Data Snooping for Pose Estimation Based on Generalized Errors-in-Variables Model

Pose estimation plays a vital role in numerous application fields, such as photogrammetry, machine vision, and robotics. Although various intelligent algorithms can be used to solve the prediction of the estimated pose, it is difficult to acquire the high-accurate pose when the observed coordinates are contaminated by gross errors, especially when the number of 3-D-2-D correspondences is small. To address this problem, a weighted least squares solution with data snooping (DSWLS) for pose estimation based on the generalized errors-in-variables (EIV) model is proposed. We first utilize the collinearity equation to present the pose estimation as a generalized EIV model. Then, the Euler-Lagrange method is used to the weighted least squares (WLS) solution of the generalized EIV model. Finally, data snooping is introduced into the generalized EIV model to eliminate the impact of gross errors on the estimated pose parameters. Two types of test statistics for data snooping are constructed based on the least squares theory with known and unknown variance components. The simulated and empirical experiment results demonstrate that the proposed method can reduce the influence of gross errors effectively, ensuring reliable pose estimation even with limited data.