自适应局部邻域特征点提取和匹配的点云配准

点云配准是三维重建的关键技术之一。针对点云匹配中迭代最近点算法(ICP)速率低、对初始位置要求高的问题，提出了一种基于自适应局部邻域特征点提取和匹配的点云配准方法。首先根据局部表面变化因子与平均变化因子的大小关系，自适应地提取特征点；其次利用快速点特征直方图（FPFH）综合描述每个特征点的局部信息，结合随机抽样一致性（RANSAC）算法实现粗配准；最后根据得到的初始变换矩阵和基于特征点的ICP算法实现精配准。对斯坦福数据集、含噪声的点云以及场景点云进行配准实验，实验结果表明:所提出的特征点提取算法能高效地提取点云的特征；相比于其他特征点检测方法，所提方法在粗配准中的配准精度和配准速度更高，且抗噪性能更好；与ICP算法相比，基于文中特征点的ICP算法在斯坦福数据集和场景点云中的配准速度提升了约10倍，在含噪声的点云中，能根据所提取的特征点高效地进行配准。该研究为提高三维重建和目标识别的匹配效率提供了一种高效的方法。

Local neighborhood feature point extraction and matching for point cloud alignment

Point cloud registration is one of the key technologies for 3D reconstruction. To address the problems of the iterative closest point algorithm (ICP) in point cloud matching, which requires high initial position and low speed, a point cloud registration method based on adaptive local neighborhood feature point extraction and matching was proposed. Firstly, according to the relationship between the local surface change factor and the average change factor, feature points were adaptively extracted. Then, the fast point feature histogram (FPFH) was used to comprehensively describe the local information of each feature point, the coarse alignment was achieved combining with the random sampling consistency (RANSAC) algorithm. Finally, according to the obtained initial transformation and feature point based ICP algorithm, the fine alignment was achieved. The alignment experiments were conducted on the Stanford dataset, noisy point cloud and scene point cloud. The experimental results demonstrate that the proposed feature point extraction algorithm can effectively extract the features of the point cloud, and by comparing with other feature point detection methods, the proposed method has higher alignment accuracy and alignment speed in coarse alignment with better noise immunity; compared with the ICP algorithm, the registration speed of the feature point based-ICP algorithm in the Stanford data set and scene point cloud is increased by about 10 times. In the noisy point cloud, the registration can be performed efficiently according to the extracted feature points. This research has certain guiding significance for improving the efficiency of target matching in 3D reconstruction and target recognition.