Liang Lexian, Pei Hailong
Key Laboratory of Autonomous Systems and Networked Control, Ministry of Education, Unmanned Aerial Vehicle Systems Engineering Technology Research Center of Guangdong, South China University of Technology, Guangzhou 510640, China.
Sensors (Basel). 2023 Jul 17;23(14):6475. doi: 10.3390/s23146475.
In this paper, we propose a novel affine iterative closest point algorithm based on color information and correntropy, which can effectively deal with the registration problems with a large number of noise and outliers and small deformations in RGB-D datasets. Firstly, to alleviate the problem of low registration accuracy for data with weak geometric structures, we consider introducing color features into traditional affine algorithms to establish more accurate and reliable correspondences. Secondly, we introduce the correntropy measurement to overcome the influence of a large amount of noise and outliers in the RGB-D datasets, thereby further improving the registration accuracy. Experimental results demonstrate that the proposed registration algorithm has higher registration accuracy, with error reduction of almost 10 times, and achieves more stable robustness than other advanced algorithms.
在本文中，我们提出了一种基于颜色信息和核相关熵的新型仿射迭代最近点算法，该算法能够有效处理RGB-D数据集中存在大量噪声、离群点和小变形的配准问题。首先，为了缓解几何结构较弱的数据配准精度低的问题，我们考虑将颜色特征引入传统仿射算法中，以建立更准确可靠的对应关系。其次，我们引入核相关熵度量来克服RGB-D数据集中大量噪声和离群点的影响，从而进一步提高配准精度。实验结果表明，所提出的配准算法具有更高的配准精度，误差降低了近10倍，并且比其他先进算法具有更稳定的鲁棒性。
