Surfaces of stored grain bulk are often reconstructed from organized point sets with noise by 3-D laser scanner in an online measuring system.As 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 surface.Based 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 distinguish.The point cloud data with those noisy points were projected into a horizontal plane.An image denoising method,discrete wavelet threshold(DWT)method,was applied to delete the third type of the noisy points.Three kinds of denoising methods including average filtering method,median filtering method and DWT method were applied respectively and compared for denoising the point cloud data.Experimental 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 methods.Furthermore,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 surface.The 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.
The simplification of 3D laser scanning point cloud is an important step of surface reconstruction and volume estimation of bulk grain in granary.This study presented an adaptive simplification algorithm based on particle swarm optimization(PSO).It introduced PSO into the average distance method,a conventional simplification method.The basic idea of this algorithm was to adaptively determine the optimal point reducing intervals of scanning lines according to original point cloud density by PSO.By using the 3D point cloud scanned from bulk grain surface in granary,the proposed algorithm was validated.Compared with the average distance method,the proposed algorithm obtained more evenly distributed point set,smaller reduction ratio(6.96%)and higher volume estimation accuracy(relative error was less than 3‰).The 3D laser scanner(GSLS003,Jilin University and SkyViTech Co.,Ltd.,Hangzhou,China)used in this study could scan the complete picture of the grain surface in a granary in one time,so the acquired point cloud data do not have to be jointed.For the good simplification performance and capability of updating the reducing interval at any moment,the proposed algorithm and the 3D laser scanner could be used to realize online real-time measurement of stored bulk grain volume in granary.
Shao QingXu TaoYoshino TatsuoZhao YujieYang WentingZhu Hang
