用于消除激光雷达点云图像弯曲的算法研究

Research on Algorithm for Eliminating Bending of Lidar Point Cloud Image

在激光雷达的光机设计中,由于各项参数需要满足实际项目的指标需求,会导致激光雷达的光源中心与机械旋转中心不重合,进而造成点云图像产生弯曲,严重影响激光雷达的性能。因此,结合三维雷达的实际扫描情况,首先分析了激光雷达点云图像产生弯曲的原因并推导出每一条扫描线的误差计算公式。其次,通过修正参数的方式,将相对坐标系下的像素点转化成世界坐标系下的深度值,从而完成坐标系的转换,最终修正点云图像的弯曲,同时结合五幅点云图像详细分析了修正后算法的可靠性。实验结果证明,在进行算法修正之后,点云图像表现为处处是平面,几乎没有弯曲,所有测试障碍物的外形轮廓没有明显变形,像素点的排列整齐有序,测距误差由原来的10.2 cm减小到2 cm以内。且雷达在监测区域内最近处[坐标为(0,1.8 m)]的横向距离分辨率达到7.5 cm,纵向距离分辨率达到4.8 cm;最远处[坐标为(25 m,4.5 m)]的横向距离分辨率达到20 cm,纵向距离分辨率达到6.1 cm。通过与传统的数据匹配和拼接模型相比,证实了坐标系转换的算法可以从根本上解决坐标中心不重合和振镜旋转引起的两种非线性误差,而且通过下雪和复杂环境下的测试实验进一步证明了算法具有很高的稳定性。

In the optomechanical design of laser radar,the light source center of lidar does not coincide with the mechanical rotation center because the parameters need to meet the requirements of the actual project,which will lead to the bending of point cloud image and seriously affect the performance of lidar. Therefore,combined with the actual scanning situation of the three-dimensional radar,first,the reason for the bending of the lidar point cloud image is analyzed and the error calculation formula for each scan line is derived. Then,by modifying the parameters,the pixels in the relative coordinate system are transformed into the depth values in the world coordinate system,so as to complete the transformation of the coordinate system. Finally,the bending of the point cloud image is corrected. At the same time,the reliability of the modified algorithm is analyzed in detail combined with five point cloud images. Experimental results show that after correcting the algorithm, the point cloud image is flat everywhere,almost no bending,the contour of all test obstacles has no obvious deformation,the pixel points are arranged orderly,and the ranging error is reduced from 10. 2 cm to less than 2 cm. In addition,the lateral range resolution of the nearest(0,1. 8 m) and the longitudinal range resolution of the radar in the monitoring area are7. 5 cm and 4. 8 cm,respectively,and the lateral range and longitudinal range resolution of the farthest point(25 m,4. 5 m)are 20 cm and 6. 1 cm,respectively. Compared with the traditional data matching and splicing model,it is proved that the proposed algorithm of coordinate system transformation can fundamentally solve the two kinds of nonlinear errors caused by the misalignment of coordinate centers and the rotation of galvanometer. Moreover,it is proved that the algorithm has high stability through the test experiments in snow and complex environment.