【摘要】 针对没有任何几何和拓扑信息的散乱点云,提出一种全自动点云数据配准算法。针对待配准的2组点云数据,首先通过局部最小二乘曲面拟合,估计每个点的法向和曲率,其次计算点云的扩展高斯图(EGI)和复扩展高斯图(CEGI),然后根据EGI和CEGI利用球面调和函数计算旋转欧拉角,构造相关函数,通过Fourier变换估计平移向量,完成粗配准,把粗配准结果作为新的初始位置,采用最近点迭代算法(ICP)进行精确配准,从而实现2组散乱点云的精确配准。实例分析表明该算法配准速度较快,效果良好。更多还原

【Abstract】 A fully automatic point cloud data registration algorithm was proposed to disorderly point cloud with no additional information other than coordinates of measured points. In the algorithm,the normal vector and curvature were first estimated according to its neighbor points and Least-Squares approximation. Secondly,the EGI and CEGI were calculated. Then,the calculation of Euler angles of rotation through spherical harmonic functions and the translation according to Fourier transform were completed. With the initialization of the former results,the iterative closest point algorithm leads to perfect registration. Experimental results indicated that this kind of registration algorithm has a better effect.更多还原