逆向工程中散乱点云变尺度配准算法研究

Research on a Variable Scale Registration Algorithm for Scattered Point Clouds in Reverse Engineering

针对传统散乱点云配准算法收敛区间与配准精度之间的矛盾,提出一种变尺度点云配准算法。构造一种基于重合点计数点云配准测度函数;对测度函数的高斯平滑过程进行研究,并对尺度参数对测度函数性能的影响规律进行分析;根据测度函数在大尺度参数下平滑但存在极值偏移,在小尺度参数下全局极值位置精确但存在局部极值的特点,提出一种尺度参数可变的散乱点云配准算法;借鉴模拟退火算法的思想,通过对比选定Lundy退化策略作为算法的尺度衰减策略;采用曲率约束进行控制点筛选并利用快速高斯变换进行测度函数值的计算以提高算法效率;利用合成数据和实测数据进行对比试验,结果基于变尺度策略的散乱点云配准算法具有更加广泛的收敛区间和更高的配准精度。

Aiming at solving the conflicts between convergence region and accuracy of classical registration algorithms,a new variable scale registration algorithm for scattered point clouds is proposed.A measure function used in point clouds registration is constructed on the basis of coincidence point counting.The Gaussian smoothing procedure of the point counting function is investigated,and the relationship between the smoothed measure function and the scale parameter is discussed.The smoothed measure function has the following characteristics：Smooth but extreme-offset using large scale parameter and accurate-global-extreme but local-extreme-existence using small scale parameter.A variable scale registration algorithm for scattered point clouds is proposed on the basis of these characteristics.Inspired by the simulated annealing algorithm,the Lundy annealing strategy is selected as the scale parameter evolution strategy based on comparative experiment.To improve the efficiency of the algorithm,curvature constraint is used to filter the control points;fast Gauss transform is used to speed up the computation of the measure function.At last,the improved convergence region and accuracy of our algorithm are validated through comparison experiments using synthetic and real point clouds.