Robust 3D point cloud registration based on bidirectional Maximum Correntropy Criterion.

ABSTRACT: This paper presents a robust 3D point cloud registration algorithm based on bidirectional Maximum Correntropy Criterion (MCC). Comparing with traditional registration algorithm based on the mean square error (MSE), using the MCC is superior in dealing with complex registration problem with non-Gaussian noise and large outliers. Since the MCC is considered as a probability measure which weights the corresponding points for registration, the noisy points are penalized. Moreover, we propose to use bidirectional measures which can maximum the overlapping parts and avoid the registration result being trapped into a local minimum. Both of these strategies can better apply the information theory method to the point cloud registration problem, making the algorithm more robust. In the process of im

SUBMITTER: Zhang X 

PROVIDER: S-EPMC5969772 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature