【摘要】 点云配准技术在三维重建、自动驾驶领域中起着重要作用。针对正态分布变换(NDT)算法点云配准数据量较大时，配准效率低和配准精度受到初始位姿影响较大的问题，提出一种内部形状描述子(ISS)特征点融合NDT的点云配准方法。首先利用ISS算法对点云进行特征点提取，然后计算特征点处的快速点特征直方图(FPFH)来描述特征点处的局部几何信息，再利用随机采样一致初始配准算法(SAC-IA)进行粗配准，在粗配准后目标点云与源点云获得一个初始变换位姿，最后将该初始变换提供给NDT算法进行精配准。通过对数据集进行不同算法下的对比实验，证明该算法可以有效地提高点云的配准精度和配准效率。更多还原

【Abstract】 Point cloud registration technology plays an important role in the field of three-dimensional reconstruction and automatic driving. Aiming at the problem that the registration efficiency is low and the registration accuracy is greatly affected by the initial pose when the point cloud registration data of the normal distribution transform(NDT)algorithm are large, an internal shape signature(ISS)feature point fusion NDT point cloud registration method is proposed. Firstly, the ISS algorithm is used to extract the feature points of the point cloud, and the fast point feature histogram(FPFH)at the feature point is calculated to describe the local geometric information at the feature point. Then, the random sampling consistent initial registration algorithm(SAC-IA)is used for coarse registration. After coarse registration, the target point cloud and the source point cloud obtain an initial transformation pose. Finally, the initial transformation is provided to the NDT algorithm for fine registration. Through the comparative experiments on the data set under different algorithms, it is proved that the algorithm can effectively improve the registration accuracy and efficiency of point cloud.更多还原