Three-dimensional (3D) point cloud registration generally involves in unsatisfied situations like Gaussian white noise, data missing and disorder in affine. This paper proposes a robust and real-time point cloud registration, which combines the Student’s-t mixture model (SMM) with factor analysis. The proposed method extending the point cloud mathematical model to the orthogonal factor model and employs the SMM to fit the point cloud data, because the degree of freedom of Student’s t-distribution makes it more flexible in fitting the probability distribution of data. Since the Expectation Maximization (EM) algorithm has a stable estimation ability for the mixture model, the EM algorithm is used to estimate the factor load matrix. The filed data and experimental results show that the proposed algorithm can achieve accurate registration and fast convergence even in the case of point cloud disorder, data occlusion, incomplete loss and noise.

三维（3D）点云配准通常涉及不满意的情况，例如高斯白噪声，数据丢失和仿射中的混乱。本文提出了一种健壮且实时的点云注册，该注册将Student's t混合模型（SMM）与因子分析相结合。该方法将点云数学模型扩展到正交因子模型，并采用SMM拟合点云数据，因为学生t的自由度-distribution使它更适合拟合数据的概率分布。由于期望最大化（EM）算法对混合模型具有稳定的估计能力，因此该EM算法用于估计因子负荷矩阵。现场数据和实验结果表明，该算法即使在点云无序，数据闭塞，不完全丢失和噪声的情况下也能实现准确的配准和快速收敛。