基于方向直方图签名描述符的点云配准方法

Point Cloud Registration Method Based on Signature of Histogram of Orientation Descriptor

针对复杂场景下传统点云配准精度与效率低、鲁棒性差,难以配合机器人进行工业作业,提出一种基于方向直方图签名(SHOT)描述符的点云配准方法。对模型点云和场景点云采用体素重心降采样预处理,对降采样后的点云采用内部形状签名(ISS)提取特征点;计算特征点SHOT描述符并构建KD树快速检索特征相似的点对;采用随机采样一致(RANSAC)去除误匹配点对并完成粗配准,获取点云粗配准初始位姿,联合迭代最近点(ICP)完成精配准。实验结果表明:复杂场景下,本文方法能够快速识别定位H型钢和热电偶,配准用时2 s,配准精度在5 mm以内;与传统ICP算法相比,本文方法配准精度更高、鲁棒性更好,能够对一定程度遮挡、残缺物体点云进行识别,满足工业要求。

In view of the low accuracy,efficiency and robustness of traditional point cloud registration in complex scenes,which are difficult to cooperate with robots in industrial operations,a point cloud registration method based on signature of histograms of orientations(SHOT)descriptor was proposed.The model point cloud and field point cloud were pre-processed by downsampling the center of gravity of voxels,and the internal shape signature(ISS)was used to extract feature points from the downsampled point cloud.The SHOT descriptors of feature points were calculated,and the KD tree was constructed to quickly retrieve the pairs of points with similar features.The random sampling consistency(RANSAC)algorithm was used to remove the mis-matched point pairs and complete the coarse alignment,obtain the initial position of the coarse alignment of the point cloud,and complete the fine alignment by combining with iterative closest point(ICP).The experimental results show that this method can quickly identify and locate H-beams and thermocouples in complex scenes,with an alignment time of 2 s and an alignment accuracy within 5 mm.Compared with the traditional ICP algorithm,this method has higher alignment accuracy and better robustness,this method can identify point clouds of objects with a certain degree of occlusion and mutilation,and has better robustness,which can meet industrial needs.