复杂曲面测量数据的位姿配准方法

Research on Precise Pose Alignment Between Point Cloud and Complex Surface

针对三维测量数据和自由曲面模型之间的位姿配准问题,研究了先粗后精的两步配准方法。在初始配准的基础上,融合最小二乘法和最小条件原则构造目标函数,应用微分进化算法对目标函数寻优,找出三维测量数据与理论曲面的最佳匹配矩阵以实现最优配准。实验结果表明,该方法与遗传算法相比具有运算速度快和精度高等特点,能较好的解决复杂曲面类零件测量数据的位姿配准问题,并且可用于逆向工程中曲面误差的分析及修正。

Aim at the question of pose alignment between 3D measured data and the free-form surface model,a two-step pose alignment method including first rough matching and then accurate matching has been proposed.On the basis of initial pose alignment,the objective function was constructed by least-square method and minimum condition principle,and then the Differential Evolution（DE） algorithm is applied to calculate the best matching matrix and find out the perfect pose between measured data and the free-form surface.The experimental results show that the pose alignment algorithm proposed in this paper possesses higher accuracy and faster convergence rate compared to the Genetic algorithm method.Not only it can be used in solving the problem of surface alignment,but also in analyzing and amending of the curved surface error in reverse engineering.