  The Perspective-n-Point (PnP) problem has been widely studied in the
literature and applied in various vision-based pose estimation scenarios.
However, existing methods ignore the anisotropy uncertainty of observations, as
demonstrated in several real-world datasets in this paper. This oversight may
lead to suboptimal and inaccurate estimation, particularly in the presence of
noisy observations. To this end, we propose a generalized maximum likelihood
PnP solver, named GMLPnP, that minimizes the determinant criterion by iterating
the GLS procedure to estimate the pose and uncertainty simultaneously. Further,
the proposed method is decoupled from the camera model. Results of synthetic
and real experiments show that our method achieves better accuracy in common
pose estimation scenarios, GMLPnP improves rotation/translation accuracy by
4.7%/2.0% on TUM-RGBD and 18.6%/18.4% on KITTI-360 dataset compared to the best
baseline. It is more accurate under very noisy observations in a vision-based
UAV localization task, outperforming the best baseline by 34.4% in translation
estimation accuracy.
