基于改进FPFH-ICP的车载激光雷达点云配准方法

为了改善传统车载激光雷达点云配准方法准确度低、计算速度慢的问题，提出了一种基于快速点特征直方图（fast point feature histograms, FPFH）初始匹配与改进迭代最近点（iterative closestpoint,ICP）精确配准相结合的改进FPFH-ICP配准算法。配准前使用体素滤波器和statistical-outlier-removal滤波器进行预处理；采用FPFH提取点云特征，基于采样一致性（sample consensus initial alignment, SAC-IA）进行初始配准，为精确配准提供良好的位姿信息；建立K-D树并在传统ICP配准算法的基础上添加法向量阈值，对车载激光雷达点云数据进行精确配准；在4种不同场景的实验中，改进FPFH-ICP配准比ICP配准的均方根误差和配准用时分别平均减少了7.56%和41.22%，比点特征直方图（point feature histograms, PFH）配准的均方根误差和配准用时分别平均减少了30.28%和18.95%，表明改进的FPFH-ICP能够对车载激光雷达点云数据实现精确且高效的配准。

Point cloud registration of vehicle-mounted lidar based on improved FPFH-ICP algorithm

To solve the problems of low accuracy and slow calculating speed of the traditional point cloud registration methods for vehicle-mounted lidar, an improved FPFH-ICP registration combining fast point feature histograms (FPFH) initial matching with improved iterative closest point (ICP) accurate registration was proposed. Firstly, voxel grid and statistical-outlier-removal filter were used for preprocessing data before registration. Then, based on sample consensus initial alignment (SAC-IA), FPFH was used for initial registration to provide good pose information for accurate registration. Finally, a K-D tree was established, and a normal vector threshold was added to traditional ICP registration for accurate registration. In the experiments of four different scenarios, the root mean square error and registration time of the improved FPFH-ICP registration were reduced by 7.56% and 41.22%, respectively, compared with ICP registration, and by 30.28% and 18.95%, respectively, compared with point feature histograms (PFH) registration, suggesting that the improved FPFH-ICP registration can achieve accurate and efficient registration of point cloud data of vehicle-mounted lidar.