结合点云统计学特性和形状特征,提出了带方差补偿的多向仿射变换点云配准算法,将求解放缩因子问题转化为求解带方差的超定非线性方程组,并通过二次曲面拟合对噪声方差进行最小二乘无偏估计。引入点云全局向量特征相似度,以相似度最大化求真解。将多向仿射变换点云配准转化为刚性配准,并利用主方向法配准点云。仿真结果表明,针对点云随机丢失和带噪声的点云配准情况,所提算法比现有配准算法的配准精度更高,并且配准耗时更短。

An algorithm for point cloud registration in multidirectional affine transformation with variance compensation is proposed based on the statistical characteristics and shape features of point clouds, in which the problem for solving the unknown scaling factors is transformed into the problem for solving matrix eigenvalues by the overdetermined nonlinear equations, and the least square method is used for the unbiased estimation of noise variance by the quadric surface fitting. The similarity of the global vector features of point clouds is introduced, and the true value of the scaling factor is calculated by maximizing the similarity. The point cloud registration in multi-directional affine transformation is transformed into the rigid registration, and then the point cloud is registered with the main direction method. The simulation results show that the proposed algorithm has higher accuracy and smaller time consumption compared with the other existing registration algorithms when the point cloud is randomly lost or registered with noise.