摘要
激光雷达探测点云数据中存在大量噪声点,导致三维图重建精度下降,无法完全复现物体结构,本文针对此问题提出了一种基于自适应阈值的三维点云分段式去噪方法。根据噪声点与非噪声点之间的欧式距离,将其划分为远信号噪声点和近信号噪声点两类,先后对两类噪声点分别采用基于非线性函数的阈值自适应去噪算法和基于曲率的去噪算法。基于非线性函数的阈值自适应去噪算法采用有序的栅格对无序的点云进行组织,然后计算栅格内点云密度,最后将栅格到激光雷达的距离作为输入,通过调用非线性函数计算阈值,以实现点云密度阈值的自适应调整。基于曲率的去噪算法采用K-D树对点云数据进行组织,对某点P的邻域内所有的点按曲率进行排序,计算出曲率中值,将大于曲率中值的点P视为噪声点。该方法可以有效去除点云中的噪声点,去噪精确度达到95%以上。
Auto-driving is developing rapidly nowadays.Tesla,Nio and other car manufacturers have put their level 2 autonomous driving products on the market.As a kind of important sensors in the car,lidar can directly get the distance and angle to the object.That information is organized into the form called“point cloud”.Point cloud is mainly used to rebuild the 3D scene,which plays a major role in guiding the vehicle.But due to weather and other reasons,there is a large number of noise points in the point cloud data detected by lidar,which will cause the accuracy of 3D reconstruction to decrease,and the structure of the object cannot be fully reproduced.It is dangerous while driving since the vehicle does not have enough information about its environment.So,point cloud denoising is necessary and important.To solve this problem,this paper proposes a three-dimensional point cloud denoising method based on adaptive threshold,which has two stages.According to the Euclidean distance between noise points and non-noise points,this method divides the noise points into two types:far-signal noise points and nearsignal noise points.For removing the two types of noise points,threshold adaptive denoising algorithm based on nonlinear function and denoising algorithm based on curvature are used respectively at different stages.At the first stage,the threshold adaptive denoising algorithm based on nonlinear function is to remove the far-signal noise.It uses ordered grids to organize disordered point clouds and calculates the average density of point clouds in the grid.Then,the nonlinear function whose input is the distance from the grid to the lidar is called for calculating the threshold to realize the adaptive adjustment of the denoising density threshold.The points in the grid whose density does not reach its threshold would be seen as noise points.At the second stage,the denoising algorithm based on the median is to remove the near-signal noise.It uses the K-D trees to organize the remaining point cloud data.Then all points in th
作者
任彬
崔健源
李刚
宋海丽
REN Bin;CUI Jianyuan;LI Gang;SONG Haili(School of Mechanical Engineering,Shijiazhuang Tiedao University,Shijiazhuang 050043,China;Army Engineering University,Shijiazhuang 050043 China)
出处
《光子学报》
EI
CAS
CSCD
北大核心
2022年第2期309-322,共14页
Acta Photonica Sinica
基金
河北省高等学校科学技术研究项目青年基金项目(No.QN2019232)
河北省教育厅青年拔尖人才项目(No.BJ2017047)
国防科技重点项目
石家庄铁道大学研究生创新资助项目(No.YC2021035)。
