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自研激光雷达三维点云配准技术

呼延嘉玥 徐立军 李小路

呼延嘉玥, 徐立军, 李小路等 . 自研激光雷达三维点云配准技术[J]. 北京航空航天大学学报, 2019, 45(10): 2099-2107. doi: 10.13700/j.bh.1001-5965.2019.0014
引用本文: 呼延嘉玥, 徐立军, 李小路等 . 自研激光雷达三维点云配准技术[J]. 北京航空航天大学学报, 2019, 45(10): 2099-2107. doi: 10.13700/j.bh.1001-5965.2019.0014
HUYAN Jiayue, XU Lijun, LI Xiaoluet al. Three-dimensional point cloud registration technique for self-designed LiDAR[J]. Journal of Beijing University of Aeronautics and Astronautics, 2019, 45(10): 2099-2107. doi: 10.13700/j.bh.1001-5965.2019.0014(in Chinese)
Citation: HUYAN Jiayue, XU Lijun, LI Xiaoluet al. Three-dimensional point cloud registration technique for self-designed LiDAR[J]. Journal of Beijing University of Aeronautics and Astronautics, 2019, 45(10): 2099-2107. doi: 10.13700/j.bh.1001-5965.2019.0014(in Chinese)

自研激光雷达三维点云配准技术

doi: 10.13700/j.bh.1001-5965.2019.0014
基金项目: 

国家重点研发计划 2018YFB0504500

国家自然科学基金 61671038

国家自然科学基金 61721091

详细信息
    作者简介:

    呼延嘉玥  女, 硕士研究生。主要研究方向:激光雷达及光信号处理

    李小路  女, 博士, 副教授, 硕士生导师。主要研究方向:激光雷达及光信号处理

    通讯作者:

    李小路, E-mail:xiaoluli@buaa.edu.cn

  • 中图分类号: TN958.98;V11

Three-dimensional point cloud registration technique for self-designed LiDAR

Funds: 

National Key R & D Program of China 2018YFB0504500

National Natural Science Foundation of China 61671038

National Natural Science Foundation of China 61721091

More Information
  • 摘要:

    为了实现激光雷达点云与图像重建点云的三维空间配准,基于自研三维扫描激光雷达系统,提出了新型的快速多尺度因子(FMSR)点云配准算法,研究了空间点云配准技术。该算法主要包括初始配准和精确配准2个步骤:初始配准使用基于尺度自适应关键点质量(ASKQ)的点云特征提取算法,提取关键点的特征匹配对,求解点云配准初始参数;精确配准利用K-邻近(KNN)算法全局搜索,提升计算效率,多次迭代得到2组点云之间的最优旋转矩阵、最优平移向量和最优尺度因子。仿真和实验结果表明,所提出的算法对空间目标(尺寸为20.30 m×7.85 m×26.56 m)实现空间点云配准,配准精度达到0.194 m,运行时间为16.207 s;与多尺度迭代最近点(S-ICP)算法相比,配准精度提高了0.131 m,运行时间提高了30%。所提出的空间点云配准技术可为场景重建和纹理匹配提供算法基础。

     

  • 图 1  三维点云数据获取系统

    Figure 1.  Three-dimensional point cloud data acquisition system

    图 2  点云配准流程图

    Figure 2.  Flowchart of point cloud registration

    图 3  多尺度小鸡模型的关键点提取结果

    Figure 3.  Key point extraction results of multi-scale chicken model

    图 4  多尺度小鸡模型的关键点匹配结果

    Figure 4.  Key point matching results of multi-scale chicken model

    图 5  多尺度小鸡模型精确配准结果

    Figure 5.  Fine registration results of multi-scale chicken model

    图 6  北京航空航天大学晨兴音乐厅建筑的实验现场

    Figure 6.  Building experimental site of Chenxing Concert Hall in Beihang University

    图 7  真实场景关键点提取结果

    Figure 7.  Key point extraction results of real scene

    图 8  真实场景的关键点匹配结果

    Figure 8.  Key point matching results of real scene

    图 9  真实场景的精确配准结果

    Figure 9.  Fine registration results of real scene

    表  1  FMSR算法精确配准结果

    Table  1.   Fine registration results of FMSR algorithm

    理论值μ 最优尺度因子S 最优旋转矩阵R 最优平移向量T 均方根误差Qrms/m 时间/s
    0.5 0.487 (-0.483, 0.010, -0.049) (2.659, -145.708, -25.783) 0.002 4.043
    1 0.977 (-0.483, 0.010, -0.049) (5.338, -291.396, -51.402) 0.002 2.601
    2 1.954 (-0.483, 0.010, -0.049) (10.679, -582.779, -102.807) 0.002 2.509
    10 9.768 (-0.483, 0.010, -0.049) (53.439, -2 913.906, -513.807) 0.002 2.489
    下载: 导出CSV

    表  2  S-ICP算法与FMSR算法对比

    Table  2.   Comparison of S-ICP and FMSR algorithms

    参数 S-ICP算法 FMSR算法
    最优尺度因子S 3.000 3.300
    最优旋转矩阵R (1.683, -0.035, 0.935) (1.561, -0.044, -2.214)
    最优平移向量T (-39.233, -29.415, 2.564) (17.394, 12.485, -0.957)
    均方根误差Qrms/m 0.325 0.194
    时间/s 23.212 16.207
    下载: 导出CSV
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出版历程
  • 收稿日期:  2019-01-16
  • 录用日期:  2019-03-29
  • 刊出日期:  2019-10-20

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