中国激光, 2013, 40 (8): 0809001, 网络出版: 2013-07-16
一种基于法向量的点云自动配准方法 下载: 687次
Automatic Registration Algorithm for the Point Clouds Based on the Normal Vector
激光光学 三维点云 点云配准 点云法向量 最近点迭代 laser optics three dimensional point cloud point cloud registration normal vector of point cloud iterative closet point
摘要
针对无任何预知信息下的散乱点云数据配准问题,提出了一种基于点云法向量信息的自动配准算法。根据点云局部法向量的变化提取特征点,通过比较特征点的直方图特征向量获得初始匹配点对;使用随机抽样一致性(RANSAC)算法,根据刚性距离约束条件得到精确匹配点对;利用四元素法计算得到初始配准参数,采用改进的最近点迭代(ICP)算法对点云精确配准。实验结果表明了此方法的有效可行性。
Abstract
To registration problem of scanned point clouds data without any additional information, a novel normal vector based automatic registration algorithm is proposed. The feature points are extracted according to the change of local normal vector, and the initial matching points are found through histogram feature proposed in this paper. The random sample consensus (RANSAC) is used to get the accurate matching points according to the distance restriction. The initial registration parameters are computed by the quaternion, and iterative closet point (ICP) algorithm is used to get accurate result. The experimental results show that this algorithm is effective.
陶海跻, 达飞鹏. 一种基于法向量的点云自动配准方法[J]. 中国激光, 2013, 40(8): 0809001. Tao Haiji, Da Feipeng. Automatic Registration Algorithm for the Point Clouds Based on the Normal Vector[J]. Chinese Journal of Lasers, 2013, 40(8): 0809001.