ICP Algorithm Implementation

In this article, we present a simple implementation of the ICP algorithm. ICP (Iterative Closest Point) is a widely used algorithm in computer vision and robotics for point cloud registration. The algorithm estimates the relative pose between two point clouds by iteratively refining a transformation matrix. Our implementation begins with an initial transformation matrix to align the point clouds, then progressively optimizes this matrix through iterations until the distance between the point clouds is minimized. We demonstrate how to implement ICP using Python, featuring key functions such as nearest neighbor search using KD-Trees for efficient point matching, and transformation estimation using singular value decomposition (SVD) for optimal rotation and translation calculation. The article includes practical code examples illustrating the algorithm's core mechanics: point correspondence establishment, transformation computation, and convergence checking. We also discuss ICP's limitations, including sensitivity to initial pose estimates and vulnerability to local minima, and suggest improvement strategies like point filtering and robust loss functions. Through this tutorial, readers will gain comprehensive understanding of ICP's fundamental principles and implementation techniques, enabling them to apply the algorithm effectively in real-world scenarios.