Abstract: For the pair-wise registration of color point clouds, we proposed a color point cloud registration algorithm based on Correntropy to enhance the robustness and accuracy of traditional registration methods. On the basis of the iterative closest point algorithm, the proposed algorithm employed hue of HSV color space combined with the traditional three-dimensional coordinates to form a four-dimensional space to assist registration, and utilized Correntropy to reduce the impact of outliers and noise on registration, so as to achieve more accurate registration results. After the pair-wise registration was completed, the result calculated by this algorithm was taken as the initial value of multi-view registration, and then a more accurate multi-view registration result was achieved through the motion average algorithm that reduced the cumulative error. Experimental results of pair-wise registration show that the proposed approach is superior in accuracy and robustness compared with other approaches. In addition, the experimental results of simulated data and real data on public datasets show that the result computed by this algorithm can be well combined with the motion average algorithm as the initial value, and that the reliable results of multi-view point cloud registration can be obtained. 

Key words:  , Correntropy, color point cloud, hue, iterative closest point algorithm, motion average algorithm