| 引用本文: | 陆军,彭仲涛.基于快速点特征直方图的特征点云迭代插值配准算法.[J].国防科技大学学报,2014,36(6):12-17.[点击复制] |
| LU Jun,PENG Zhongtao.Iterative interpolation point cloud registration algorithm based on fast point feature histograms[J].Journal of National University of Defense Technology,2014,36(6):12-17[点击复制] |
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| 基于快速点特征直方图的特征点云迭代插值配准算法 |
| 陆军, 彭仲涛 |
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(哈尔滨工程大学 自动化学院，黑龙江 哈尔滨 150001)
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| 摘要: |
| 为提高三维激光扫描点云数据的配准精度，提出了一种基于快速点特征直方图特征的迭代插值配准方法。配准过程中，点云数据获取时受扫描仪分辨率影响，点云局部或整体密度偏小，两次测量点云数据的相同位置不存在完全相同的点，以致对应点之间存在误差。为减小误差对配准精度影响，引入迭代插值方法，增加点云整体密度。通过计算关键点的快速点特征直方图的特征寻找对应关系，使用随机采样一致算法去除错误对应关系，对对应点协方差矩阵进行奇异值分解求得粗配准旋转平移矩阵，再使用迭代最近点算法进行点云的精确配准。实验结果表明,改进的配准方法简单、稳定可靠、计算速度有所增加，有效地提高了配准精度。 |
| 关键词: 点云配准 迭代插值 关键点 快速点特征直方图 迭代最近点 |
| DOI：10.11887/j.cn.201406003 |
| 投稿日期：2014-04-11 |
| 基金项目:黑龙江省自然科学基金资助项目(F201123)；中央高校基本科研业务费专项基金资助项目（HEUCFX41304） |
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| Iterative interpolation point cloud registration algorithm based on fast point feature histograms |
| LU Jun, PENG Zhongtao |
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(College of Automation, Harbin Engineering University, Harbin 150001, China)
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| Abstract: |
| To improve the registration accuracy of point cloud data generated by 3D laser scanning, a new method of iterative interpolation registration based on fast point feature histograms (FPFH) was proposed. Due to the effect of the scanner's resolution in the process of registration, partial or overall density of obtained point cloud data was smaller so that there were no same points even the measuring locations of point cloud data were fixed. Therefore, errors existed between corresponding points. In order to reduce the influence of these errors on the registration accuracy, iterative interpolation method was introduced to increase the overall density of point cloud. The FPFH features of key points were used to find the corresponding relationship; random sample consensus algorithm was used to remove the false correspondence between two point clouds; then the coarse registration rotation and translation matrix was gotten by using singular value decomposition algorithm on the corresponding covariance matrix; at last, the iterative closest point algorithm was employed for the precise registration of point clouds. The experimental results show that the improved registration algorithm is simple, stable and reliable and its computation velocity is faster. This method effectively improves the accuracy of registration results. |
| Keywords: point cloud registration iterative interpolation key points fast point feature histograms iterative closest point |
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