摘要
针对传统的迭代最近点(ICP)算法计算量较大、效率较低、易受点云初始位姿影响等缺点,提出一种基于normal distribution transform(NDT)和特征点检测的点云配准算法。该算法采用“粗细结合”的配准策略,首先对点云进行预处理;然后采用NDT算法对处理后的点云进行粗配准,为精配准提供较理想的初始位姿;再利用3D-Harris特征点检测算法提取点云特征点;最后利用ICP算法对提取特征点后的点云集进行精细配准,得到最优解。仿真结果显示,与传统算法对比,所提算法进一步提高了点云配准的效率和精确度。
This paper proposes a point cloud registration algorithm based on normal distribution transform(NDT)and feature point detection to address the shortcomings of traditional iterative closest point(ICP)algorithms,such as a large amount of calculation,low efficiency,and ease of being affected by the initial pose of the point cloud.The algorithm employs a“coarse and fine”registration strategy.First,the point cloud is preprocessed;thereafter,the NDT algorithm is used to coarsely register the processed point cloud for providing a more ideal initial pose for fine registration.Next,the 3DHarris feature point detection algorithm is used to extract the point cloud feature points.Finally,the ICP algorithm is used to finely register the point cloud set after the feature point extraction to obtain an optimal solution.The simulation results show that when compared to the traditional algorithm,the algorithm used in this paper improves the efficiency and the accuracy of point cloud registration.
作者
杨宜林
李积英
王燕
俞永乾
Yang Yilin;Li Jiying;Wang Yan;Yu Yongqian(School of Electronic and Information Engineering,Lanzhou Jiaotong University,Lanzhou,Gansu 730070,China;Gansu Industrial Transportation Automation Engineering Technology Research Center,Lanzhou,Gansu 730070,China)
出处
《激光与光电子学进展》
CSCD
北大核心
2022年第8期188-194,共7页
Laser & Optoelectronics Progress
基金
甘肃省自然科学基金(20JR5RA407)。
