RGB-D point cloud registration via infrared and color camera

Teng Wan, Shaoyi Du, Yiting Xu, Guanglin Xu, Zuoyong Li, Badong Chen, Yue Gao

Abstract: The iterative closest point (ICP) algorithm is widely used for rigid registration for its simplicity and speed, but the registration is easy to fail when point sets lack of obvious structure variety, such as smooth surface and hemisphere. RGB-D information obtained from infrared camera and color camera could use color information to compensate the shapes, so we propose a precise new algorithm for RGB-D point cloud registration, which is an extension of ICP algorithm. First of all, we introduce the color information as a constraint condition to establish correct correspondences between point clouds. Secondly, to reduce the impact of noises and outliers, we use maximum correntropy criterion (MCC) to increase the robustness and accuracy. Thirdly, we add both color information and correntropy into our objective function model and solve it with ICP algorithm. Finally, the compared experiments on simulation and real datasets prove that our algorithm can align two smooth surfaces more accurate and robust than other point set registration algorithms.