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 implementation, we integrate the fixed-point optimization technique based on the iterative closest point algorithm, resulting in the correspondence and transformation parameters that are solved iteratively. The comparison experiments under noisy conditions with related algorithms have demonstrated good performance of the proposed algorithm.
Highlights
The development of scanning equipment makes the acquisition of 3D point cloud possible [1, 2]
Different from the above methods, this paper presents a robust point cloud registration algorithm based on the bidirectional Maximum Correntropy Criterion (BiMCC)
Using bidirectional measure can maximum the overlapping parts and avoid the registration result being trapped into a local minimum
Summary
The development of scanning equipment makes the acquisition of 3D point cloud possible [1, 2]. Xu et al [23] employed the correntropy and Yang et al [24] employ a non-second statistical measure in kernel space under the ICP framework, which can establish the correspondence and do the matching concurrently Their algorithms are converge to partial noisy points. Different from the above methods, this paper presents a robust point cloud registration algorithm based on the bidirectional Maximum Correntropy Criterion (BiMCC). Using bidirectional measure can maximum the overlapping parts and avoid the registration result being trapped into a local minimum Both of these two strategies can better apply the information theory method to the registration problem, making the algorithm more robust.
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