Pseudo-Huber loss function-based affine registration algorithm of point clouds

Point cloud registration is a fundamental and important technology in the field of intelligent mobile robots, which is widely used for robot localization, environment perception, route planning, construction of 3D environments and accurate grasping of target objects. For 3D point clouds with noise and missing data, the traditional affine registration algorithms are poor in robustness and accuracy. To overcome these weaknesses, a pseudo-Huber loss function-based affine registration algorithm of 3D point clouds is proposed. Since the pseudo-Huber loss function is insensitive to noise and outliers, it is introduced to establish the nonlinear optimization model of affine transformation between two 3D point clouds. Then, the initial value of affine transformation is obtained by using independent component analysis (ICA) algorithm. After that, the nonlinear optimization model is optimized by using the framework of affine iterative closest point to achieve the optimal affine transformation. The valid of the proposed algorithm is demonstrated by several experiments, and the resulted indicate that the proposed algorithm can effectively reduce the influence of noise and missing data, and accurate affine registration can be realized.