【摘要】 为解决三维点云数据在白噪声、数据不对应的情况下的配准问题,提出基于高斯似然估计因子分析的点云配准算法。采用因子分析法对点云数据进行表示,利用极大似然估计的方法求得因子载荷矩阵,从而完成对带噪声点云的配准。仿真实验表明,与CDP算法和Go-ICP算法相比,该算法不会陷入局部最小值,在快速精确配准和稳定性方面具有良好的鲁棒性。更多还原

【Abstract】 In order to solve the registration problem of 3D point cloud data in the case of white noise and data non-correspondence, this paper proposes a point cloud registration algorithm based on Gaussian likelihood estimation factor analysis. The point cloud data was represented by the factor analysis method, and the factor load matrix was obtained by the method of maximum likelihood estimation, thereby completing the registration of the noisy point cloud. In the simulation experiment, compared with the CDP algorithm and the Go-ICP algorithm, the proposed algorithm does not fall into the local minimum, and has good robustness in fast and accurate registration and stability.更多还原