MATLAB Implementation of ICP Algorithm for Point Cloud Registration

This document presents an implementation of the Iterative Closest Point (ICP) algorithm, a computational method specifically designed for point cloud registration - the process of merging multiple point clouds into a larger, unified point cloud structure. The algorithm's source code is developed in MATLAB and operates by calculating similarity metrics between point clouds to achieve optimal alignment. The core implementation involves two key computational phases: first, the algorithm identifies corresponding closest point pairs between two point clouds using efficient nearest-neighbor search techniques (typically implemented with k-d trees or exhaustive search methods in MATLAB). Second, it computes the optimal rigid transformation (rotation and translation matrices) that minimizes the sum of squared distances between these matched point pairs through singular value decomposition (SVD) or quaternion-based methods. While the ICP algorithm demonstrates high effectiveness in solving point cloud registration problems, it does present certain limitations. The implementation assumes rigid transformations between point clouds, meaning it may produce less accurate results when dealing with non-rigid or deformable point cloud data. Additional considerations include sensitivity to initial alignment and potential convergence to local minima, which can be addressed through implementation enhancements like multi-resolution approaches or robust point pairing strategies.