基于3DSIFT和BSHOT特征的点云配准方法
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山西省重点研究计划项目(No.201903D121130)资助。


Point cloud registration method based on 3DSIFT and BSHOT feature
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    摘要:

    针对点云配准过程中易产生错误匹配点、配准时间长、配准精度低等问题。提出了基于三维尺度不变特征变换(3DSIFT)关键点检测方法,结合二进制方向直方图描述子(BSHOT)构建点云匹配对的配准方法。该方法首先利用差分高斯模型在三维尺度空间上检测SIFT关键点,其次在关键点的邻域构建局部坐标系来计算SHOT描述子,并将SHOT描述子转化为二进制描述符。然后利用随机采样一致性算法去除误匹配的点云,初步估计点云的变换矩阵。最后在精配准上利用ICP算法估计最优的变换矩阵。在数据集的实验中验证了本文算法的快速性,同时在两个点云重叠率较低时,配准精度较高。

    Abstract:

    In the process of point cloud registration,it is easy to produce wrong matching points,long registration time,and low registration accuracy.A registration method is proposed,which bases on 3DSIFT key point detection method combined with BSHOT descriptor to construct a point cloud matching pair.This method firstly uses the differential Gaussian model to detect SIFT key points in the three-dimensional scale space,and secondly constructs a local coordinate system in the neighborhood of the key points to calculate the SHOT descriptor,and converts the SHOT descriptor into a binary descriptor.Then the random sampling consensus algorithm is used to remove the mismatched point cloud,and the transformation matrix of the point cloud is preliminarily estimated.Finally,the ICP algorithm is used to estimate the optimal transformation matrix in the fine registration.Experiments on the data set verify the rapidity of the algorithm in this paper.At the same time,when the overlap rate of the two point clouds is low,the registration accuracy is high.

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刘雷,柏艳红,王银,孙志毅.基于3DSIFT和BSHOT特征的点云配准方法[J].激光与红外,2021,51(7):848~852
LIU Lei, BAI Yan-hong, WANG Yin, SUN Zhi-yi. Point cloud registration method based on 3DSIFT and BSHOT feature[J]. LASER & INFRARED,2021,51(7):848~852

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  • 在线发布日期: 2021-07-22