Classified denoising method for laser point cloud data of stored grain bulk surface based on discrete wavelet threshold

作     者：Shao Qing Xu Tao Yoshino Tatsuo Song Nan Zhu Hang 

作者机构：School of Mechanical Science and EngineeringJilin UniversityChangchun 130022China 

出 版 物：《International Journal of Agricultural and Biological Engineering》 (国际农业与生物工程学报（英文）)

年 卷 期：2016年第9卷第4期

页      面：123-131页

核心收录：

学科分类：0710[理学-生物学] 0810[工学-信息与通信工程] 08[工学] 0805[工学-材料科学与工程（可授工学、理学学位）] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：National Natural Science Foundation of China(No.50975121) Jilin Province Science and Technology Development Plan Item(No.20130522150JH) 2013 Jilin Province Science Foundation for Post Doctorate Research(No.RB201361) 

主　　题：point cloud data denoising grid method discrete wavelet threshold(DWT)method 3-D laser scanning stored grain 

摘      要：Surfaces of stored grain bulk are often reconstructed from organized point sets with noise by 3-D laser scanner in an online measuring *** a result,denoising is an essential procedure in processing point cloud data for more accurate surface reconstruction and grain volume calculation.A classified denoising method was presented in this research for noise removal from point cloud data of the grain bulk *** on the distribution characteristics of cloud point data,the noisy points were divided into three types:The first and second types of the noisy points were either sparse points or small point cloud data deviating and suspending from the main point cloud data,which could be deleted directly by a grid method;the third type of the noisy points was mixed with the main body of point cloud data,which were most difficult to *** point cloud data with those noisy points were projected into a horizontal *** image denoising method,discrete wavelet threshold(DWT)method,was applied to delete the third type of the noisy *** kinds of denoising methods including average filtering method,median filtering method and DWT method were applied respectively and compared for denoising the point cloud *** results show that the proposed method remains the most of the details and obtains the lowest average value of RMSE(Root Mean Square Error,0.219)as well as the lowest relative error of grain volume(0.086%)compared with the other two ***,the proposed denoising method could not only achieve the aim of removing noisy points,but also improve self-adaptive ability according to the characteristics of point cloud data of grain bulk *** results from this research also indicate that the proposed method is effective for denoising noisy points and provides more accurate data for calculating grain volume.