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作 者:吴志才 魏冠军 党空雁 赵丰炯 Wu Zhicai;Wei Guanjun;Dang Kongyan;Zhao Fengjiong(School of Geomatics and Geographic Information,Lanzhou Jiaotong University,Lanzhou 730070,Gansu,China;National and Local Joint Engineering Research Center of Geographical Monitoring Technology Application,Lanzhou 730070,Gansu,China;Gansu Provincial Engineering Laboratory of Geographical Monitoring,Lanzhou 730070,Gansu,China)
机构地区:[1]兰州交通大学测绘与地理信息学院,甘肃兰州730070 [2]地理国情监测技术应用国家地方联合工程研究中心,甘肃兰州730070 [3]甘肃省地理国情监测工程实验室,甘肃兰州730070
出 处:《应用激光》2024年第11期174-182,共9页Applied Laser
基 金:国家自然科学基金(41964008)。
摘 要:针对三维激光扫描技术获取轨道点云存在多尺度混合噪声严重影响后续的轨道特征提取和三维模型重建的问题,提出一种基于改进双边滤波的去噪算法。首先通过密度阈值和连通性分析去除孤立噪声;其次使用基于点云法向量二范数的分类方式将轨道点云划分为特征区域和非特征区域;最后对特征区域和非特征区域点云分别采用改进双边滤波和最小二乘平面拟合的方法进行平滑去噪。实验结果表明,所提算法对两组不同类型的轨道点云数据去噪后,P2point(点到点的距离)分别为1.6473 mm和0.9537 mm,P2plane(点到平面的距离)分别为1.2467 mm和0.9037 mm,均取得了较好的效果,且较好地保留了轨道特征信息,为快速、准确提取轨道信息提供理论支撑。This paper addresses the challenge of multi-scale mixed noise in track point clouds acquired through 3D laser scanning,which significantly impacts subsequent feature extraction and 3D model reconstruction.An improved bilateral filtering-based denoising algorithm is proposed to mitigate this issue.Firstly,isolated noise is removed by density threshold and connectivity analysis.Then,the track point cloud is divided into feature and non-feature areas using a classification method based on the Euclidean norm of the point cloud normal vectors,and improved bilateral filtering and least squares plane fitting are used to smooth and denoise the point clouds in the respective areas.The experimental results show that for two sets of different track point cloud data,the algorithm achieves better results with P2point values of 1.6473 mm and 0.9537 mm,and P2plane values of 1.2467 mm and 0.9037 mm,respectively.Furthermore,the algorithm effectively retains track feature information,offering a theoretical foundation for the rapid and precise extraction of track information.
分 类 号:P237[天文地球—摄影测量与遥感]
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