1. 西北大学 信息科学与技术学院,陕西,西安,710127
2. 咸阳师范学院 机构 教育科学学院,陕西,咸阳,712000
3. 北京师范大学 信息科学与技术学院,北京,100875
纸质出版:2017
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赵夫群, 周明全, 耿国华. 基于有界旋转角的点云配准算法[J]. 微电子学与计算机, 2017, 34(3): 46-49.
ZHAO Fu-qun, ZHOU Ming-quan, GENG Guo-hua. Point Cloud Registration Algorithm Based on Bounded Rotation Angle[J]. 2017, 34(3): 46-49.
针对目前已有的三维点云配准算法不能很好地解决旋转角度变化较大的配准问题
提出一种基于有界旋转角的配准算法.首先根据刚体变换的低维性质将全局配准问题转化成区域配准问题
通过区域划分、区域配准、求解组合系数、求解刚体变换等四个基本步骤实现初始配准; 然后在基本ICP算法的基础上通过设置旋转角度的边界来改进算法
以达到精确配准的目的.实验结果表明
该算法具有精确、快速、鲁棒的特点
并能避免噪声和外点的干扰
适用范围更加广泛.
In the paper
a registration algorithm based on bounded rotation angle is proposed to solve the problem of the existing 3D point cloud algorithm which cant complete registration with large rotation angle change. Firstly
the global registration is transformed into regional registration according to the low dimensional nature of the rigid body transformation
and it achieves initial registration through the four steps of region division
region registration
solving combination coefficient and solving rigid body transformation; Secondly
the basic ICP algorithm is improved by setting boundary for rotation angle to achieve fine registration. The experimental results show that the proposed algorithm in the paper is accurate
fast and robust
and it can avoid the interference of noises and outliers.
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卫维恕, 罗笑南, 李峥. 基于水平集的扫描点云简化算法及在人体模型上的应用[J]. 计算机研究与发展, 2008, 45(9): 1605-1611. DOI: 1605
Zhang Chunjia, Du Shaoyi, Liu Juan. Robust 3D point set registration using iterative closest point algorithm with bounded rotation angle[J]. Signal Processing, 2016, 120(C): 777-788. DOI: 777
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