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山东理工大学 计算机科学与技术学院, 山东 淄博 255049
[ "孟晓亮(1988-), 男, 山东潍坊人, 博士, 讲师, 2018年于哈尔滨理工大学获得博士学位, 主要从事视觉检测与图像处理方面的研究。E-mail:xiaoliang@sdut.edu.cn" ]
[ "张立晔(1986-), 男, 山东济南人, 博士, 讲师, 2018年于哈尔滨工业大学获得博士学位, 主要从事机器学习与图像处理方面的研究。E-mail:zhangliye@sdut.edu.cn" ]
收稿日期:2020-05-09,
修回日期:2020-08-07,
录用日期:2020-8-7,
纸质出版日期:2020-12
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孟晓亮, 张立晔, 房超. 基于电磁追踪系统的点云配准方法研究[J]. 液晶与显示, 2020,35(12):1309-1314.
Xiao-liang MENG, Li-ye ZHANG, Chao FANG. Point cloud registration method based on electro-magnetic tracking system[J]. Chinese journal of liquid crystals and displays, 2020, 35(12): 1309-1314.
孟晓亮, 张立晔, 房超. 基于电磁追踪系统的点云配准方法研究[J]. 液晶与显示, 2020,35(12):1309-1314. DOI: 10.37188/YJYXS20203512.1309.
Xiao-liang MENG, Li-ye ZHANG, Chao FANG. Point cloud registration method based on electro-magnetic tracking system[J]. Chinese journal of liquid crystals and displays, 2020, 35(12): 1309-1314. DOI: 10.37188/YJYXS20203512.1309.
针对视觉测量中的点云配准问题,提出一种基于电磁追踪系统的点云配准方法。首先,搭建测量和配准系统,采用基于平面的坐标系转换方法获得世界坐标系与发射器坐标系之间的转换关系。其次,利用发射器和接收器之间准确的位姿信息,获得发射器坐标系和接收器坐标系之间的转换关系。最后,以发射器坐标系作为中间坐标系,将获取的点云由世界坐标系转换到接收器坐标系下,实现点云的粗配准,进而采用ICP方法实现精配准。对不同深度的平面进行测量配准实验,并使用石膏像对配准效果进行验证。实验结果表明:粗配准后平面的配准误差在0.40 mm以下,精配准后平面的配准误差在0.04 mm以下;经过粗配准后的石膏像,采用ICP方法进行精配准时其误差能够快速收敛,且精配准后视觉效果良好。
For the point cloud registration problem in the vision measurement
a point cloud registration method based on electro-magnetic tracking system is proposed in this paper. First
the measurement and registration systems are built
and coordinate system transformation relation between world coordinate system and transmitter coordinate system is calculated using coordinate system transformation method based on the flat surface. Second
the accurate position and orientation of the receiver relative to the transmitter are used to calculate the transformation relation of transmitter coordinate system and receiver coordinate system. Finally
transmitter coordinate system is taken as intermediate coordinate system. The point cloud from different view can be transformed from world coordinate system to receiver coordinate system
and the point cloud coarse registration is accomplished
then the Iterative Closest Point (ICP) method is used to conduct the fine registration. The registration experiment is conducted using the flat surface with different depth
and the plaster model is also used to verify the registration vision effect. The experiment results show that the registration error of the flat surface is below 0.40 mm after the coarse registration
and the registration error of the flat surface is below 0.04 mm after the fine registration. After the coarse registration
the error of the plaster model can converge quickly when we conduct the fine registration using the ICP method
and the vision effect is good.
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