Point Cloud Data Matching and Processing

In point cloud data matching processing, various algorithms can be employed. Among these, the Iterative Closest Point (ICP) algorithm serves as a widely-used classical method that operates by iteratively finding the transformation matrix that minimizes the distance between two point clouds, thereby achieving point cloud registration. The algorithm implementation typically involves key steps: point correspondence establishment through nearest neighbor search, transformation matrix calculation using singular value decomposition (SVD) or least squares optimization, and iterative convergence checking. Although ICP algorithm demonstrates good accuracy and reliability in many scenarios, it does present certain limitations. For instance, when point clouds have significantly different initial poses, the ICP algorithm may converge to local minima, resulting in registration failure. Consequently, researchers have developed numerous enhanced versions of the ICP algorithm, incorporating techniques such as point filtering, normal vector constraints, and robust loss functions to address diverse application requirements and challenges. These improvements include feature-based initialization, multi-resolution approaches, and outlier rejection mechanisms to enhance convergence stability and registration accuracy.