移动测量的视频影像与激光点云配准方法

Registration Method of Video Image and Laser Point Cloud Under Moving Measurement

移动测量系统可同时采集三维激光点云、影像纹理及位置姿态数据,但使用影像照片与激光点云配准融合难以满足高效率和高重叠度的需求。针对这一问题,提出一种在移动测量下使用视频影像与激光点云配准的方法。利用定位定姿系统(POS)传感器提供的位置姿态数据,结合共线方程模型得到视频影像关键帧与激光点云初始配准值;利用基于Robust估计的选权迭代法提高初始配准值精度,确定配准参数的精确值;自视频影像生成立体密集匹配点云,并与三维激光点云进行最邻近迭代配准。实验结果表明,视频影像与车载激光点云的配准方法是可行的,配准精度高,能满足城市街道三维重建、部件采集、目标提取等量测应用需求。

Motion measurement system can simultaneously collect three-dimensional(3D) laser point clouds, image texture, and the position and attitude data;however the registration fusion of image photos and laser point cloud is challenging to meet the needs of high efficiency and high overlap. This paper proposes a method to register video images with laser point clouds under motion measurement to solve this problem. The positioning orientation system(POS)sensor’s position and attitude data are used to calculate the initial registration value of keyframes in a video image and a laser point cloud using a collinear equation model. The iterative method for selecting weights based on Robust estimation improves the accuracy of initial registration values and determines the exact values of registration parameters. The stereodense matching point cloud is generated from the keyframe of the video image, and the nearest neighbor iterative registration is performed using the 3D laser point cloud. The experimental results show that the registration method of video image and a vehicular laser point cloud is feasible, and the registration accuracy is high, meeting the requirements of3D reconstruction of urban streets, component acquisition, target extraction, and other measurement applications.