Precise Point Set Registration Using Point-to-Plane Distance and Correntropy for LiDAR Based Localization

Guanglin Xu, Shaoyi Du, Dixiao Cui, Sirui Zhang, Badong Chen, Xuetao Zhang, Jianru Xue, Yue Gao

Abstract: In this paper, we propose a robust point set registration algorithm which combines correntropy and point-to-plane distance, which can register rigid point sets with noises and outliers. Firstly, as correntropy performs well in handling data with non-Gaussian noises, we introduce it to model rigid point set registration problem based on point-to-plane distance; Secondly, we propose an iterative algorithm to solve this problem, which repeats to compute correspondence and transformation parameters respectively in closed form solutions. Simulated experimental results demonstrate the high precision and robustness of the proposed algorithm. In addition, LiDAR based localization experiments on automated vehicle performs satisfactory for localization accuracy and time consumption.