摘要
针对双Kinect扫描大型物体过程中双Kinect外参标定问题,利用改进的随机采样一致性方法提取了点云球体及平面模型的关键参数,分析了不同Kinect坐标系下的球心坐标及平面法向量间的关系,提出了一种基于球体及平面模型的双Kinect空间位置的标定方法,实现了视角位移变化较大的平移旋转矩阵的标定。相对于利用传统迭代最近点(ICP)算法及改进的ICP算法标定两个Kinect外参的方法,所提方法的速度、精度均有较大幅度提高。最后,利用实际模型的配准结果验证了所提方法的可行性,所提方法为视角位移变化较大的双Kinect外参的标定提供了一种快捷、准确的标定方案。
In order to solve the dual-Kinect external parameters calibration problem in scanning large object with dual-Kinect, we use an improved random sample consensus （RANSAC） method to extract the key parameters of sphere and plane model of the point cloud. The relationship between sphere center coordinate and normal vector of plane in different Kinect coordinate systems is analyzed. A new calibration method of dual-Kinect spatial position based on sphere and plane model is proposed. The calibration of the rotation translation matrix with large change in view and positiorr movement is realized. Compared with the traditional iterative closest point （ICP） algorithm and improved ICP algorithm to calibrate dual-Kinect external parameters, the proposed method is faster and more accurate. Finally, the feasibility of this calibration method is verified by the actual model registration results. The proposed method provides a quick and accurate calibration scheme for dual-Kinect external parameters calibration with large change in view and position movement.
作者
欧攀
周锴
吴帅
Ou Pan;Zhou Kai;Wu Shuai(School of Instrumentation Science and Opto-Electronic Engineering, Beihang University, Beijing 100191, China)
出处
《激光与光电子学进展》
CSCD
北大核心
2018年第4期366-373,共8页
Laser & Optoelectronics Progress
基金
国家自然科学基金(61675031)
关键词
测量
标定
随机采样一致性算法
深度传感器
点云
measurement
calibration
random sample consensus algorithm
depth sensor
point cloud
