基于主成分分析的高效点云配准算法

An Efficient Point Cloud Registration Algorithm Based on Principal Component Analysis

经典的迭代最近点算法对初始位置敏感并可能陷入局部最优解,而如果先进行粗配准调整位姿,又会出现计算时间长的问题。因此,提出一种基于主成分分析的高效点云配准算法。首先,利用主成分分析法得到两片点云的主轴方向;然后,通过两个主轴关系进行坐标系变换;最后,利用轴上轮廓点的距离进行校正,解决主轴反向问题,相较于原本的误差校正极大减小了计算量。实验结果表明:对包含2万多点的点云,基于轮廓距离改进的主成分分析配准算法平均可减少80%的运行时间,计算效率显著提高;同时还可有效处理点云初始位置较差的情况,实现两片点云任意位姿下的快速配准。该算法可实际应用于列车部件的三维点云配准,提高配准效率。

The classic iterative closest point algorithm is sensitive to the initial position and may fall into a local optimal solution.However,if coarse registration is carried out first to adjust the position and pose,it will take a long time to calculate.Thus,an efficient point cloud registration algorithm based on principal component analysis(PCA)is proposed.First,PCA was used to identify the principal axis directions between the two point clouds.Subsequently,the coordinate system was transformed based on the relationship between two principal axes.Finally,the distance between the contour points on the axes was used for correction to avoid spindle reverse.Compared with the typical error correction method,this approach greatly reduces calculation time.The experimental results show that the improved PCA registration algorithm reduces the running time by 80%on average,and the computational efficiency is significantly improved for point clouds containing more than 20000 points.Further,the algorithm addresses poor initial position and realizes the rapid registration of the two point clouds under any pose.Moreover,the algorithm can be applied to the 3D point cloud registration of train components to improve registration efficiency.