Uncertainty-Aware Camera Pose Estimation from Points and Lines
Computer Vision and Pattern Recognition (CVPR), 2021
Alexander Vakhitov, Luis Ferraz Colomina, Antonio Agudo, Francesc Moreno-Noguer
Perspective-n-Point-and-Line (PnPL) algorithms aim at fast, accurate, and robust camera localization with respect to a 3D model from 2D-3D feature correspondences, being a major part of modern robotic and AR/VR systems. Current point-based pose estimation methods use only 2D feature detection uncertainties, and the line-based methods do not take uncertainties into account. In our setup, both 3D coordinates and 2D projections of the features are considered uncertain. We propose PnP(L) solvers based on EPnP and DLS for the uncertainty-aware pose estimation. We also modify to the motion-only bundle adjustment to take 3D uncertainties into account. We perform exhaustive synthetic and real experiments on two different visual odometry datasets. The new PnP(L) methods outperform the state-of-the-art on real data in isolation, showing an increase in mean translation accuracy by 18% on a representative subset of KITTI, while the new uncertain refinement improves pose accuracy for most of the solvers, e.g. decreasing mean translation error for the EPnP by 16% compared to the standard pose refinement on the same dataset.
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Uncertainty-Aware Camera Pose Estimation From Points and Lines pdf bib
Vakhitov, Alexander and Ferraz, Luis and Agudo, Antonio and Moreno-Noguer, Francesc
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2021, pp. 4659–4668