Iterative Closest Point Algorithm Based on Point Cloud Curvature and Density Characteristics

Obtaining accurate 3D perception from video sequences is a core topic in computer vision and robotics, as it forms the basis for subsequent scene analysis. ToF (Time of Flight) cameras have the advantages of low cost and fast imaging to assist in 3D reconstruction work. Aiming at the defects of ToF camera such as low resolution and small field of view, this paper proposes a 3D reconstruction splicing algorithm for ToF camera, which introduces the point cloud curvature and point cloud density features compared with the traditional Iterative Closest Point (ICP) algorithm and optimizes the point cloud matching based on the features. It is more suitable for point cloud splicing in the low cloud coincidence rate scenario, which can effectively reduce the number of algorithm iterations while keeping the splicing residual close to or better than ICP.