A comparative study of the robustness of two pose estimation techniques

Abstract

The paper presents an analysis of the stability of pose estimation. Stability is defined as sensitivity of the pose parameters towards noise in image features used for estimating pose. The specific emphasis of the analysis is on determining {how the stability varies with viewpoint} relative to an object and to understand the relationships between object geometry, viewpoint, and pose stability. Two pose estimation techniques are investigated. One uses a numerical scheme for finding pose parameters; the other is based on closed form solutions. Both are “pose from trihedral vertices” techniques, which provide the rotation part of object pose based on orientations of three edge segments. The analysis is based on generalized sensitivity analysis propagating the uncertainty in edge segment orientations to the resulting effect on the pose parameters. It is shown that there is a precomputable, generic relationship between viewpoint and pose stability, and that there is a drastic difference in stability over the range of viewpoints. This viewpoint variation is shared by the two investigated techniques. Additionally, the paper offers an explicit way to determine the most robust viewpoints directly for any given vertex model. Experiments on real images show that the results of the work can be used to compute the variance in pose parameters for any given pose. For the predicted {instable} viewpoints the variance in pose parameters is on the order of 20 (degrees squared), whereas the variance for robust viewpoints is on the order of 0.05 (degrees squared), i.e., two orders of magnitude difference.