Robust Motion Averaging for Multi-view Registration of Point Sets Based Maximum Correntropy Criterion

• URL: http://arxiv.org/abs/2208.11327v1
• Date: Wed, 24 Aug 2022 06:49:43 GMT
• Title: Robust Motion Averaging for Multi-view Registration of Point Sets Based Maximum Correntropy Criterion
• Authors: Yugeng Huang, Haitao Liu, Tian Huang
• Abstract summary: We propose a novel motion averaging framework for the multi-view registration with Laplacian kernel-based maximum correntropy criterion (LMCC) Our method achieves superior performance in terms of efficiency, accuracy and robustness.
• Score: 4.318555434063273
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: As an efficient algorithm to solve the multi-view registration problem,the motion averaging (MA) algorithm has been extensively studied and many MA-based algorithms have been introduced. They aim at recovering global motions from relative motions and exploiting information redundancy to average accumulative errors. However, one property of these methods is that they use Guass-Newton method to solve a least squares problem for the increment of global motions, which may lead to low efficiency and poor robustness to outliers. In this paper, we propose a novel motion averaging framework for the multi-view registration with Laplacian kernel-based maximum correntropy criterion (LMCC). Utilizing the Lie algebra motion framework and the correntropy measure, we propose a new cost function that takes all constraints supplied by relative motions into account. Obtaining the increment used to correct the global motions, can further be formulated as an optimization problem aimed at maximizing the cost function. By virtue of the quadratic technique, the optimization problem can be solved by dividing into two subproblems, i.e., computing the weight for each relative motion according to the current residuals and solving a second-order cone program problem (SOCP) for the increment in the next iteration. We also provide a novel strategy for determining the kernel width which ensures that our method can efficiently exploit information redundancy supplied by relative motions in the presence of many outliers. Finally, we compare the proposed method with other MA-based multi-view registration methods to verify its performance. Experimental tests on synthetic and real data demonstrate that our method achieves superior performance in terms of efficiency, accuracy and robustness.

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