基于多模态数据协同的弱纹理表面高精度点云配准算法

High-Accuracy Point Cloud Matching Algorithm for Weak-Texture Surface Based on Multi-Modal Data Cooperation

为解决大型构件表面弱纹理特征的捕捉和多次测量的精度配准问题,采用集成结构光与光度立体视觉的复合测量系统,利用结构光测量获取工件表面整体形状的点云数据,利用光度立体视觉获取表面精细弱纹理的法向量信息。在此基础上,提出了一种融合邻域点云坐标与法向量信息的新型局部特征描述子,可对弱纹理工件表面特征进行有效且鲁棒地描述。大量仿真和真实实验验证了所提方法的有效性,其性能大幅超越了基于传统特征描述子的迭代最近点算法。所提方法可有效捕捉并描述弱纹理表面的丰富细节特征,构建鲁棒显著的特征描述子,进而大幅提升了测量结果的匹配精度,减小了大型复杂构件的整体重建误差。

In order to solve the problems of capturing weak-texture features on the surface of large components and precision registration of multiple measurements,a compound measurement system integrating structured light and photometric stereo vision is adopted.The point cloud data of the overall shape of the workpiece surface are obtained by structured light measurement,and the normal vector information of the fine and weak texture of the surface is obtained by photometric stereo vision.On this basis,a new type of local feature descriptor which combines neighborhood point cloud coordinates and normal vector information is proposed,which can describe the surface features of weak-texture workpieces effectively and robustly.Extensive simulations and practical experiments verify the effectiveness of the proposed method,and its performance greatly surpasses the iterative nearest point algorithm based on traditional feature descriptors.The proposed method can effectively capture and describe the rich detail features of the weak-texture surfaces,construct robust and significant feature descriptors,and then greatly improve the matching accuracy of the measurement results and reduce the overall reconstruction error of large and complex components.