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

TL;DR

This paper introduces a robust motion averaging method for multi-view point set registration using a maximum correntropy criterion with Laplacian kernel, improving efficiency, accuracy, and robustness against outliers.

Contribution

The paper proposes a novel motion averaging framework based on maximum correntropy with Laplacian kernel, addressing efficiency and robustness issues of traditional methods.

Findings

Outperforms existing methods in accuracy and robustness.

Effective in handling outliers in multi-view registration.

Demonstrates superior efficiency on synthetic and real datasets.

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.