A robust normal estimation algorithm based on statistical distance
Normal vector of point cloud has being widely used in the field of laser sensor mapping,stereoscopic vision and surface reconstruction.Because of the present of noise,classical method based on locally plane fitting could not get an accurate result and greatly decrease precision of the follow-up work.This paper proposes a robust method for normal estimation in dealing with point cloud contained noise point.We first obtain the best set,which have the maximum consistency,using the difference of statistical distance between inliers and outliers,then introduce median and the Median Absolute Deviation to remove noise point from the best set,finally get the locally best-fit-plane.Experiment results show that our method could efficiently couple with samples containing 50% noises and get accurate normal vectors.This new method is of great value in surface reconstruction,point cloud characterization,segmentation,matching or other reverse engineering task.
plane-fitting normal-estimation denoising MAD
Zuo Liying Ding Yong
School of Mechatronics Engineering Harbin Institute of Technology Harbin, 150001, China The 18th Institute China Academy of Launch Vehicle Tehchnology Beijing, 150001, China
国际会议
秦皇岛
英文
1290-1293
2015-09-18（万方平台首次上网日期，不代表论文的发表时间）