying principles and practical applications of the S-ICP algorithm, providing valuable reference for research in related fields. The code implementation demonstrates robust handling of point cloud data structures and efficient optimization techniques for achieving precise spatial alignment.