融合轮廓特征的线激光点云的快速配准算法  

Fast registration algorithm combining contour features for line laser point clouds

作　　者：孙炜[1] 苑河南 刘乃铭 刘权利 舒帅 Sun Wei;Yuan Henan;Liu Naiming;Liu Quanli;Shu Shuai(College of Electrical and Information Engineering,Hunan University,Changsha 410000,China)

机构地区：[1]湖南大学电气与信息工程学院,长沙410000

出　　处：《电子测量与仪器学报》2021年第7期156-162,共7页Journal of Electronic Measurement and Instrumentation

基　　金：国家自然科学基金项目(U1813205);湖南省科技计划项目(2020GK2025);湖南大学汽车车身先进设计制造国家重点实验室自主研究项目;电子制造业智能机器人技术湖南省重点实验室开放课题资助。

摘　　要：点云配准是计算机三维视觉的研究热点。传统点云配准算法存在着配准时间长和配准成功率低的问题,针对上述问题,设计了融合轮廓特征的线激光点云配准算法。该算法通过搜索轮廓特征关键点,并将这些关键点用于配准迭代并计算配准结果,减少了迭代次数且对源点云和目标点云初始位置要求较低。实验对比了迭代最近点(ICP)算法、Fast ICP算法和改进的点云配准算法,实验结果表明改进的点云配准算法的配准效果明显改善,与ICP和Fast ICP算法相比,改进的点云配准算法在速度上分别提高了14倍和2倍,并且未出现配准失败的情况。The line laser sensor is a new type of sensor that has been more widely used in recent years. It has the advantages of non-contact, high accuracy and fast speed, which can acquire high-resolution point cloud data in a short time. However, the traditional point cloud registration algorithm has the problems of low registration accuracy and long registration time when processing the line laser point cloud, which make it difficult to meet the requirements of production time and accuracy in practical application scenarios. In this paper, we propose a line laser point cloud registration method that combines contour features to extract the contour features of the line laser and utilize them as the key points for point cloud registration iteratively, and experiments are conducted to compare the traditional iterative closest point. The experimental results demonstrate that our method has great potential for application because of its high accuracy and short registration time.

关 键 词：线激光点云 配准 迭代最近点 轮廓特征 

分 类 号：TN911.73[电子电信—通信与信息系统] TP391[电子电信—信息与通信工程]