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Hierarchical registration of laser point clouds between airborne and vehicle-borne data considering building eave attributes

机译：考虑到建筑物eave属性，在机载和车辆传播数据之间激光点云的分层注册

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摘要

Laser point cloud registration is a key step in multisource laser scanning data fusion and application. Aimed at the problems of fewer overlapping regional features and the influence of building eaves on registration accuracy, a hierarchical registration algorithm of laser point clouds that considers building eave attributes is proposed in this paper. After extracting the building feature points of airborne and vehicle-borne light detection and ranging data, the similarity measurement model is constructed to carry out coarse registration based on pseudo-conjugate points. To obtain the feature points of the potential eaves (FPPE), the building contour lines of the vehicle-borne data are extended using the direction prediction algorithm. The FPPE data are regarded as the search set, in which the iterative closest point (ICP) algorithm is employed to match the true conjugate points between the airborne laser scanning data and vehicle-borne laser scanning data. The ICP algorithm is used again to complete the fine registration. To evaluate the registration performance, the developed method was applied to the data processing near Shandong University of Science and Technology, Qingdao, China. The experimental results showed that the FPPE dataset can effectively address the coarse registration accuracy effects on the convergence of the iterative ICP. Before considering eave attributes, the mean registration errors (MREs) of the proposed method in the xoz plane, yoz plane, and xoy plane are 0.318, 0.96, and 0.786 m, respectively. After considering eave attributes, the MREs decrease to 0.129, 0.187, and 0.169 m, respectively. The developed method can effectively improve the registration accuracy of the laser point clouds, which not only solves the problem of matching true conjugate points under the effects of the eaves but also avoids converging to a local minimum due to ICP's poor coarse registration. (C) 2021 Optical Society of America
机译：激光点云配准是多源激光扫描数据融合和应用的关键步骤。针对重叠区域特征较少以及建筑物屋檐对配准精度的影响等问题，提出了一种考虑建筑物屋檐属性的激光点云分层配准算法。在提取机载和车载光探测和测距数据的建筑物特征点后，构建相似性度量模型，基于伪共轭点进行粗配准。为了获得潜在屋檐（FPPE）的特征点，使用方向预测算法对车载数据的建筑物轮廓线进行扩展。将FPPE数据作为搜索集，采用迭代最近点（ICP）算法匹配机载激光扫描数据和车载激光扫描数据之间的真共轭点。再次使用ICP算法完成精细配准。为了评估注册性能，所开发的方法被应用于山东科技大学中国青岛附近的数据处理。实验结果表明，FPPE数据集可以有效地解决粗配准精度对迭代ICP收敛性的影响。在考虑屋檐属性之前，该方法在xoz平面、yoz平面和xoy平面上的平均配准误差（MRE）分别为0.318、0.96和0.786 m。考虑屋檐属性后，MRE分别降至0.129、0.187和0.169米。该方法有效地提高了激光点云的配准精度，不仅解决了在屋檐效应下匹配真共轭点的问题，而且避免了因ICP粗配准不好而收敛到局部极小值的问题。（2021）美国光学学会

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  • 来源
    《Applied optics》 |2021年第15期|共12页
  • 作者

    Guo Yadong; Wang Xiankun; Su Dianpeng; Yang Fanlin; Li Guoyu; Qi Chao;

  • 作者单位

    Shandong Univ Sci &

    Technol Coll Geodesy &

    Geomat Qingdao 266590 Shandong Peoples R China;

    Shandong Univ Sci &

    Technol Coll Geodesy &

    Geomat Qingdao 266590 Shandong Peoples R China;

    Shandong Univ Sci &

    Technol Coll Geodesy &

    Geomat Qingdao 266590 Shandong Peoples R China;

    Shandong Univ Sci &

    Technol Coll Geodesy &

    Geomat Qingdao 266590 Shandong Peoples R China;

    Qingdao Xiushan Mobile Measurement Co Ltd Qingdao 266590 Shandong Peoples R China;

    Shandong Univ Sci &

    Technol Coll Geodesy &

    Geomat Qingdao 266590 Shandong Peoples R China;

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