Point cloud registration algorithm based on neighborhood feature similarity for contour fitting

This paper introduces a point cloud registration algorithm based on neighborhood feature similarity, specifically targeting the challenge of quality optimization in component assembly within the modern manufacturing industry, particularly for precise contour fitting. By enhancing the classic Iterative Closest Point (ICP) algorithm, the study focuses on feature extraction of contour point clouds and improving fitting accuracy. The algorithm incorporates neighborhood feature similarity based on curvature characteristics and multi-neighborhood normal vector variation, along with a loss function design based on contour deviation. Experiments on fitting actual headlight contour point clouds with CAD theoretical model point clouds demonstrate the algorithm's high precision and faster iteration speed in aligning narrow-band point clouds with unclear feature points, significantly outperforming traditional ICP and its variants. The research not only enhances component assembly quality but also provides an effective tool for contour point cloud fitting in complex manufacturing scenarios.