基于三维点云数据的预处理技术研究与应用  

Research and Application of Preprocessing Techniques Based on 3D Point Cloud Data

作　　者：王冕 颜康 罗鑫 袁娴枚 吴建蓉 范强 胡天嵩 Wang Mian;YAN Kang;LUO Xin;YUAN Xianmei;WU Jianrong;FAN Qiang;HU Tiansong(Electric Power Research Institute of Guizhou Power Grid Co.,Ltd.,Guiyang 550002,Guizhou,China)

机构地区：[1]贵州电网有限责任公司,贵州贵阳550002

出　　处：《电力大数据》2024年第9期61-68,共8页Power Systems and Big Data

基　　金：中国南方电网有限责任公司科技项目(GZKJXM20222336)。

摘　　要：该文研究了三维点云数据的预处理技术,并提出了一种改进的自适应滤波算法,以提高复杂场景下点云数据的质量和处理效率。三维点云数据广泛应用于自动驾驶、机器人视觉和三维建模等领域,但其采集过程中常受噪声和异常点影响。该文分析了激光雷达、立体视觉系统及RGB-D摄像头等多种数据采集手段的特点,详细介绍了现有的点云滤波与去噪技术,并通过实验对均值滤波、中值滤波、统计滤波和体素滤波等方法进行了比较。为了克服传统方法在复杂噪声条件下的局限性,提出了一种改进的自适应滤波算法,通过局部噪声水平估计、滤波参数动态调整和边缘检测与保留,显著提高了滤波效果和计算效率。实验结果表明,该算法在去除噪声的同时能够有效保留点云数据的边缘细节,并在多种实验场景中展现了优异性能。This paper investigates preprocessing techniques for 3D point cloud data and proposes an improved adaptive filtering algorithm to enhance the quality and processing efficiency of point cloud data in complex scenes.3D point cloud data is widely used in fields such as autonomous driving,robotic vision,and 3D modeling,but it often suffers from noise and outliers during acquisition.The paper analyzes various data acquisition methods including LiDAR,stereo vision systems,and RGB-D cameras,provides a detailed review of existing point cloud filtering and denoising techniques,and compares methods such as mean filtering,median filtering,statistical filtering,and voxel filtering through experiments.To overcome the limitations of traditional methods under complex noise conditions,this paper introduces an enhanced adaptive filtering algorithm that significantly improves filtering effectiveness and computational efficiency through local noise level estimation,dynamic adjustment of filtering parameters,and edge detection and preservation.Experimental results demonstrate that the proposed algorithm effectively removes noise while preserving edge details of the point cloud data,showcasing superior performance across various experimental scenarios.

关 键 词：三维点云数据 预处理 滤波 去噪 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]