为了克服迭代最近点(ICP)算法鲁棒性差、配准精度低的问题,提出了一种改进的基于快速点特征直方图(FPFH)的ICP点云配准算法。首先,基于改进内部形态描述子和法向矢量角的变化来提取点云特征;其次,使用指数函数优化欧氏距离,并将优化的欧氏距离作为FPFH算法的权重系数,用于特征点描述,从而保证利用初始对齐估计得到更准确的点云位置;然后使用双重约束和单位四元数算法完成初始配准;最后,给ICP算法构建双向k维树,并使用点对的欧氏距离与最大欧氏距离的比值来计算每个点对的权重,将权重作为ICP迭代误差函数的加权系数,以减少迭代时间并减少不良对应关系在配准中的影响。实验结果表明,本文算法的配准精度较ICP算法提高2~6个量级,并且具有更强的鲁棒性。

In order to overcome the problems of poor robustness and low registration accuracy of the iterative closest point (ICP) algorithm, this paper proposes an improved ICP point cloud registration algorithm based on fast point feature histograms (FPFHs). Firstly, the point cloud feature is extracted based on the improved internal shape descriptor and the change of the normal vector angle. Secondly, an exponential function is used to improve Euclidean distance, which is used as the weight coefficient of the FPFH algorithm for describing the feature points, therefore ensuring that the initial alignment estimation obtains more accurate positions of point clouds. Then the double constraint and unit quaternion algorithm are used to complete the initial registration. Finally, in order to reduce the iteration time and reduce the influence of bad correspondence in the registration, the bidirectional k-d tree is constructed for the ICP algorithm, and the ratio of the Euclidean distance of a point pair to the maximum Euclidean distance is used to calculate the weight of each point pair, which is used as a weight coefficient of the ICP iteration error function. Experimental results show that the registration accuracy of the proposed algorithm is 2--6 orders of magnitude higher than that of the ICP algorithm, and the proposed method has stronger robustness.