ICP Algorithm Implementation and Enhancement with MATLAB Code

This article provides a detailed implementation of the Iterative Closest Point (ICP) algorithm using MATLAB. We conduct an in-depth analysis of both the strengths and limitations of the standard ICP approach, particularly focusing on its sensitivity to initial positions and susceptibility to local minima. Our implementation incorporates several key enhancements including point cloud preprocessing, adaptive thresholding for correspondence matching, and robust outlier rejection mechanisms. The MATLAB code features optimized vectorization techniques for efficient nearest-neighbor searches using k-d tree structures and implements a weighted least-squares transformation estimation module. Through systematic validation, we have developed a high-performance ICP program that demonstrates improved convergence stability and registration accuracy. The solution includes configurable parameters for different application scenarios and integrates visualization tools for real-time alignment monitoring. We believe this enhanced implementation will find broad applications in robotics, medical imaging, and 3D reconstruction domains, providing researchers with a reliable tool for point cloud registration challenges.