A fast registration method for multi-view point clouds with low overlap in robotic measurement

With the rapid advancement of mechanical automation and intelligent processing technology, accurately measuring the surfaces of complex parts has emerged as a significant research challenge. Robotic measurement technology plays a crucial role in facilitating rapid quality inspections during the manufacturing process due to its inherent flexibility. However, the irregular shapes and viewpoint occlusions of complex parts complicate precise measurement. To address these challenges, this work proposes a point cloud registration network for robotic scanning systems and introduces a DBR-Net (Dual-line Registration Network) to overcome the issues of low overlap rates and perspective occlusion that currently impede the registration of certain workpieces. First, feature extraction is performed using a bilinear encoder and multi-level feature interactions of both point-wise and global features. Subsequently, the features are sampled through unanimous voting and fed into the RANSAC (Random Sample Consensus) algorithm for pose estimation, enabling multi-view point cloud registration. Experimental results demonstrate that this method significantly outperforms many existing techniques in terms of feature extraction and registration accuracy, thereby enhancing the overall performance of point cloud registration.