智能科学与技术学报2025，Vol.7Issue(4)：484-492,9.DOI:10.11959/j.issn.2096-6652.202534
点云数据去噪的自适应联合滤波方法
An adaptive joint filtering method for point cloud denoising
摘要
Abstract
To address the problem of severe noise in LiDAR point cloud data collected under adverse weather conditions and the low accuracy of traditional denoising methods,an adaptive joint filtering method based on local features and spa-tial distance relationships is proposed.First,an intensity threshold is set based on the characteristics of snowflake noise to distinguish potential noise points from valid points.Then,the spatial distribution of snowflake noise is modeled using a log-normal distribution,dividing the noise regions into three types.Corresponding denoising strategies are designed ac-cording to the characteristics of noise points in different regions.In high-density regions,an improved DSOR algorithm is applied,incorporating the intensity information of points into the threshold calculation,enabling the algorithm to consider both spatial structure and intensity features.In low-density regions,dynamic thresholds are set based on the local geomet-ric features and neighborhood density of points to filter and remove isolated noise points.Finally,the filtered point clouds from high-and low-density regions are merged with the point cloud from noise-free regions to obtain the complete denoised point cloud.Experimental results show that the adaptive joint filtering method improves precision by 5.16%and recall by ap-proximately 3.29%compared to DSOR,thereby effectively reducing the number of missed noise points.Compared with DDIOR,which also incorporates intensity information,the proposed method maintains a high precision while further en-hancing the ability to identify noise points,achieving more stable denoising performance across different traffic scenarios.关键词
恶劣天气/点云去噪/自适应联合滤波/DSOR/反射强度Key words
adverse weather/point cloud denoising/adaptive joint filtering/DSOR/intensity分类
信息技术与安全科学引用本文复制引用
曲大义,韦良帅,王可栋,张智,李文杰..点云数据去噪的自适应联合滤波方法[J].智能科学与技术学报,2025,7(4):484-492,9.基金项目
国家自然科学基金项目(No.52272311) The National Natural Science Foundation of China(No.52272311) （No.52272311）
