基于面阵三维成像激光雷达的目标点云分割技术

针对偏振三维成像系统的高效目标三维点云分割问题,提出一种多维信息融合的高效分割理念。系统采用高分辨率EMCCD相机作为面阵探测器,在一次成像过程中,可同时获得视场中的灰度图像以及三维点云数据。根据该成像特点,建立灰度图的像素坐标与点云数据像素坐标之间的点对点映射关系,结合粒子群优化算法的边缘分割方法,将灰度图中目标分割后的坐标信息映射到三维点云数据中,得到其三维点云数据。该方法将三维点云数据降维处理为二维图像处理,显著降低了计算复杂度,避免了点云数据误差对分割精度造成的影响。实验验证了多维数据融合目标三维点云分割方法的有效性。

Point Cloud Segmentation Method Based on Planar Array 3D Imaging Lidar System

Aiming at the problem of efficient target 3D point cloud segmentation in polarization-modulated 3D imaging system, an efficient segmentation concept of multi-dimensional information fusion is proposed. The system uses a high-resolution EMCCD camera as a planar array detector, during an imaging cycle, the gray image in the field-of-view and the 3D point cloud data can be obtained simultaneously. According to the imaging characteristics, the point-to-point mapping relationship between pixel coordinates of gray image and pixel coordinates of point cloud data is established. Combining with the image edge segmentation method by using particle swarm optimization algorithm, the coordinate information of the target after segmentation is mapped to the 3D point cloud data, so its 3D point cloud data is obtained. In this method, 3D point cloud data processing is reduced to 2D image processing, which significantly reduces the computational complexity and avoids the influence of distance noise on segmentation accuracy. The effectiveness of the method is verified by experiments.